Who We Are

Babylist is the leading registry, e-commerce, and content platform for growing families. More than 9 million people shop with Babylist every year, making it the go-to destination for seamless purchasing, trusted guidance, and expert product recommendations for new parents and the people who love them. What began as a universal registry has grown into a full ecosystem for new parents, including the Babylist Shop, Babylist Health, and a flagship showroom in Los Angeles. Hundreds of brands in baby and beyond partner with Babylist to engage meaningfully with families during one of life’s most important transitions. With over $1 billion in annual GMV, and more than $500 million in 2024 revenue, Babylist is reshaping the $320 billion baby product industry. We’re helping parents feel confident, connected, and cared for at every step. As we build the generational brand in baby, our mission remains simple: to connect growing families with everything they need to thrive.To learn more, visit www.babylist.com.

Our Ways of Working

Babylist is remote-first with team members across the U.S. and Canada who move fast, think smart, and use AI as part of how they work every day — not as an experiment, as an expectation. We come together twice a year to build the relationships behind the work, and we hire people who are genuinely excited about what's possible and prove it through how they show up.

How We Build

Babylist is in the middle of a fundamental shift in how software gets made, and we are not tiptoeing into it. We are rebuilding our engineering culture around a simple belief: AI changes everything. How teams are structured, how decisions get made, how fast ideas become working software. Our engineers own problems end to end, working directly with product, design, and business partners with short feedback loops and real stakeholder access. We ship, learn, and iterate fast. When something is not working, we throw it out and start over — project failure and personal failure are not the same thing here. AI tools are as natural to our workflow as an IDE or version control. We are not exploring this, we are living it. Our engineers use AI to explore tradeoffs, pressure-test designs, and move from problem to solution in hours instead of days. They generate code with AI so they can stay focused on the decisions that actually require human judgment — not the routine ones. More velocity means more time for craft: better test coverage, stronger architecture, and deeper customer understanding. We hold ourselves to a higher quality bar because of AI, not in spite of it. We are building this playbook in real time, and we are looking for people who want to build it with us. If you have already changed how you work because of AI — or you are ready to — and you care more about shipping something great than following a prescribed process, we should talk.

Our Tech Stack 

• React, Native iOS and Android
• Ruby on Rails
• AWS, Terraform
• MySQL
• Sidekiq
• Redis
• Shopify

What the Role Is

As Babylist’s Senior Engineering Manager, Machine Learning & Discovery+Site Monetization, you’ll lead the team driving the next era of our machine learning and personalization efforts across the product. This is a hands-on leadership role for a manager who remains technically fluent while scaling people, systems, and impact. You’ll manage and grow a high-performing ML-focused pod spanning product recommendations, search, PLPs, and site monetization, with a clear mandate: maximize revenue and user value across surface areas through personalization.

You’ll guide the team on technical direction behind our homepage feed, “add next” experience, and other core personalization surfaces, while building the foundations for long-term ML excellence at Babylist.

Who You Are

• 3+ years of experience as a front-line manager delivering production-grade software or ML systems
• 5+ years of experience as an individual contributor in ML or Data Science, plus proven experience leading teams that build end-to-end machine learning systems, ideally in personalization, recommendations, or consumer-facing ML products
• Deep familiarity with the Python ML ecosystem (e.g., pandas, sklearn, xgboost, PyTorch) and comfortable reviewing designs, unblocking technical challenges, and setting high technical standards
• Experience supervising ML Engineers and/or Data Scientists; bonus points for experience managing Software Engineers
• Strong understanding of the full ML lifecycle, including data pipelines, workflow orchestration (e.g., Airflow), deployment, monitoring, and iteration in production
• Product-minded: passionate about technology, but primarily motivated by solving real user problems and driving business impact
• Experience working with large-scale, real-time or near-real-time data systems
• Comfortable owning and evolving the ML tech stack end-to-end, from data and modeling through production systems
• Background in consumer-facing products; experience in e-commerce, marketplaces, or complex user journeys is strongly preferred
• You're genuinely excited about what AI can do - not just as a concept, but as something you want to get your hands on. At Babylist, every team uses AI daily, and we're looking for people who lean in.

How You Will Make An Impact

• Build and lead the ML & personalization function: Hire, mentor, and support a multidisciplinary team (Machine Learning Engineers, Software Engineers, Product Manager, and occasional Designer), shaping the roadmap, standards, and culture for ML at Babylist
• Own high-impact ML initiatives: Deliver personalization systems that improve discovery, engagement, and monetization across key surfaces, directly impacting business outcomes
• Set technical and algorithmic direction: Define modeling approaches, data strategy, and system architecture in partnership with senior technical leaders, balancing experimentation with scalable execution
• Be hands-on when needed: Review ML/DS designs and code, build proof-of-concept models, and conduct your own data science research to de-risk ideas and accelerate progress
• Develop talent: Coach engineers on technical growth, career progression, and performance, combining strong technical standards with empowerment
• Drive cross-functional alignment: Partner closely with Product, Design, Analytics Engineering, and Data to translate ambiguous business problems into clear technical direction
• Understand the full funnel: Develop a deep understanding of Babylist’s user journeys, user feedback loops, and financial models — and how ML systems influence them end-to-end

About Compensation

We use a market-based approach to compensation. The starting salary range for this role is:

USD - $250,000 to $308,000
CAD - $230,000 to $288,000

Your starting salary will be based on your location, experience, and qualifications, with increases over time tied to performance, role growth, and internal pay equity.

Why You Will Love Working At Babylist

Our Culture

• We work with focus and intention, then step away to recharge
• We believe in exceptional management and invest in tools and opportunities to connect with colleagues
• We build products that positively impact millions of people's lives
• AI tools are as natural to how we work as your IDE or version control — we're not exploring this, we're living it.

Growth & Development

• Competitive pay and meaningful opportunities for career advancement
• We believe technology and data can solve hard problems
• We're committed to career progression and performance-based advancement

Compensation & Benefits

• Competitive salary with equity and bonus opportunities
• Company-paid medical, dental, and vision insurance
• Retirement savings plan with company matching and flexible spending accounts
• Generous paid parental leave and PTO
• Remote work stipend to set up your office
• Perks for physical, mental, and emotional health, parenting, childcare, and financial planning

Important Notices

Recorded Interviews Babylist uses an interview recording tool to record and transcribe interviews for evaluation purposes in accordance with applicable privacy laws. By participating in an interview, you consent to this recording and transcription.

Interview Integrity At Babylist, every team uses AI daily and we love it. During interviews though, we want to see you — your thinking, your problem-solving, your creativity. All interviews and assessments should be completed independently without AI tools or third-party assistance unless we tell you otherwise. We'll always be clear when AI is welcome. Misrepresentation during the process may result in removal from consideration.

Protect Yourself from Scams All official communication comes from the Babylist Talent Team via @babylist.com email addresses. We will never ask for payment, bank information, or personal financial details. If you receive outreach via WhatsApp, Telegram, or a non-Babylist email, it's not us. Verify open roles on our careers page.

Connections at Babylist In line with our conflict of interest policy, please let us know if you have a family member or close personal relationship with a current Babylist employee. This helps us keep our process fair for everyone.

Text Message Updates You may opt in to receive SMS updates about your application. Opting out won't affect your status. Message and data rates may apply. Reply STOP to unsubscribe or HELP for assistance. See our Privacy Policy for details.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Lyft is looking for experienced software engineers from a scope of disciplines. We are growing our team with people who want to build, improve and incorporate technologies that make the lives of our community more enriched. As an engineer at Lyft, you'll collaborate with teams like product, data science, analytics, and operations on code that empower us to iterate quickly, while focusing on delighting our riders and drivers.

As a Software Engineer for Lyft Ads - you will work on one of Lyft’s newest lines of business focused on building the world’s largest transportation media network. We build products that allow brands to engage with our unique audience throughout their transportation journeys and beyond. For this role we are seeking software engineers who are passionate about backend and data engineering. You will join our Ad Infra Engineering team and contribute to building the systems and pipelines powering our ad-serving, measurement and audience platform. We work on technologies that let brands engage with our unique audience throughout their transportation journeys. This role is a great opportunity for an early-career engineer to gain experience working with distributed backend systems and large-scale data workflows, collaborating closely with product, analytics, and data science teams.

Why Lyft Ads?

• Be part of a high-impact team building the foundations of Lyft’s advertising infra
• Gain experience working with complex systems at scale, backed by mentorship and growth opportunities.
• Join a supportive, inclusive environment focused on engineering excellence and business impact.

Responsibilities:

• Develop and maintain backend systems that support real-time ad serving, campaign management, and reporting.
• Design and implement scalable data models to support analytics, targeting, and measurement use cases.
• Build and maintain data pipelines that handle ingestion, transformation, and distribution of large volumes of advertising data.
• Work with orchestration frameworks (e.g., Airflow) to schedule and monitor reliable, production-grade data workflows.
• Collaborate with cross-functional partners to integrate internal and third-party systems via APIs and batch data feeds.
• Write clean, maintainable, and testable code following best practices (CI/CD, observability, code reviews).
• Participate in architecture discussions and design reviews with an emphasis on performance and reliability.
• Learn and grow through mentorship, documentation, and regular team collaboration.

Experience:

• BSc/MSc in Computer Engineering, Computer Science, related field or relevant work experience
• 3+ years of industry experience in backend or data engineering roles
• Proficiency in at least one backend programming language (e.g., Python, Go, Java)
• Good understanding of distributed systems, microservices, and database design (SQL and NoSQL).
• Exposure to orchestration tools (e.g., Airflow, Prefect) and data distribution technologies (e.g., Kafka, Pub/Sub).
• Experience working with relational and non-relational databases (e.g., PostgreSQL, BigQuery, DynamoDB).
• Experience with Linux.

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $108,000 - CAD $135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

Role & Team

As a Data Engineering Manager, you will lead across several cross-functional teams, managing up to ~10 data engineers and GIS specialists. You will support teams working on data processing and pipelines to enable ML-powered product capabilities.

Our product teams are cross functional and will typically include product managers, designers, engineers and subject matter experts relating to the team’s domain.

You will be accountable for the technical direction and delivery outcomes of your area. You will challenge your teams to pursue ambitious goals while providing a high level of support — growing engineering talent and fostering a highly collaborative, team-based environment where people can do great work.

Your primary focus will be on building high-performing teams and driving results through others. You will not be expected to spend significant time doing hands-on coding; however, you will be expected to dive deep technically when required and leverage your experience to support strong technical decision-making.

Time zone requirement: Europe (GMT/WET, CET, EET) and Eastern North America (NST, AST, EST)

What You'll Do

• Enable our data engineers to become highly productive members of their cross-functional teams, empowering them to ship high-quality, reliable data platforms and pipelines on a daily basis.
• Grow the teams by attracting great talent from your network.
• Foster an inclusive and caring culture where everyone can do their best work.
• Provide regular 1:1 coaching and feedback to tap into the potential of all your team members and help them thrive and reach their aspirations.
• Be a strategic partner to product managers, machine learning engineers, and other stakeholders within your domains and ensure that we are making the correct data architecture and platform decisions that balance short and long term goals.
• Ensure suitability of the data architecture, pipelines, and data models within your domains to enable longer term product, analytics, and machine learning goals.
• Work closely with the other Product & Engineering leaders on strategy, technology and people, including improving our data engineering practices and introducing capabilities that can accelerate data access, reliability, and unlock new product and data-driven opportunities

Skills & Experience

• Experience working in a high growth scaleup environment
• Passionate about climate
• Product minded and able to demonstrate significant impact you have had on a business through the application of data platforms and data-driven systems
• 2+ years experience leading and managing data engineers across multiple teams
• Experience working with the following (or similar) technologies: Python, SQL, data pipeline orchestration tools (e.g., Airflow, Dagster), data warehouses/lakehouses, Postgres DB, data pipelines
• Ability to unblock your team by contributing at a senior engineering level (e.g., reviewing data models, debugging pipelines, guiding architecture). How hands-on you'll be in this role will depend on team size and maturity
• Comfortable working in a build it, run it environment (GCP, cloud run, grafana, k8s) - or similar, with an understanding of data reliability, observability, and pipeline operations
• Strong leadership skills with the ability to mentor and coach engineers at all levels
• Excellent communication skills and ability to collaborate effectively in a cross-functional team environment
• Passion for learning and staying updated with evolving technologies and industry trends, particularly in data engineering and data infrastructure

Nice To Have

• Experience working with maps/imagery/geospatial domains  

Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.

What We Offer

• Competitive, location-specific compensation and benefits 
• Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
• Home office stipend, coworking and ongoing education budgets 
• A company culture that genuinely embodies each of our core values
• To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 91.6 million monthly active users (MAU), as of September 30, 2025, across more than 180 countries.

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basically family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

Life360 is a Remote-First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely (within the US) regardless of any specified location above. 

We are AI Native 

We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process.
Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy.

About The Team

The Finance Data Team sits at the intersection of Finance & Accounting teams and Life360’s data. We provide the data ingestion / processing / reporting / egress needed by our partner teams in Finance & Accounting to enable their work and ensure SOX compliance with rigor. We push the envelope on how work is done through implementation of AI tools and capabilities to enhance our own pace of development and capabilities that we deliver to our stakeholders.

We are hiring a bar-raising Staff Data Engineer to drive our ingress / egress capabilities and build cross-cutting capabilities to drive developer experience and security. This role requires someone who can step into ambiguity, make sound architectural decisions, eliminate operational fragility, and establish an engineering discipline that others adopt.

You will serve as a technical reference point - shaping standards, influencing cross-team architecture, and driving initiatives to clear, production-ready outcomes. We value engineers who are direct, collaborative, and proactive in surfacing risks early, while helping build a team culture where high standards and psychological safety coexist.

About the Job

The Life360 Finance Data Team works as the integrator for numerous systems - bringing data into the Finance Data Warehouse, transforming it, and pushing it to its relevant destinations (reporting, data asset deliverables, tools, etc).  To support our role we are continuously building and enhancing our system - adding new data, new transformations, and new tooling to improve developer velocity and ‘buy down’ overhead costs associated with maintaining our system.

As a Staff Data Engineer you will drive forward the:

• Data ingestion suite - a mix of capabilities ranging from Fivetran to completely custom connectors that bring data into our warehouse and monitor for data quality.
• Data transformation suite - enhancing our dbt environment (dbt core) and the suite of tools that we leverage to build data models, semantic models, and other interfaces.
• Data egress suite - a mix of capabilities that allow us to push data to its end destinations.
• CI/CD pipelines and other tools / capabilities to enhance the developer experience and velocity.
• Infrastructure and networking behind our warehouse and related connectors.
• Databricks configuration and capabilities.
• Security posture and access controls.

The ideal candidate:

• They have spent years building out data platforms / infrastructure as well as creating ingress / egress data frameworks that are used in pipelines.  They have tackled numerous challenges and found novel solutions to problems for data ingestion, processing, and egress.
• They have learned how to leverage LLMs for development velocity and analytics - not just asking it to write the code but leveraging the tools to support their development under clear guidance and accepts ownership of the work produced as their own.
• They have learned to think about scalability / velocity / experience of future development and not just shipping the current project.
• They are part software engineer, part data integration engineer, and part data platform engineer.
• They adhere to the controls / procedures / separation of duties necessary to maintain our SOX compliance.

We are looking for someone with strong engineering depth who demonstrates ownership, decisiveness, and the ability to elevate both the system and the team around them.

For candidates based in the US, the salary range for this position is $190,000 to $280,500 USD. For candidates based out of Canada, the salary range for this position is $220,000 to $260,000 CAD. We take into consideration an individual's background and experience in determining final salary; therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity.

AI / LLM Usage

The Finance Data Team leverages LLM’s to support code generation, analysis, and other use cases.

• Cursor / Claude Code.
• Other tools (Wispr Flow, Claude & Claude Cowork, Gemini / Codex).

Your experience with AI / LLM usage should include managing code generation with a close eye on quality, standards, and testing - owning the outputs as your own.  Your work with and ability to leverage these tools will drive your velocity and ability to effectively work within our environment.

What You’ll Do

• Architect and evolve scalable data ingestion and egress frameworks and pipelines that are well tested and offer strong data quality monitoring. 
• Architect and evolve our CI/CD processes - enhancing the testing environment and observability (such as building LLM-driven reviews with context awareness through data diffing, lineage analysis / downstream impact analysis, and general context).
• Architect delivery architecture of data assets to external team partners to reduce manual operational overhead associated with month end close.
• Enhance our Claude Code / LLM development support capabilities - creating tools / skills / agents that give our LLMs more context and help us continually improve their abilities to debug, create code, and maintain systems.
• Enhance our security posture in our AWS / Databricks environment.
• Design and implement distributed data processing systems using Spark and Databricks on AWS.
• Establish clear ingestion and integration boundaries that eliminate single points of failure.
• Proactively surface risks, dependencies, and tradeoffs before they impact delivery.
• Produce clear technical artifacts and recommendations for stakeholders and leadership.
• Design logical and physical data models balancing flexibility, performance, governance, and scalability.
• Partner closely with the Analytics Engineers on the Finance Data Team to support high-quality downstream data modeling & reporting.
• Harden pipelines with monitoring, alerting, SLAs, and recovery mechanisms.
• Mentor engineers and elevate distributed systems rigor across the team.

What We’re Looking For

• 8+ years designing and operating high-volume distributed data systems in production.
• Deep expertise with a cloud data platform (Databricks strongly preferred) and AWS from an infrastructure / services architecture, deployment, and ownership perspective.
• Strong proficiency in Python, SQL, and Spark for large-scale processing.
• Strong proficiency with modern CI/CD practices (creating GitHub Actions, writing Terraform code to manage infrastructure in Databricks / Airflow / AWS / and others).
• Hands-on experience with dbt from an infrastructure / deployment perspective and understanding of how platform decisions impact downstream modeling.
• Strong grasp of data modeling, partitioning strategies, storage formats, and analytical workload optimization.
• Experience with Airflow and data flow orchestration.
• Experience with networking challenges in data ingestion (e.g., VPC peering, firewall traversal, API rate limiting, cross-AWS account access, etc.)
• Able to effectively leverage / oversee LLM-supported code development while maintaining a high quality bar.
• Demonstrated experience with AI tools to support / enhance development - Claude Code, Cursor, etc.
• Demonstrated ability to independently scope ambiguous problems and drive them to decisive outcomes.
• Track record of proactively escalating risks and closing long-running efforts with clear recommendations.
• Experience defining ingestion validation standards and implementing data quality controls.
• Proven ability to reduce operational fragility and eliminate single points of failure.
• Strong systems design skills across distributed and event-based architectures.
• Demonstrated technical leadership influencing cross-team architectural decisions.
• Excellent communication skills across engineering, analytics, product, and executive stakeholders.
• BS in Computer Science, Engineering, Mathematics, or equivalent experience.

Our Benefits

• Competitive pay and benefits.
• Medical, dental, vision, life and disability insurance plans (100% paid for US employees). We offer supplemental plans for medical and dental for Canadian employees.
• 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees.
• Employee Assistance Program (EAP) for mental wellness.
• Flexible PTO and 12 company wide days off throughout the year.
• Learning & Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.
• Free Life360 Platinum Membership for your preferred circle.

Life360 Values

Our company’s mission-driven culture is guided by our shared values to create a trusted work environment where you can bring your authentic self to work and make a positive difference 

• Be a Good Person - We have a team of high-integrity people you can trust. 
• Be Direct With Respect - We communicate directly, even when it’s hard.
• Members Before Metrics - We focus on building an exceptional experience for families. 
• High Intensity, High Impact - We do whatever it takes to get the job done. 

Our Commitment to Diversity

We believe that different ideas, perspectives, and backgrounds create a stronger and more creative work environment that delivers better results. Together, we continue to build an inclusive culture that encourages, supports, and celebrates the diverse voices of our employees. It fuels our innovation and connects us closer to our customers and the communities we serve. We strive to create a workplace that reflects the communities we serve and where everyone feels empowered to bring their authentic best selves to work.

We are an equal opportunity employer and value diversity at Life360. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any legally protected status.  

We encourage people of all backgrounds to apply. We believe that a diversity of perspectives and experiences creates a foundation for the best ideas. Come join us in building something meaningful. Even if you don’t meet 100% of the qualifications above, you should still seriously consider applying!

#LI-Remote

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Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

Web Presence and Platform is organized into two pillars, each of which is grouped into pods that focus on the central tenets of Stripe's public mission. The Presence pillar creates industry-leading designs for Stripe’s front door surfaces, educating customers about the power of our platform, sharing Stripe’s ideas and expertise with the public, and driving adoption. The Platform pillar builds the internal machinery that powers these surfaces, and is responsible for making our websites fast, stable, and easy to update.

Together we design and build stripe.com and other sites that amount to what is, for many, their first impression of Stripe. As such, WPP offers exciting opportunities to have a major impact on Stripe’s success. We want to make every pixel count, we want it to be enthralling, and we want to help other Stripes seamlessly benefit from our systematic work.

While the team's mission anchors to web surfaces, this role lives deeper in the stack. You will be the engine behind the experiences rather than the pixel-level presentation — APIs, data pipelines, model orchestration, and service reliability that make LLM-driven user experiences possible at scale.

What you’ll do

The Expansion pod brings creativity and executional rigor to attracting new prospects and driving conversion, focusing on user journeys, interactive and highly polished tools for users, and building targeted experiences for our global users.

As a backend engineer you will architect and build the server-side systems and LLM orchestration layers that power intelligent, dynamic user experiences across acquisition journeys. You will design the APIs, data pipelines, and inference infrastructure behind delightful and original interactive tools and resources that attract users. You will collaborate deeply across design, engineering, and cross-functional teams and work across teams and tech stacks to create new site capabilities — with a specific focus on integrating large language models into production-grade services that are reliable, performant, and safe.

This is not a research or ML training role. You will be responsible for wiring LLMs into real user-facing products: managing prompt pipelines, handling model routing and fallback logic, implementing guardrails and output validation.

Responsibilities

• Develop and implement the tooling and platforms to support interactive content tools (e.g., calculators, generators, templates) that provide utility to current and prospective businesses.
• Utilize a user-first mindset to implement feedback mechanisms for continuous improvement of content and product offerings.
• Design and build integrations to third-party software and create seamless workflows across these tools to unleash their full potential to the rest of Stripe.
• Ensure reliability, security, and responsible AI practices across all LLM-driven features, including implementing content filtering, PII handling, rate limiting, graceful degradation, and output guardrails.
• Work with a small team of technically sophisticated engineers who put users first.

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 5+ years of backend development experience, with emphasis on building APIs and services that power user-facing experiences
• Experience writing clear, elegant code in a team environment
• Familiarity with LLM APIs and concepts — prompt engineering, token management, embedding models, retrieval-augmented generation, or function calling. You don't need to be a researcher, but you should understand how these models work well enough to build production systems around them.

• Experience building and operating distributed backend systems — you're comfortable with service-oriented architectures, async processing, caching layers, and monitoring/alerting in production.
• Passion for engineering solutions focused around growth hacking & enablement 
• Excellent verbal and written communication

Preferred qualifications

• Proven ability to stitch different services and processes together even if you have not worked with them before
• Experience in developing interactive tools and integrating them with existing systems.
• Experience building highly complex interactive tools, including state management and data fetching
• Experience with data pipeline orchestration (Airflow, Dagster, Temporal, or similar) and structured output parsing from LLMs
• Experience with prompt versioning, A/B testing model outputs, or building model gateway/routing layers that allow teams to swap or compare providers (e.g., OpenAI, Anthropic, open-source models)
• Proficiency with React, with a focus on interactions, animation, performance, and polish
• Experience with A/B testing, synthetic monitoring, or accessibility testing

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Data Transformation team is responsible for building and operating batch and real time transformation pipelines and platforms for 400+ product teams at Stripe.  We're aiming to make data development and management workflow at Stripe a breeze with emphasis on producing high quality datasets used for critical analytics, dashboards and workflows at Stripe. The Products owned by this team are widely adopted with 800+ weekly active users improving developer productivity for data users by many folds. The team is in an interesting phase of innovation tied to topmost priority for Stripe building and executing the strategic roadmap for streaming transformation, incremental processing and ergonomic data modeling.    

We empower our users ranging from Data Scientists to Engineers building pipelines to create exceptional Stripe product experiences while providing a robust transformation platform for critical dashboarding, analytics and workflows at Stripe including user-facing reporting products Sigma and Radar. 

We adopt a combination of open source technologies and in-house built software to ensure high scalability, reliability, and usability of our transformation offerings. Key example technologies used include: Spark, Airflow, Iceberg, Hive, S3, SQL, Python, Scala, GRPC, Kafka and FlinkSQL.

What you’ll do

As an Engineering Manager of the Data Transformation team, you will lead a team of engineers, collaborate with infrastructure and product engineering orgs, and advance the Data Transformation roadmap and adoption at Stripe. You will be driving critical workstreams for Stripe’s topmost priorities around delivering high quality, materialized datasets Stripe products and AI agents.

You will:

• Deliver infrastructure and services that scale to our users needs with an eye on reliability and efficiency
• Lead and manage a team of talented engineers on the team, providing mentorship, guidance, and support to ensure their success
• Work with high-visibility teams and their stakeholders to support the Infrastructure’s key engineering initiatives
• Understand user needs and pain points to prioritize engineering work and deliver high quality solutions that meet user needs
• Drive the execution of projects, overseeing the entire development lifecycle from planning to delivery, while maintaining high standards of quality and timely completion
• Provide hands-on technical leadership (architecture/design, vision/direction/requirements setting, and incident response processes) for your reports
• Work with leaders across the company to create and drive toward the longer term vision of Stripe’s Data Transformation roadmap
• Foster a collaborative and inclusive work environment, promoting innovation, knowledge sharing, and continuous improvement within the team
• Partners with our recruiting team to attract and hire top talent, and define the overall hiring strategies for your team

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

We’re looking for someone who has: 

• 1 - 3 years of experience managing teams that shipped and operated data pipelines and critical distributed system infrastructure
• Successfully recruited and built great teams.
• Extreme customer focus, committed to investing in partnerships with other Stripe engineers to establish empathy and understand their use-cases
• Works effectively cross-functionally and is able to think rigorously, communicate effectively, and make or coordinate hard decisions and trade-offs.
• Thrives with a high degree of autonomy and responsibility in an ambiguous environment.
• Technical acumen to drive clarity with staff engineers about architecture and technical strategic decisions.
• Encourage a healthy and inclusive work environment that’s both challenging and supportive

Preferred qualifications

• Managed teams that shipped products to data users and operated large-scale, high-availability data transformation pipelines, with expertise in Kafka, Flink, Spark, Airflow, Python, SQL, and API design a plus.
• A genuine enjoyment of learning and diving into the nuts-and-bolts of how things work, with the ability to question and direct architectural decisions
• Strong written and verbal communication skills for different audiences (leadership, users, company-wide)

Role Overview 

We are seeking an accomplished Principal Engineer to lead the technical architecture and evolution of our hybrid digital ecosystem. In this pivotal role, you will drive the strategic integration of multi-modal AI into our core infrastructure while architecting an omnichannel platform capable of supporting complex customer journeys. You will balance the adoption of proven third-party AI solutions (OpenAI, Anthropic) with the development of proprietary optimizations where they deliver a competitive advantage. As a technical leader, you will champion a vendor-agnostic, ethically grounded, and Privacy by Design approach to AI implementation.

Key Responsibilities

• Infrastructure Modernization: Lead the roadmap for migrating legacy data pipelines to AI-native architectures. You will design modern data orchestration solutions utilizing Apache Airflow, dbt, and Kafka to replace outdated batch processing with real-time, event-driven flows.
• AI-Native Web Transformation: Define the web infrastructure required for a high-agility ecosystem. Architect the evolution of our Headless CMS environments (AEM, Contentful) and modern frontend frameworks to enable automated page assembly and AI-driven UI components.
• Agentic Pipeline Development: Design and build autonomous pipelines that bridge the gap between design systems, automated component development, and publishing workflows.
• ML & Data Engineering Leadership: Establish standards for RAG, vector databases, and LLM orchestration. Provide architectural guidance for the seamless integration of AI capabilities across headless and omnichannel systems while ensuring consistency, performance, and security.
• Engineering Productivity: Champion the use of AI-assisted coding and engineering productivity tools such as Claude Code and Cursor to accelerate development cycles and optimize architectural decision-making.

Required Qualifications

• Experience: 8–10 years in software engineering, specializing in SaaS platform architecture and distributed systems design.
• Leadership & Advocacy: Excellence in technical diplomacy and stakeholder influence. Proven ability to translate complex AI/Data roadmaps into business value while mentoring senior teams and advocating for architectural best practices.
• Data Orchestration: Strong hands-on experience with Apache Airflow (or similar), Kafka, and dbt to support real-time AI applications.
• AI & Web Convergence: Proven expertise integrating LLMs into production web environments with a focus on agentic workflows and autonomous UI generation.
• Architectural Vision: Deep understanding of headless CMS, composable, and event-driven patterns that allow for programmatic content and UI generation.
• Modern Delivery: Experience with cloud-native technologies (AWS/GCP, Docker, Kubernetes) and a deep expertise in data privacy frameworks and AI ethics.

#LI-Hybrid

#P24079_3413893

Marqeta is looking for a talented Senior Software Engineer to independently identify and deliver software solutions on our Data Infrastructure team through a set of milestones spanning a specific platform focus or a multi-component system. You will own and deliver projects in service of quarterly goals on the team and independently identify solutions to solve ambiguous, open-ended problems. You will work in collaboration with your team and cross-functional partners.

The Data Infrastructure team, within our Data & ML Platforms organization, is responsible for the data lakehouse, streaming, orchestration, and catalog platforms that power analytics and AI across Marqeta. We're a platform engineering team building infrastructure that other engineering teams depend on.

At Marqeta, participation in a rotational on-call pager duty is a required part of the software engineering role. The specifics of the rotation may vary by team, depending on team size and structure, and will be discussed further during the interview process.

We work Flexible First. This role can be performed remotely anywhere within Ontario or British Columbia, Canada. We’d love for you to join us!

Learn more about our Product and Engineering team

The Impact You'll Have

• Define and deliver well-scoped milestones for projects, and may be a technical lead for projects on your team.
• Lead design for small-to-medium components and participate in design for larger components, moving from task-level impact to small-to-medium project-level impact.
• Have significant ownership of a component of the data infrastructure, including its reliability, performance, and developer experience.
• Identify gaps and propose plans towards addressing those gaps.
• Independently write code that is clear, well-tested, and appropriately flexible, reusable, and adaptable to changing requirements.
• Actively level up less-experienced members of your team by helping them with their craft, providing guidance, and setting a good example.
• Participate in on-call coverage.

Who You Are

• 5+ years of experience as a software engineer with a Bachelor's degree; or 3 years of experience with an advanced degree. In lieu of a degree, 8+ years of relevant experience may suffice.
• Strong proficiency in Python in a production context.
• Proficiency with SQL.
• Experience working with distributed systems.
• Production experience with multiple core data technologies, including Apache Kafka, Apache Spark, and Apache Airflow (or close equivalents).
• Production experience with modern table formats (Parquet, Hudi, Iceberg, or Delta Lake).
• Production experience with AWS or another major cloud.
• Experience with infrastructure-as-code (Terraform, CloudFormation, CDK, or equivalent).
• Experience monitoring and alerting on production systems.
• Ability to decompose business problems into solutions that may require designs with multiple interacting components.
• Ability to build correct, maintainable software components.
• Ability to work with cross-functional stakeholders to identify technical blindspots and clarify ambiguity.
• Ability to present clear and concise messages tailored to various audiences; able to communicate clearly and respectfully both verbally and in writing.
• A focus on quality and meeting deadlines.
• Ability to decompose business problems into solutions that may require designs with multiple interacting components.
• Ability to build correct, maintainable software components.
• Ability to work with cross-functional stakeholders to identify technical blindspots and clarify ambiguity.
• Ability to present clear and concise messages tailored to various audiences; able to communicate clearly and respectfully both verbally and in writing.
• A focus on quality and meeting deadlines.

Nice-To-Haves

• Experience with payments or the FinTech space.
• Experience with Apache Iceberg specifically.
• Experience with CDC tooling, particularly Debezium or Kafka Connect.
• Experience with data catalog tooling (DataHub or equivalent).
• Experience with Java in addition to Python.
• Exposure to Kubernetes and container-based workloads.
• Experience building scalable software platforms.

Our (typical) process:

1. Application Submission
2. Recruiter Video Call
3. Coding Interview & Hiring Manager Interview
4. Virtual “Onsite” consisting of three 60 minute rounds
5. Offer!

Compensation and Benefits

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office. To support Flex First, we calibrate pay to a competitive value according to working location. 

When determining salaries, we consider several factors including, but not limited to, skills, prior experience, and work location. The new-hire base salary range for this position, reflected in CAD, is: 116,000 - 145,000

We also believe in recognizing the contributions of our people. That's why we award annual bonuses to eligible employees, rewarding both individual performance and the success of the entire company.

Along with monetary compensation, Marqeta offers

• Multiple health insurance options
• Flexible vacation time
• Retirement savings program with company contribution
• Equity in a publicly-traded company 
• Monthly stipend to support our remote work model
• Annual “development dollars” to support our people growth and development
• Family-forming benefits and up to 20 weeks of Parental Leave

Role & Team

As a Senior Engineer, you will have a direct impact on the development of core geospatial products and internal data-processing and annotation tools at Overstory. 

Your breadth of skills will allow you to set a technical vision for the development of the web applications, ETL pipelines, and backend services owned by these teams.

You’ll partner with product managers, external resources, and your team members to execute this vision.

Time zone requirement: Europe (GMT/WET, CET, EET) and North America (NST, AST, EST, CST, MST, PST)

What You’ll Do

• Explore best practices to combine coding agents with software craftsmanship and teach other engineers these practices
• Optimize the orchestration of several object detection and image alignment models
• Design and maintain robust data and feature pipelines for millions of geospatial entities
• Contribute to the technical vision for the development of our internal annotation and QA web applications
• Work closely with your Product Manager to discover and propose new products to solve use cases such as anticipating the impact of an outage at a particular locatio

Skills & Experience

• 5+ years of experience as a senior software engineer 
• Skilled in designing ETL pipelines with near-real-time orchestration, in Python.
• Strong background building and maintaining scalable web-applications with React, FastAPI, PostgreSQL
• Experience with XP practices such as TDD or trunk based development. The team would be new to these practices
• Strong communication skills and ability to explain complex technical concepts to various audience

Nice To Have

• Experience with the Geospatial ecosystem such as PostGIS, Geopandas or Deck.GL
• Experience with Dagster, Airflow or similar orchestration frameworks
• Experience with building web-applications tailored for repeatable human or agentic workflows
• Experience working in B2B, in particular with electrical utilities

Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.

What We Offer

• Competitive, location-specific compensation and benefits 
• Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
• Home office stipend, coworking and ongoing education budgets 
• A company culture that genuinely embodies each of our core values
• To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis

Consensys is the leading blockchain and web3 software company founded by Joe Lubin, CEO of Consensys and Co-Founder of Ethereum. Since 2014, Consensys has been at the forefront of innovation, pioneering technological developments within the web3 ecosystem.

Through our product suite, including the MetaMask platform, Infura, Linea, Diligence, and our NFT toolkit Phosphor, we have become the trusted collaborator for users, creators, and developers on their path to build and belong in the world they want to see.

Whether building a dapp, an NFT collection, a portfolio, or a better future, the instinct to build is universal. Consensys inspires and champions the builder instinct in everyone by making web3 universally easy to use and develop on.

Our mission is to unlock the collaborative power of communities by making the decentralized web universally easy to access, use, and build on. 

You’ll get to work on the tools, infrastructure, and apps that scale these platforms to onboard one billion participants and 5 million developers. You’ll be constantly exposed to new concepts, ideas, and frameworks from your peers, and as you work on different projects — challenging you to stay at the top of your game. You’ll join a network of builders that reaches the edge of our ecosystem. Consensys alumni have moved on to become tech entrepreneurs, CEOs, and team leads at tech companies. 

About the Data Team

The Data team sits within the Office of the COO and builds the infrastructure that powers analysis across the organisation. We design, run, and optimise pipelines that bring together product, marketing, and financial data at scale. Our role is to ensure data is reliable, accessible, and ready for use—helping Consensys move fast while maintaining quality and trust.

What you’ll do

• Design, build, and maintain robust data pipelines that integrate sources across the business.
• Collaborate closely with analysts, business stakeholders, and other engineering teams to gather requirements, align timelines, discuss architecture, and ensure successful delivery.
• Document data pipelines, best practices, and processes to support onboarding and knowledge sharing within the team.
• Develop and optimize data models to deliver trusted, structured, and business-ready data.
• Ensure data quality, security, and governance are embedded in all pipelines and systems.
• Orchestrate and monitor pipeline execution to ensure reliability and scalability.
• Deploy and manage infrastructure as code.
• Build and tune big data pipelines using SQL, Python, and distributed processing frameworks.
• Work with cloud data warehouses to enable insights and analytics.
• Maintain and update reporting solutions and user dashboards.
• Automate workflows and improve CI/CD pipelines to reduce manual processes.

Would be great if you brought this to the role

• Over 6 years of experience as a Data Engineer
• Use Trusted Execution Environments (TEEs) to securely process sensitive user data, ensuring it remains protected and only aggregated insights are released.
• Strong SQL skills and experience with cloud warehouses (e.g.: Snowflake, BigQuery, Redshift).
• Hands-on experience with transformation and orchestration tools (e.g.: dbt, Airflow, Dagster).
• Comfort with Python or other scripting languages for ETL and automation.
• Familiarity with data governance and metadata management (e.g.: DataHub).
• Experience deploying and managing infrastructure as code (e.g.: Terraform, Pulumi).
• Exposure to data integration and ingestion tools (e.g.: Airbyte, Segment).
• Experience with big data and distributed processing (e.g.: Apache Spark, AWS EMR, S3).
• Exposure to open source projects
• Experience maintaining and improving reporting solutions and dashboards (e.g.: Preset/Superset, Cube.dev).
• Familiarity with CI/CD practices and automation (e.g.: GitHub Actions).
• A collaborative mindset and eagerness to work with both technical and non-technical colleagues.

Don't meet all the requirements? Don't sweat it. We’re passionate about building a diverse team of humans and as such, if you think you've got what it takes for our chaotic-but-fun, remote-friendly, start-up environment—apply anyway, detailing your relevant transferable skills in your cover letter. While we have a pretty good idea of what we need, we're ready for you to challenge our thinking on who needs to be in this role.

It is a requirement of employment in this position that applicants will be required to submit to background checks including but not limited to employment, education and criminal record checks. Further details will be provided to applicants that successfully meet the criteria for the position as determined by the company in its sole discretion. By submitting an application for employment, you are acknowledging and consenting to this requirement.

Overview

Our backend systems power the clients used by millions of customers every year to buy their groceries online. These systems must also support tight integration with the largest retailers in the US and Canada. Engineering at Instacart provides the opportunity to work on challenging scaling problems while also designing the features that will define our industry. You will learn how to build in an open collaborative environment serving millions of requests daily.

Finance data engineering is part of the Data Infrastructure team, working closely with accounting, billing & revenue teams to support the monthly/quarterly book close, retailer invoicing and internal/external financial reporting. The team plays a critical role in defining how financial data is modeled and standardized for uniform, reliable, timely and accurate reporting. This is a high impact, high visibility role owning critical data integration pipelines and models across all of Instacart's products.

The team is still small and many of our bigger initiatives are at an early stage. We expect you to work closely with stakeholders and shape these systems from design, technical decisions, project management to execution. Your input will be critical for driving the Finance Data Strategy and building the platform that Instacart's financial data infrastructure will be built upon.

About the Job

• You will work closely with finance, accounting, billing, and revenue teams to understand their main pain points and translate them into self-serve, reliable, and scalable data solutions.
• You are expected to mentor other team members and be a champion of engineering excellence across the organization.
• You will be part of a small team with a large amount of ownership and autonomy, managing initiatives directly from design through execution.
• You will have the freedom to suggest and drive organization-wide initiatives that shape the financial data vision and roadmap.
• You will own critical data integration pipelines and models, ensuring uniform, reliable, timely, and accurate financial reporting.

About You

Minimum Qualifications

• 8+ years of working experience in a Data/Software Engineering role, with a focus on building data pipelines (specialized in financial data).
• Strong knowledge of cloud based warehouses like Snowflake, Redshift, BigQuery.
• Strong knowledge of common data infra technologies (Python, Airflow, Spark, Iceberg, Delta Lake) in a production environment.
• Expert in data modeling with experience in modeling complex financial data with scalability and usability in mind.
• Expert with SQL and dbt (Data Build Tool).
• Experience building high quality and complex ETL/ELT pipelines including pipelines for accounting/billing purposes.
• Experience with data immutability, auditability, slowly changing dimensions or similar concepts.
• Adept at fluently communicating with many cross-functional stakeholders to drive requirements and design shared datasets.
• An ability to balance a sense of urgency with shipping high quality and pragmatic solutions.
• Experience working with a large codebase on a cross functional team.

Preferred Qualifications

• Bachelor's degree in Computer Science, computer engineering, electrical engineering or equivalent work experience.
• Experience with Snowflake, dbt (Data Build Tool) and Airflow.
• Experience with SOX controlled data systems.

#LI-Remote

Overview

Our backend systems power the clients used by millions of customers every year to buy their groceries online. These systems must also support tight integration with the largest retailers in the US and Canada. Engineering at Instacart provides the opportunity to work on challenging scaling problems while also designing the features that will define our industry. You will learn how to build in an open collaborative environment serving millions of requests daily.

Finance data engineering is part of the Data Infrastructure team, working closely with accounting, billing & revenue teams to support the monthly/quarterly book close, retailer invoicing and internal/external financial reporting. The team plays a critical role in defining how financial data is modeled and standardized for uniform, reliable, timely and accurate reporting. This is a high impact, high visibility role owning critical data integration pipelines and models across all of Instacart's products.

The team is still small and many of our bigger initiatives are at an early stage. We expect you to work closely with stakeholders and shape these systems from design, technical decisions, project management to execution. Your input will be critical for driving the Finance Data Strategy and building the platform that Instacart's financial data infrastructure will be built upon.

About the Job

• You will work closely with finance, accounting, billing, and revenue teams to understand their main pain points and translate them into self-serve, reliable, and scalable data solutions.
• You are expected to mentor other team members and be a champion of engineering excellence across the organization.
• You will be part of a small team with a large amount of ownership and autonomy, managing initiatives directly from design through execution.
• You will have the freedom to suggest and drive organization-wide initiatives that shape the financial data vision and roadmap.
• You will own critical data integration pipelines and models, ensuring uniform, reliable, timely, and accurate financial reporting.

About You

Minimum Qualifications

• 8+ years of working experience in a Data/Software Engineering role, with a focus on building data pipelines (specialized in financial data).
• Strong knowledge of cloud based warehouses like Snowflake, Redshift, BigQuery.
• Strong knowledge of common data infra technologies (Python, Airflow, Spark, Iceberg, Delta Lake) in a production environment.
• Expert in data modeling with experience in modeling complex financial data with scalability and usability in mind.
• Expert with SQL and dbt (Data Build Tool).
• Experience building high quality and complex ETL/ELT pipelines including pipelines for accounting/billing purposes.
• Experience with data immutability, auditability, slowly changing dimensions or similar concepts.
• Adept at fluently communicating with many cross-functional stakeholders to drive requirements and design shared datasets.
• An ability to balance a sense of urgency with shipping high quality and pragmatic solutions.
• Experience working with a large codebase on a cross functional team.

Preferred Qualifications

• Bachelor's degree in Computer Science, computer engineering, electrical engineering or equivalent work experience.
• Experience with Snowflake, dbt (Data Build Tool) and Airflow.
• Experience with SOX controlled data systems.

#LI-Remote

About the Role

In 2026, Orium is investing in AI and automation to improve how SG&A functions operate across the organization.

Our goal is to build agentic systems that support real human decision-making — including a unified entry point and two-way workflows that allow agents, humans, and systems to interact reliably. We’re approaching this work intentionally, with strong foundations around trust, escalation, and human judgment.

We’ve completed early experimentation through pilots and demos, and we’re now looking for someone to help turn that learning into durable, production-ready capabilities.

This is a contract role focused on hands-on execution with strong technical ownership. As a Senior AI Systems Engineer you’ll work hands-on to design and build the technical foundations of these systems, partnering closely with internal stakeholders to translate evolving needs into reliable, scalable agentic workflows. The focus of this engagement is to accelerate the build-out of core agentic capabilities and to establish strong technical foundations over a defined period.

Role Details:

• Type: Contract (Part-Time)
• Duration: ~16 weeks
• Expected Commitment: ~20 hours per week
• Potential for extension or future engagement based on outcomes

Key Responsibilities

Agentic System Development

• Partner in shaping the technical approach and architecture of agentic systems, helping translate evolving needs into practical, scalable solutions.
• Design and implement agentic workflows that support two-way interaction between humans, agents, and internal systems.
• Build and orchestrate workflow logic using tools and frameworks such as Zapier + code, n8n (or similar), APIs, and event-driven architectures.
• Partner to integrate people-focused agentic workflows into a cohesive, reliable entry-point experience.

Systemization & Reliability

• Bring a technical perspective to early-stage ideas and prototypes, helping assess feasibility, trade-offs, and paths to production.
• Help evolve early implementations into more robust, maintainable, and production-ready systems.
• Help establish and document patterns for reliability, observability, and safe failure in agentic workflows.
• Ensure implementations align with defined trust models, escalation rules, and human-in-the-loop principles.

AI & Automation Craft

• Work hands-on with LLMs, prompts, and agent behaviour to prioritize correctness, clarity, and human accountability.
• Evaluate and integrate tools, frameworks, and platforms that support agentic patterns as the system matures.
• Contribute to and document shared standards and patterns for AI-augmented systems at Orium.
• Design and evolve retrieval and indexing approaches (e.g., RAG, structured knowledge sources) to ensure agents operate on accurate, relevant, and up-to-date context.

Skills & Experience

• Experience building, integrating, or supporting AI / LLM-powered systems, with an emphasis on reliability, validation, and safe failure.
• Proven experience taking systems from prototype to production, including defining architecture, managing trade-offs, and ensuring reliability at scale.
• Strong experience building backend systems, workflows, or integrations, including APIs, event-driven systems, and automation frameworks.
• Comfortable working with orchestration tools and workflow engines (e.g., Zapier + code, n8n, Temporal, Airflow, or similar), including extending platforms with custom logic where needed.
• Experience designing or contributing to system architecture, particularly in integration-heavy or distributed systems.
• Strong proficiency in JavaScript/Typescript, including building and deploying backend services, integrations, or extensions to support workflow and automation systems.
• Comfortable taking ownership in ambiguous problem spaces, helping define the path forward rather than waiting for fully defined requirements.
• Strong collaborator who enjoys building in partnership with product, domain, and systems owners.

Is the Role a Fit for You?

You’ll be a strong fit for this role if you:

• Think with a product mindset, not a ticket mindset. You take the time to understand stakeholder needs and real-world workflows before building solutions.
• Are comfortable working in ambiguity. You can take rough ideas or early prototypes and shape them into clear, working systems.
• Think in systems. You naturally connect tools, people, and processes into cohesive, scalable workflows.
• Take ownership and drive outcomes. You define the path forward and see things through to production, not just implementation.
• Balance speed with sound judgment. You move quickly while being thoughtful about reliability, failure modes, and human impact, especially when working with AI systems.

Compensation and Hiring Transparency 

• Expected Compensation (Mexico): Compensation will be offered in MXN, aligned with local market benchmarks and the employment model. Final compensation will consider experience, skills, location, internal equity, and business needs.
• Use of AI in Hiring: To keep the process efficient and focused on human connection, we use AI-enabled and automated tools for tasks like candidate communications and scheduling, interview note-taking, and summarizing feedback for hiring teams. Our recruiters and hiring managers review all information, and humans make every hiring decision.
• Vacancy Status: This posting is for an existing vacancy we are actively hiring for.

Committed to an Inclusive Workplace

We are committed to fostering an inclusive and accessible workplace. We provide reasonable accommodations throughout the hiring process in accordance with applicable local laws. If you require accommodations, we will work with you to meet your needs.

Committed to Social and Environmental Stewardship

Orium is committed to promoting and working towards a more socially and environmentally sustainable future. All employees are expected to make decisions and take actions in relation to their role and work that follow through on this commitment.

#LI-Remote

About the Role

In 2026, Orium is investing in AI and automation to improve how SG&A functions operate across the organization.

Our goal is to build agentic systems that support real human decision-making — including a unified entry point and two-way workflows that allow agents, humans, and systems to interact reliably. We’re approaching this work intentionally, with strong foundations around trust, escalation, and human judgment.

We’ve completed early experimentation through pilots and demos, and we’re now looking for someone to help turn that learning into durable, production-ready capabilities.

This is a contract role focused on hands-on execution with strong technical ownership. As a Senior AI Systems Engineer you’ll work hands-on to design and build the technical foundations of these systems, partnering closely with internal stakeholders to translate evolving needs into reliable, scalable agentic workflows. The focus of this engagement is to accelerate the build-out of core agentic capabilities and to establish strong technical foundations over a defined period.

Role Details:

• Type: Contract (Part-Time)
• Duration: ~16 weeks
• Expected Commitment: ~20 hours per week
• Potential for extension or future engagement based on outcomes

Key Responsibilities

Agentic System Development

• Partner in shaping the technical approach and architecture of agentic systems, helping translate evolving needs into practical, scalable solutions.
• Design and implement agentic workflows that support two-way interaction between humans, agents, and internal systems.
• Build and orchestrate workflow logic using tools and frameworks such as Zapier + code, n8n (or similar), APIs, and event-driven architectures.
• Partner to integrate people-focused agentic workflows into a cohesive, reliable entry-point experience.

Systemization & Reliability

• Bring a technical perspective to early-stage ideas and prototypes, helping assess feasibility, trade-offs, and paths to production.
• Help evolve early implementations into more robust, maintainable, and production-ready systems.
• Help establish and document patterns for reliability, observability, and safe failure in agentic workflows.
• Ensure implementations align with defined trust models, escalation rules, and human-in-the-loop principles.

AI & Automation Craft

• Work hands-on with LLMs, prompts, and agent behaviour to prioritize correctness, clarity, and human accountability.
• Evaluate and integrate tools, frameworks, and platforms that support agentic patterns as the system matures.
• Contribute to and document shared standards and patterns for AI-augmented systems at Orium.
• Design and evolve retrieval and indexing approaches (e.g., RAG, structured knowledge sources) to ensure agents operate on accurate, relevant, and up-to-date context.

Skills & Experience

• Experience building, integrating, or supporting AI / LLM-powered systems, with an emphasis on reliability, validation, and safe failure.
• Proven experience taking systems from prototype to production, including defining architecture, managing trade-offs, and ensuring reliability at scale.
• Strong experience building backend systems, workflows, or integrations, including APIs, event-driven systems, and automation frameworks.
• Comfortable working with orchestration tools and workflow engines (e.g., Zapier + code, n8n, Temporal, Airflow, or similar), including extending platforms with custom logic where needed.
• Experience designing or contributing to system architecture, particularly in integration-heavy or distributed systems.
• Strong proficiency in JavaScript/Typescript, including building and deploying backend services, integrations, or extensions to support workflow and automation systems.
• Comfortable taking ownership in ambiguous problem spaces, helping define the path forward rather than waiting for fully defined requirements.
• Strong collaborator who enjoys building in partnership with product, domain, and systems owners.

Is the Role a Fit for You?

You’ll be a strong fit for this role if you:

• Think with a product mindset, not a ticket mindset. You take the time to understand stakeholder needs and real-world workflows before building solutions.
• Are comfortable working in ambiguity. You can take rough ideas or early prototypes and shape them into clear, working systems.
• Think in systems. You naturally connect tools, people, and processes into cohesive, scalable workflows.
• Take ownership and drive outcomes. You define the path forward and see things through to production, not just implementation.
• Balance speed with sound judgment. You move quickly while being thoughtful about reliability, failure modes, and human impact, especially when working with AI systems.

Compensation and Hiring Transparency 

• Compensation: Contract rates are determined based on experience, engagement scope, duration, and time commitment. Rate discussions will take place during initial conversations.
• Use of AI in Hiring: To keep the process efficient and focused on human connection, we use AI-enabled and automated tools for tasks like candidate communications and scheduling, interview note-taking, and summarizing feedback for hiring teams. Our recruiters and hiring managers review all information, and humans make every hiring decision.

Committed to an Inclusive Workplace

We are committed to fostering an inclusive and accessible environment as we build a workforce that reflects the diversity of the community in which we live, including those with disabilities. We are dedicated to providing accommodations in all parts of the hiring process in accordance with the Accessibility for Ontarians with Disabilities Act and the Ontario Human Rights Code. If you require any accommodation, we will work with you to meet your needs.

Committed to Social and Environmental Stewardship

Orium is committed to promoting and working towards a more socially and environmentally sustainable future. All individuals engaged by Orium are expected to make decisions and take actions in relation to their role and work that follow through on this commitment.

#LI-Remote

Overview

Instacart’s Data Infrastructure organization builds and operates the systems that power our company’s data ecosystem, including a modern open data lakehouse on Apache Iceberg, a multi-engine compute platform for stream and analytical workloads, and self-serve tooling that helps Product, Data Science, ML, Ads, Finance, and engineering teams move fast with data.

We’re looking for a Staff Software Engineer, Data Infrastructure to join our Data Governance and Foundations Team. In this role, you’ll serve as a senior technical leader owning the architecture and delivery of our open lakehouse foundation, governance and access patterns, and multi-engine compute strategy—balancing today’s reliability with the next three to five years of scale, maturity, and cost efficiency.

You’ll collaborate closely with engineering leadership and stakeholders across Data Science, ML Platform, Ads Infrastructure, Finance Engineering, Product Engineering, and Security. You’ll operate with a high degree of ownership in a fast-paced environment where architectural decisions have real technical and financial consequences. If you thrive on complex, high-scale challenges and roll-up-your-sleeves execution, this is a chance to shape the backbone of Instacart’s data platform.

Our stack includes technologies such as Apache Iceberg, Apache Flink, Trino, ClickHouse, Apache Kafka, Apache Spark, Snowflake, Databricks, Confluent, Airflow, dbt, Delta Lake, Scala, Python, Postgres, and AWS. You’ll join a focused team of 7 engineers that values pragmatism, clarity, and impact.

About the Job

• Translate Instacart’s data strategy (e.g., monetization, federated access, real-time) into an actionable multi-year architecture roadmap; align with leadership while evolving the platform for scale, maturity, and cost efficiency.
• Own the open lakehouse foundation: define and deliver unified table formats, storage governance, and a multi-engine compute portfolio (interactive, batch, streaming) that enables portability and prevents lock-in.
• Drive real-time and streaming infrastructure for critical use cases (Ads, Fraud, ML): set deployment patterns, SLAs, and operational practices that balance performance, availability, and spend.
• Pioneer AI-native data infrastructure engineering by applying LLM/AI tools to the platform lifecycle—accelerating development, automation, observability, and cost optimization—and partnering to embed AI-powered capabilities into the platform.
• Elevate engineering excellence: lead architecture reviews, mentor senior/staff engineers, influence hiring, and clearly communicate complex trade-offs to both technical and executive audiences to ensure cross-org alignment.

About You

Minimum Qualifications

• 5+ years of software engineering experience building and operating data infrastructure or distributed systems at production scale.
• Hands-on expertise with modern data lakehouse architectures and open table formats (e.g., Apache Iceberg, Delta Lake, Hudi) and with distributed query/compute engines (e.g., Trino, Spark, ClickHouse), including performance tuning and production reliability.
• Experience with event-driven and streaming infrastructure (e.g., Kafka, Flink) for real-time pipelines and serving systems.
• Proven ownership of major platform transitions or migrations (build vs. buy, migration design, risk management) delivered to production.
• Ability to build cost/benefit and TCO models for infrastructure investments and to drive alignment via clear architecture docs and strategy memos across multiple teams and leadership levels.

Preferred Qualifications

• Experience designing platform-level governance controls and familiarity with compliance frameworks (e.g., SOX, CPRA, GDPR).
• FinOps experience optimizing data platform spend, including managing multi-million dollar infrastructure budgets and negotiating vendor contracts.
• Deep SQL proficiency and strong skills in Python or Scala for systems-level development.
• Experience with orchestration (e.g., Apache Airflow) and data transformation pipelines (e.g., dbt) in large-scale production environments.
• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience.

#LI-Remote

About the Role:

We are looking for a Senior Software Engineer to architect, build, and maintain the data infrastructure at Gusto.  As part of the Data Platform team, you will collaborate closely with Data Science, Business Intelligence, and analysts across Gusto teams to help them achieve their goals. 

The Data Platform position is a software development role with knowledge of data architectures and data delivery. The ideal candidate is passionate about developing software, working with data, and

Here’s what you’ll do day-to-day:

• Architect, build, and refine our infrastructure and tools that enable other teams to work with data.
• Efficiently handle vast amounts of clickstream, database, and third party application data, processing in batch and real-time
• Take full ownership of the solutions you build, working with stakeholders to develop requirements, implement solutions, monitor production, and troubleshoot problems that arise.
• Work as part of a team. We value team players who share their knowledge and like collaborating with others.

Here’s what we're looking for:

• • At least 7 years of software engineering experience.
  • Experience building solutions in the cloud, AWS preferred (Redshift, MSK, EMR).
  • Experience with OLAP databases (Clickhouse)
  • Experience building data pipelines at scale, Airflow and Python preferred.
  • Experience with streaming systems desired (Kafka, Kinesis, or similar).
  • Ability to turn vague requirements into clear deliverables with minimal guidance.
  • Experience building and maintaining a modern data stack in production.

Our cash compensation amount for this role is targeted at $163,000-$204,000/year in Denver, $178,000-$223,000/year in Los Angeles, and $197,000-$247,000/year for San Francisco, New York, and Seattle. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

Role Description

Responsibilities

• Help define company data assets (data model), Spark, SparkSQL jobs to populate data models
• Help define and design data integrations, data quality frameworks and design and evaluate open source/vendor tools for data lineage
• Work closely with Dropbox business units and engineering teams to develop strategy for long term Data Platform architecture to be efficient, reliable and scalable 
• Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
• Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
• Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
• Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts

Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.

Requirements

• 5+ years of Spark, Python, Java, C++, or Scala development experience
• 5+ years of SQL experience
• 5+ years of experience with schema design, dimensional data modeling, and medallion architectures
• Experience with the Databricks platform and data lake architectures for large-scale data processing and analytics
• Excellent product strategic thinking and communications to influence product and cross-functional teams by identifying the data opportunities to drive impact
• BS degree in Computer Science or related technical field involving coding (e.g., physics or mathematics), or equivalent technical experience
• Experience designing, building and maintaining data processing systems

Role Description

Responsibilities

• Help define company data assets (data model), Spark, SparkSQL jobs to populate data models
• Help define and design data integrations, data quality frameworks and design and evaluate open source/vendor tools for data lineage
• Work closely with Dropbox business units and engineering teams to develop strategy for long term Data Platform architecture to be efficient, reliable and scalable 
• Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
• Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
• Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
• Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts

Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.

Requirements

• 5+ years of Spark, Python, Java, C++, or Scala development experience
• 5+ years of SQL experience
• 5+ years of experience with schema design, dimensional data modeling, and medallion architectures
• Experience with the Databricks platform and data lake architectures for large-scale data processing and analytics
• Excellent product strategic thinking and communications to influence product and cross-functional teams by identifying the data opportunities to drive impact
• BS degree in Computer Science or related technical field involving coding (e.g., physics or mathematics), or equivalent technical experience
• Experience designing, building and maintaining data processing systems

Role Description

Dropbox is looking for a Staff Data Engineer to join our Analytics Data Engineering (ADE) team within Data Science & AI Platform. You will be responsible for solving cross-cutting data challenges that span multiple lines of business while driving standardization in how we build, deploy, and govern analytics pipelines across Dropbox.

This is not a maintenance role. We are modernizing our analytics platform, upgrading orchestration infrastructure, building shared and reusable data models with conformed dimensions, establishing a certified metrics framework, and laying the foundation for AI-native data development. You will partner closely with Data Science, Data Infrastructure, Product Engineering, and Business Intelligence teams to make this happen.

You will play a crucial role in establishing analytics engineering standards, designing scalable data models, and driving cross-functional alignment on data governance. You will get substantial exposure to senior leadership, shape the technical direction of analytics infrastructure at Dropbox, and directly influence how data powers product and business decisions.

Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.

Responsibilities

• Lead the design and implementation of shared, reusable data models, defining shared fact tables, conformed dimensions, and a semantic/metrics layer that serves as the single source of truth across analytics functions
• Drive standardization of data engineering practices across ADE and functional analytics teams, including pipeline patterns, CI/CD workflows, naming conventions, and data modeling standards
• Partner with Data Infrastructure to modernize orchestration, improve pipeline decomposition, and establish secure dev/test environments with production data access
• Architect and implement a shift-left data governance strategy,  working with upstream data producers to establish data contracts, SLOs, and code-enforced quality gates that catch issues before production
• Collaborate with Data Science leads and Product Management to translate metric definitions into reliable, certified data pipelines that power executive dashboards, WBR reporting, and growth measurement
• Reduce operational burden by improving pipeline granularity, observability, and failure recovery, establishing runbooks and alerting standards that make on-call sustainable
• Evaluate and integrate AI-native tooling into the data development lifecycle, enabling conversational data exploration with guardrails and AI-assisted pipeline development

Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.

Requirements

• BS degree in Computer Science or related technical field, or equivalent technical experience
• 12+ years of experience in data engineering or analytics engineering with increasing scope and technical leadership
• 12+ years of SQL experience, including complex analytical queries, window functions, and performance optimization at scale (Spark SQL)
• 8+ years of Python development experience, including building and maintaining production data pipelines
• Deep expertise in dimensional data modeling, schema design, and scalable data architecture, with hands-on experience building shared data models across multiple business domains
• Strong experience with orchestration tools (Airflow strongly preferred) and dbt, including pipeline design, scheduling strategies, and failure recovery patterns
• Demonstrated ability to drive cross-team technical alignment, establishing standards, influencing without authority, and working across Data Engineering, Data Science, Data Infrastructure, and Product Engineering boundaries

Preferred Qualifications

• Experience with Databricks (Unity Catalog, Delta Lake) and modern lakehouse architectures
• Experience leading orchestration or platform modernization efforts at scale
• Familiarity with data governance and observability tools such as Atlan, Monte Carlo, Great Expectations, or similar
• Experience building or contributing to a metrics/semantic layer (dbt MetricFlow, Databricks Metric Views, or equivalent)
• Track record of establishing data engineering standards and best practices in a federated analytics organization

Compensation

Role Description

Dropbox is looking for a Staff Data Engineer to join our Analytics Data Engineering (ADE) team within Data Science & AI Platform. You will be responsible for solving cross-cutting data challenges that span multiple lines of business while driving standardization in how we build, deploy, and govern analytics pipelines across Dropbox.

This is not a maintenance role. We are modernizing our analytics platform, upgrading orchestration infrastructure, building shared and reusable data models with conformed dimensions, establishing a certified metrics framework, and laying the foundation for AI-native data development. You will partner closely with Data Science, Data Infrastructure, Product Engineering, and Business Intelligence teams to make this happen.

You will play a crucial role in establishing analytics engineering standards, designing scalable data models, and driving cross-functional alignment on data governance. You will get substantial exposure to senior leadership, shape the technical direction of analytics infrastructure at Dropbox, and directly influence how data powers product and business decisions.

Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.

Responsibilities

• Lead the design and implementation of shared, reusable data models, defining shared fact tables, conformed dimensions, and a semantic/metrics layer that serves as the single source of truth across analytics functions
• Drive standardization of data engineering practices across ADE and functional analytics teams, including pipeline patterns, CI/CD workflows, naming conventions, and data modeling standards
• Partner with Data Infrastructure to modernize orchestration, improve pipeline decomposition, and establish secure dev/test environments with production data access
• Architect and implement a shift-left data governance strategy,  working with upstream data producers to establish data contracts, SLOs, and code-enforced quality gates that catch issues before production
• Collaborate with Data Science leads and Product Management to translate metric definitions into reliable, certified data pipelines that power executive dashboards, WBR reporting, and growth measurement
• Reduce operational burden by improving pipeline granularity, observability, and failure recovery, establishing runbooks and alerting standards that make on-call sustainable
• Evaluate and integrate AI-native tooling into the data development lifecycle, enabling conversational data exploration with guardrails and AI-assisted pipeline development

Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.

Requirements

• BS degree in Computer Science or related technical field, or equivalent technical experience
• 12+ years of experience in data engineering or analytics engineering with increasing scope and technical leadership
• 12+ years of SQL experience, including complex analytical queries, window functions, and performance optimization at scale (Spark SQL)
• 8+ years of Python development experience, including building and maintaining production data pipelines
• Deep expertise in dimensional data modeling, schema design, and scalable data architecture, with hands-on experience building shared data models across multiple business domains
• Strong experience with orchestration tools (Airflow strongly preferred) and dbt, including pipeline design, scheduling strategies, and failure recovery patterns
• Demonstrated ability to drive cross-team technical alignment, establishing standards, influencing without authority, and working across Data Engineering, Data Science, Data Infrastructure, and Product Engineering boundaries

Preferred Qualifications

• Experience with Databricks (Unity Catalog, Delta Lake) and modern lakehouse architectures
• Experience leading orchestration or platform modernization efforts at scale
• Familiarity with data governance and observability tools such as Atlan, Monte Carlo, Great Expectations, or similar
• Experience building or contributing to a metrics/semantic layer (dbt MetricFlow, Databricks Metric Views, or equivalent)
• Track record of establishing data engineering standards and best practices in a federated analytics organization

Compensation

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

 Our mission depends on having a digital representation of the physical world - a map with all routing related (real-time) information. This is what makes Lyft different from many products: our products don’t just facilitate online interactions, they facilitate dynamic, real-world ones. Without mapping services, none of these real world interactions between people and transport can happen.

We are hiring a Senior Data Engineer to join our Pickup, Places and Search team within the Mapping organization that is responsible for building, enhancing and maintaining the Rider and Driver product for the Pickup/Drop off Experience. This team supports the backbone of Lyft’s search system, supporting millions of rides by helping our riders and drivers connect and reach their destination. Our focus is to ensure the pickup/dropoff experience is seamless by enhancing guidance and routing as well as the rider experience. Additionally the team uses map data and other signals to create new product experiences in collaboration with teams across Lyft. This team has a history of enabling rich and creative features that directly influence the product for all of our users. We constantly innovate and incorporate cutting-edge technologies to make the lives of our community more enriched.

In this role, you'll collaborate with other engineering teams, product, data science, analytics, and operations on programs that empower us to iterate quickly, delighting our passengers and drivers with rideshare focused mapping experiences.

Responsibilities:

• Owner of core company data pipelines, responsible for scaling up data processing flow to meet the rapid data growth at Lyft
• Continuously evolve data models and schemas based on business and engineering requirements
• Implement and maintain systems to monitor and enhance data quality and consistency
• Develop tools that support self-service management of data pipelines (ETL) and optimize data processing performance
• Write well-crafted, well-tested, readable, maintainable code that prioritizes scalability and cost efficiency
• Conduct code reviews to ensure code quality standards and facilitate knowledge sharing
• Participate in on-call rotations to maintain high availability and reliability of workflows and data pipelines
• Collaborate with internal and external partners to remove blockers, provide support, and achieve results
• Help establish technical roadmaps and architectures based on technology and our business needs.
• Drive high-impact projects and innovate new solutions to provide the best mapping experience possible.
• Ship ML models at scale and low cost by focussing on systems / performance engineering.
  
  

Experience:

• 5+ years of relevant professional experience in data engineering or a related field
• Strong expertise in SQL and experience with Trino or Spark/PySpark
• Experience building and optimizing complex data models and pipelines with a strong understanding of ETL processes
• Expertise in data modeling and ETL, with experience developing and optimizing complex data models and pipelines
• Hands-on experience with workflow management tools (e.g., Airflow or similar)
• Proficiency in a scripting language like Python or Bash
• Comfortable working directly with cross-functional teams (data analytics, data science, engineering) to align solutions with business goals

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $136,000 - CAD $170,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

 This job fills an existing vacancy.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

As a Data Engineer on the Mapping team, you will collaborate with our world class team of engineers, product managers, and data scientists to grow and improve the quality of recommended routes and accuracy of our travel time estimations. You will architect, build, and maintain scalable data pipelines and services to support route simulation, experimentation, analytics, and machine learning models. Your work will enable integration with partner teams and allow stakeholders across Engineering, Data Science, and Product to make data-informed decisions that directly impact Lyft’s growth and profitability.

Our technology stack is based on the latest technologies such as AWS, Kubernetes and Apache Airflow. You will work with incredibly passionate and talented colleagues from software engineering, machine learning and data science on projects that directly impact millions of riders and drivers.

Responsibilities

• Owner of the core data pipelines in mapping, responsible for scaling up data processing flow to meet the rapid data growth at Lyft
• Develop strong subject matter expertise in the systems you manage, setting and managing SLAs for both data pipeline and datasets
• Continuously evolve data models and schemas to meet business and engineering requirements
• Develop tools that support self-service management of data pipelines (ETL), schema evolution, and perform SQL tuning to optimize data processing performance
• Write clean, well-tested, and maintainable code, prioritizing scalability and cost efficiency
• Conduct code reviews to uphold code quality standards and facilitate knowledge sharing
• Participate in on-call rotations to maintain high availability and reliability of data workflows
• Unblock, support and communicate with internal & external partners to achieve results

Experiences

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field
• 4+ years of relevant professional experience
• Strong experience with Spark and with a scripting language (Python, Ruby, Bash)
• Exceptional analytical skills, with an in-depth understanding of data quality issues and the ability to dive into complex datasets to resolve inconsistencies
• Excellent communication skills, with the ability to articulate technical concepts clearly to both technical and non-technical audiences while collaborating effectively across teams
• Experience with disparate database, querying and streaming technologies such as S3, DynamoDB, HDFS, Hive, Presto, Pig, HBase, Parquet, Iceberg, Flink, Spark Streaming, Kafka
• Experience with data quality tools such as Great Expectations, dbt, Monte Carlo, Soda, Collibra
• Strong understanding of SQL Engine, experience with querying geospatial data, and able to conduct advanced performance tuning
• Proficient in at least one of the SQL languages (MySQL, PostgreSQL, SqlServer, Oracle)
• Experience with workflow management tools (Airflow, Oozie, Azkaban, UC4, Prefect)
• Experience with infra tooling (Terraform, Cloud Formation, Docker, Kubernetes, Ansible, Chef, Puppet etc preferably in an AWS context) 
• Experience defining API schemas and developing backend services in a microservices environment

Benefits

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $108,000 - CAD $135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. 

The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection. 

As a Data Engineer on the SCC team, you will have ownership over the data modeling and pipelines that power SCC’s Associate and AI Agent Platform . Your efforts will be critical to the reliability of our pipelines, execution of third party data integrations, accurate reporting of agents performance, and efficiency improvements that can save millions of dollars / year. You will work cross-functionally to bridge Lyft's business goals with data engineering. Your efforts will allow access to business and user behavior insights, using huge amounts of Lyft data to fuel several teams such as Analytics, Data Science, Engineering, and many others. 

Responsibilities:

• Owner of the core data pipeline, responsible for scaling up data processing flow to meet the rapid data growth at Lyft
• Evolve data model and data schema based on business and engineering needs
• Implement systems tracking data quality and consistency
• Develop tools supporting self-service data pipeline management (ETL)
• SQL and MapReduce job tuning to improve data processing performance
• Write well-crafted, well-tested, readable, maintainable code
• Participate in code reviews to ensure code quality and distribute knowledge
• Collaborate cross-functionally with product, engineering, data science, and marketing teams to understand business problems and align on prioritization and solutions

Experience:

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field
• 4+ years of professional experience in data engineering, ideally with large-scale distributed systems.
• Strong skills in Spark, Python (or similar scripting language), and SQL performance tuning
• Experience with AWS, Hadoop/S3, Hive, Presto, Airflow, and related tools
• Solid understanding of ETL processes, workflow orchestration, and data warehousing
• A collaborative mindset—you’re comfortable working across teams to solve real-world problems

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $108,000 - CAD $135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning.

We hire Machine Learning Engineers across both our Consumer and Ads organizations, giving you the opportunity to work on a wide range of high-impact problems across the Reddit ecosystem.

We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, — and who want to help shape the future of discovery, relevance, and monetization at Reddit.

If you love working on complex, real-world ML problems at massive scale, this role is for you.

What You’ll Work On

As a Machine Learning Engineer at Reddit, you will design and build production ML systems that power core experiences across the platform, including:

• Personalized recommendations, search, and ranking systems that help users discover the most relevant content and communities
• Intelligent advertising systems including ranking, bidding, measurement, and optimization
• Content, Advertisers, and User understanding, from building foundational content/user representations to deriving insightful signals
• Large-scale machine learning pipelines, model serving infrastructure, and real-time decision systems
• Applied AI and LLM-driven experiences that improve relevance, discovery, and user engagement

You’ll work on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes.

What You’ll Do

• Design, build, and deploy production-grade machine learning models and systems at scale
• Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
• Build scalable data and model pipelines with strong reliability, observability, and automated retraining
• Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems. 
• Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions
• Improve system performance across latency, throughput, and model quality metrics
• Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment 
• Contribute to technical strategy, architecture, and long-term ML roadmap

Basic Qualifications

• 3-5+ years of experience building, deploying, and operating machine learning systems in production
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals
• ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
• Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
• Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions
• Experience improving measurable metrics through applied machine learning

Preferred Qualifications

• Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems
• Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)
• Experience working with real-time systems and low-latency production environments
• Background in feature engineering, model optimization, and production monitoring
• Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
• Advanced degree in Computer Science, Machine Learning, or related quantitative field

Potential Teams

• Ads Measurement Modeling 
• Ads Targeting and Retrieval
• Advertiser Optimization
• Ads Marketplace Quality
• Ads Creative Effectiveness
• Ads Foundational Representations
• Ads Content Understanding
• Ads Ranking
• Feed Relevance
• Search and Answers Relevance
• ML Understanding
• Notifications Relevance

Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

Who you are:

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a Data Scientist Intern who can help us discover the value out of our rich and large datasets. Our ideal team member will have the mathematical and statistical expertise you’d expect, but a natural curiosity and creative mind that’s not so easy to find. As you mine, interpret and clean our data, we will rely on you to ask questions, connect the dots, and uncover opportunities that lie hidden within—all with the ultimate goal of realizing the data’s full potential. You will join a team of data specialists, but will “slice and dice” data using your own methods, creating new visions for the future. If you love technology and are keen to join an industry leader — we would love to hear from you!

What you'll do:

As a Data Scientist Intern, you will be a crucial part of producing innovative analytics from Geotab’s big data environment. We believe that there is collective value in the massive amount of data arising from all of our sensors across the globe. And most importantly, we believe this data can be used to improve safety, infrastructure, and productivity for our customers. You will work with our team of data scientists and engineers who have curated our data repository and have created a series of near real-time and historical datasets. You will leverage data mining techniques, Machine Learning, Deep Learning, Neural Networks and other Artificial Intelligence and statistical analysis tools to build models and high-quality prediction systems to help our customers make informed and smarter decisions. You will work closely with Data Analysts, Data Engineers, Professional Services, Data Privacy & Governance, firmware as well as other internal departments such as Software Development and Solutions Engineering to achieve these objectives.

The opportunity:

• 8+ month work-term beginning September 2026.
• Full-time, paid internship: Monday - Friday, 37.5hrs/week.
• Your first week at Geotab begins with 'GEO Launch' - a one-week Employee Orientation. Click here to learn more!
• Learn more about the Geotab Campus Program here.
• This posting is for an existing vacancy.

How you'll make an impact:

• The primary objective of this internship is to develop a "Reasoning & Automation" solution that evolves Geotab Ace into a fully integrated, context-aware assistant. Key goals include:
  • Multi-Modal Ingestion: Implement a multi-modal ingestion layer (leveraging technologies like Google's LiveAPI) capable of processing screen content, audio, and text simultaneously to establish real-time user context.
  • Contextual Enrichment: Develop mechanisms to automatically enrich user prompts with environmental state (e.g., current page URL, visible data charts), eliminating the need for users to manually explain their context.
  • Proactive Assistance & Navigation: Extend the reasoning capabilities of the agent to not only answer data queries but also guide users through complex workflows ("Where do I go to...") and offer proactive support.
  • Optimization of RAG & SQL Generation: Continue to refine the underlying RAG architecture and "text-to-SQL" generation for Google BigQuery to ensure the answers provided are accurate, safe, and efficient.

What you'll bring to the role:

We are a Canadian leader in digital automotive solutions. Our flagship brands — AutoTrader.ca, AutoSync, Dealertrack Canada and CMS — help Canadians buy, sell, and finance vehicles with confidence. 

AutoTrader.ca is Canada’s largest automotive marketplace, with over 25 million monthly visits. 

As part of AutoScout24 group, Europe’s largest online car marketplace, we’re shaping the future of automotive retail in Canada and beyond.

What's in it for you:

We understand that there is life at work and life outside of work. Here are a few benefits we all benefit from that support us to be our creative best. 

• Gym discounts 

• Employee and Family Assistance program 

• Virtual wellness events 

• Conferences & training budget 

• Regular internal training programs 

• Financial planning with 3% matching Pension  

• Competitive salary 
• Annual bonus structure 

For a career where you can drive our business and shape your future, apply now. 

Tucows (NASDAQ:TCX, TSX:TC) is possibly the biggest Internet company you’ve never heard of. We started as a simple shareware site in 1993 and have since grown into a stable of businesses: Tucows Domains, Ting Internet and Wavelo.

We embrace a people-first philosophy that is rooted in respect, trust, and flexibility. We believe that whatever works for our employees is what works best for us. It’s also why the majority of our roles are remote-first, meaning you can work from anywhere you can connect to the Internet!

Today, over one thousand people work in over 20 countries to help us make the Internet better. If this sounds exciting to you, join the herd! 

About the Role

We’re looking for a curious, technically strong, and business-minded Data Analyst to join our Data Engineering team. This is a high-impact analytics position for someone who works at the intersection of data engineering, business intelligence, finance analytics, and AI-enabled decision support.

You’ll partner closely with Finance, Operations, Data Engineering, and business leadership to turn complex questions into trusted, actionable insights — with a particular focus on supporting the Finance organization. This role reports to the Data Engineering Manager.

What You’ll Do

• SQL & Business Analysis: Translate ambiguous business questions into precise SQL queries against our Snowflake warehouse. Own and deliver recurring financial reporting, reconciliation, and operational analytics with an eye for improving efficiency over time.
• Diagnostic & Advanced Analytics: Go beyond dashboards to explain why metrics move. Conduct root cause analyses, build forecasting models, and support decision-making through churn analysis, customer segmentation, CLV, A/B testing, and similar techniques.
• Business Intelligence & Self-Service Analytics: Build, maintain, and optimize Looker dashboards, Explores, and LookML definitions that provide governed, reliable, self-service analytics. Govern the semantic layer — metrics, dimensions, and business logic — that powers trusted reporting and AI-enabled analytics. Train business users to leverage these tools confidently.
• Finance Partnership & Compliance: Serve as the primary analytical partner to the Finance team. Own financial reporting, reconciliation, and forecasting workflows. Support SOX audits and ITAC testing, ensuring financial data is accurate, auditable, and well-documented.
• Data Quality & Cross-Functional Collaboration: Monitor and improve data quality across analytical pipelines, especially for finance-critical datasets. Gather requirements from stakeholders, translate business needs into technical specs, and collaborate with Data and Analytics Engineers to resolve upstream issues.

What You Bring

Must Have

• Experience: 3+ years in a Data Analyst, Analytics Engineer, or BI Analyst role in a modern cloud data stack environment.
• SQL mastery: Advanced SQL including CTEs, window functions, joins, and performance-aware querying in Snowflake or a comparable cloud warehouse.
• Looker expertise: Hands-on experience with Looker: dashboard development, Explores, LookML, governed metrics, and enabling self-service analytics for business users.
• dbt proficiency: Ability to read, maintain, test, and document dbt models. Familiarity with sources, tests, macros, lineage, and dbt development workflows.
• Finance domain knowledge: Solid understanding of GL, journal entries, trial balance, financial statements, reconciliations, and period-close processes. SOX/ITAC experience is highly valued.
• Data modeling: Working knowledge of dimensional modeling — fact/dimension tables, star schemas, slowly changing dimensions.
• Statistical & predictive analysis: Applied experience with regression, classification, A/B testing, segmentation, and forecasting using SQL, Python, or similar tools.
• Communication: Ability to explain complex analysis clearly to both Finance leadership and technical peers. Strong written and verbal skills.
• Version control: Comfortable with Git/GitHub: branching, PRs, and collaborative development in a terminal environment.
• AI & LLM analytics: Experience with LLMs or AI-assisted analytics tools (Claude, Gemini, GPT, Looker Conversational Analytics). Exposure to Cortex Analyst, Cortex Search, or AISQL capabilities. Understanding of MCP integration patterns is a plus.

Nice to Have

• Telecom / subscription domain: Experience in telco, connectivity, or subscription environments (MVNOs, ISPs). Familiarity with churn, ARPU, MRR, billing, and provisioning metrics.
• Workflow orchestration: Familiarity with Airflow, Dagster, or Prefect for understanding scheduled pipelines and diagnosing failures.
• Certifications: Looker, Snowflake, dbt, or cloud platform certifications.

​​The base salary range for this position is $79,650 - $88,500. Range shown in $CAD for Canadian residents. Other countries will differ. Range may vary on a number of factors including, but not limited to: location, experience and qualifications. Tucows believes in a total rewards offering that includes fair compensation and generous benefits

Want to know more about what we stand for? At Tucows we care about protecting the open Internet, narrowing the digital divide, and supporting fairness and equality.

We also know that diversity drives innovation. We are committed to inclusion across race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability status. We celebrate multiple approaches and diverse points of view.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

We use AI-enabled tools throughout our recruitment process to help us work more efficiently and consistently. These tools support our hiring teams by organizing and reviewing information, while final hiring decisions are always made by people.

Tucows and its subsidiaries participate in the E-verify program for all US employees.

Learn more about Tucows, our businesses, culture and employee benefits on our site here.

Tucows (NASDAQ:TCX, TSX:TC) is possibly the biggest Internet company you’ve never heard of. We started as a simple shareware site in 1993 and have since grown into a stable of businesses: Tucows Domains, Ting Internet and Wavelo.

We embrace a people-first philosophy that is rooted in respect, trust, and flexibility. We believe that whatever works for our employees is what works best for us. It’s also why the majority of our roles are remote-first, meaning you can work from anywhere you can connect to the Internet!

Today, over one thousand people work in over 20 countries to help us make the Internet better. If this sounds exciting to you, join the herd! 

About the Role

We’re looking for a curious, technically strong, and business-minded Data Analyst to join our Data Engineering team. This is a high-impact analytics position for someone who works at the intersection of data engineering, business intelligence, finance analytics, and AI-enabled decision support.

You’ll partner closely with Finance, Operations, Data Engineering, and business leadership to turn complex questions into trusted, actionable insights — with a particular focus on supporting the Finance organization. This role reports to the Data Engineering Manager.

What You’ll Do

• SQL & Business Analysis: Translate ambiguous business questions into precise SQL queries against our Snowflake warehouse. Own and deliver recurring financial reporting, reconciliation, and operational analytics with an eye for improving efficiency over time.
• Diagnostic & Advanced Analytics: Go beyond dashboards to explain why metrics move. Conduct root cause analyses, build forecasting models, and support decision-making through churn analysis, customer segmentation, CLV, A/B testing, and similar techniques.
• Business Intelligence & Self-Service Analytics: Build, maintain, and optimize Looker dashboards, Explores, and LookML definitions that provide governed, reliable, self-service analytics. Govern the semantic layer — metrics, dimensions, and business logic — that powers trusted reporting and AI-enabled analytics. Train business users to leverage these tools confidently.
• Finance Partnership & Compliance: Serve as the primary analytical partner to the Finance team. Own financial reporting, reconciliation, and forecasting workflows. Support SOX audits and ITAC testing, ensuring financial data is accurate, auditable, and well-documented.
• Data Quality & Cross-Functional Collaboration: Monitor and improve data quality across analytical pipelines, especially for finance-critical datasets. Gather requirements from stakeholders, translate business needs into technical specs, and collaborate with Data and Analytics Engineers to resolve upstream issues.

What You Bring

Must Have

• Experience: 3+ years in a Data Analyst, Analytics Engineer, or BI Analyst role in a modern cloud data stack environment.
• SQL mastery: Advanced SQL including CTEs, window functions, joins, and performance-aware querying in Snowflake or a comparable cloud warehouse.
• Looker expertise: Hands-on experience with Looker: dashboard development, Explores, LookML, governed metrics, and enabling self-service analytics for business users.
• dbt proficiency: Ability to read, maintain, test, and document dbt models. Familiarity with sources, tests, macros, lineage, and dbt development workflows.
• Finance domain knowledge: Solid understanding of GL, journal entries, trial balance, financial statements, reconciliations, and period-close processes. SOX/ITAC experience is highly valued.
• Data modeling: Working knowledge of dimensional modeling — fact/dimension tables, star schemas, slowly changing dimensions.
• Statistical & predictive analysis: Applied experience with regression, classification, A/B testing, segmentation, and forecasting using SQL, Python, or similar tools.
• Communication: Ability to explain complex analysis clearly to both Finance leadership and technical peers. Strong written and verbal skills.
• Version control: Comfortable with Git/GitHub: branching, PRs, and collaborative development in a terminal environment.
• AI & LLM analytics: Experience with LLMs or AI-assisted analytics tools (Claude, Gemini, GPT, Looker Conversational Analytics). Exposure to Cortex Analyst, Cortex Search, or AISQL capabilities. Understanding of MCP integration patterns is a plus.

Nice to Have

• Telecom / subscription domain: Experience in telco, connectivity, or subscription environments (MVNOs, ISPs). Familiarity with churn, ARPU, MRR, billing, and provisioning metrics.
• Workflow orchestration: Familiarity with Airflow, Dagster, or Prefect for understanding scheduled pipelines and diagnosing failures.
• Certifications: Looker, Snowflake, dbt, or cloud platform certifications.

​​The base salary range for this position is $79,650 - $88,500. Range shown in $CAD for Canadian residents. Other countries will differ. Range may vary on a number of factors including, but not limited to: location, experience and qualifications. Tucows believes in a total rewards offering that includes fair compensation and generous benefits

Want to know more about what we stand for? At Tucows we care about protecting the open Internet, narrowing the digital divide, and supporting fairness and equality.

We also know that diversity drives innovation. We are committed to inclusion across race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability status. We celebrate multiple approaches and diverse points of view.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

We use AI-enabled tools throughout our recruitment process to help us work more efficiently and consistently. These tools support our hiring teams by organizing and reviewing information, while final hiring decisions are always made by people.

Tucows and its subsidiaries participate in the E-verify program for all US employees.

Learn more about Tucows, our businesses, culture and employee benefits on our site here.

Tucows (NASDAQ:TCX, TSX:TC) is possibly the biggest Internet company you’ve never heard of. We started as a simple shareware site in 1993 and have since grown into a stable of businesses: Tucows Domains, Ting Internet and Wavelo.

We embrace a people-first philosophy that is rooted in respect, trust, and flexibility. We believe that whatever works for our employees is what works best for us. It’s also why the majority of our roles are remote-first, meaning you can work from anywhere you can connect to the Internet!

Today, over one thousand people work in over 20 countries to help us make the Internet better. If this sounds exciting to you, join the herd! 

About the opportunity

We are seeking an experienced and versatile Analytics Engineer to join our dynamic team. In this role, you will apply your advanced analytics expertise to extract actionable insights from raw data. The ideal candidate will have a strong background in data engineering, analytics, and machine learning, with the ability to drive data-driven decision-making across the organization. 

What You Will Be Doing

• Data Modeling & Pipelines: Design, develop, and maintain complex data models in our Snowflake data warehouse. Utilize dbt (Data Build Tool) to create efficient data pipelines and transformations for our data platform.
  
  
• Snowflake Intelligence Integration: Leverage Snowflake Intelligence features (e.g., Cortex Analyst, Cortex Agents, Cortex Search, AISQL) to implement conversational data queries and AI-driven insights directly within our data environment. Develop AI solutions that harness these capabilities to extract valuable business insights.
  
  
• Advanced SQL & Analysis: Design and build advanced SQL queries to retrieve and manipulate complex data sets. Dive deep into large datasets to uncover patterns, trends, and opportunities that inform strategic decision-making.
  
  
• Business Intelligence (BI): Develop, maintain, and optimize Looker dashboards and LookML to effectively communicate data insights. Leverage Looker’s conversational analytics and data agent features to enable stakeholders to interact with data using natural language queries.
  
  
• Cross-Functional Collaboration: Communicate effectively with stakeholders to understand business requirements and deliver data-driven solutions. Identify opportunities for implementing AI/ML/NLP technologies in collaboration with product, engineering, and business teams.
  
  
• Programming & Automation: Write efficient Python code for data analysis, data processing, and automation of recurring tasks. Skilled in shell scripting and command-line tools to support data workflows and system tasks. Ensure code is well-tested and integrated into automated workflows (e.g., via Airflow job scheduling).
  
  
• Visualization & Presentation: Create compelling visualizations and presentations to deliver analytical insights and actionable recommendations to senior management and cross-functional teams. Tailor communication of complex analyses to diverse audiences.
  
  
• Innovation & Best Practices: Stay up-to-date with industry trends, emerging tools, and best practices in data engineering and analytics (with a focus on dbt features, Snowflake’s latest offerings and BI innovations). Develop and implement innovative ideas to continuously improve our analytics stack and practices.

What You Bring

• Education: Bachelor’s degree in Computer Science, Statistics, or a related field; Master’s degree preferred.
  
  
• Experience: 2+ years of experience in data analytics or a related field, with significant exposure to AI and Machine Learning applications in analytics.
  
  
• SQL Expertise: Advanced SQL skills with experience in writing and optimizing complex queries on large-scale datasets.
  
  
• dbt Proficiency: Hands-on experience with dbt (Data Build Tool) and its features for building, testing, and documenting data models.
  
  
• Data Modeling: Expert-level knowledge of data modeling and data warehouse concepts (e.g., star schema, normalization, slowly changing dimensions).
  
  
• Snowflake & AI Capabilities: Experience with Snowflake’s Data Cloud platform and familiarity with its advanced AI capabilities (Snowflake Intelligence – Cortex Analyst, Cortex Agents, Cortex Search, AISQL, etc.) is highly preferred.
  
  
• Business Intelligence Tools: Strong skills in Looker data visualization and LookML (including familiarity with Looker’s conversational AI and data agent capabilities) or similar BI tools.
  
  
• AI Agents & Automation: Experience with AI agents or generative AI tools to optimize workflows and service delivery (such as creating chatbots or automated analytic assistants) is a plus.
  
  
• Real-Time & Streaming Data: Experience with real-time data processing and streaming technologies (e.g., Kafka, Kinesis, Spark Streaming) for handling continuous data flows.
  
  
• Programming: Proficient in Python for data analysis and manipulation (pandas, NumPy, etc.), with the ability to write clean, efficient code. Experienced with shell scripting and command-line tools for automating workflows and data processing tasks.
  
  
• ETL/Orchestration: Familiarity with ETL processes and workflow orchestration tools like Apache Airflow (or similar scheduling tools) for automating data pipelines alongside Docker for local development and testing.
  
  
• Cloud Platforms: Experience with cloud platforms and services (especially AWS or GCP) for data storage, compute, and deployment.
  
  
• Version Control & CI/CD: Solid understanding of code versioning (Git) and continuous integration/continuous deployment (CI/CD) processes in a data engineering context.
  
  
• Agile Methodology: Familiarity with agile development methodologies and ability to work in a fast-paced, iterative environment.
  
  
• Soft Skills: Excellent communication and presentation skills, with critical thinking and problem-solving abilities. Proven track record of working effectively on cross-functional teams and translating business needs into technical solutions.
  
  
• Data Governance & Ethics: Experience implementing data governance best practices, ensuring data quality and consistency. Knowledge of data ethics, bias mitigation strategies, and data privacy regulations (e.g., GDPR, CCPA) with a commitment to compliance.

Nice to Have

• Community & Open Source: Contributions to open-source projects or active participation in data community initiatives.
  
  
• AI/ML Skills: Experience with applying Artificial Intelligence/Machine Learning techniques in analytics (e.g., building predictive models for forecasting, churn prediction, fraud detection, etc.). Practical experience deploying models and using MLOps/DataOps practices for lifecycle management.
  
  
• Statistical Background: Solid foundation in statistics and probability, with ability to apply various modeling techniques and design A/B tests or experiments.
  
  
• Additional Programming: Knowledge of additional programming or query languages (e.g., R, Scala, Julia, Spark SQL) that can be applied in analytics workflows.
  
  
• Certifications: Certifications in relevant data technologies or cloud platforms (such as Snowflake, AWS, GCP, or Looker) demonstrating your expertise.

The ideal candidate will be a self-starter with a passion for data and analytics, capable of navigating complex datasets to uncover valuable insights. They should be comfortable working in a fast-paced environment, adapting to new technologies, and driving innovation in our data practices.They should be able to navigate complex data landscapes, uncover meaningful insights, and communicate these findings effectively to both technical and non-technical audiences. The ability to stay current with industry trends and continuously learn new technologies is essential in this role.

This role offers the opportunity to make a significant impact on our organization's data strategy and contribute to critical business decisions through advanced analytics. The successful candidate will play a key role in shaping our data culture and driving the adoption of cutting-edge data technologies and methodologies.

If you are a data enthusiast with a track record of delivering impactful analytics solutions and a desire to push the boundaries of what's possible with data, we want to hear from you!

​​The base salary range for this position is $81,990 to $91,100. Range shown in $CAD for Canadian residents. Other countries will differ. Range may vary on a number of factors including, but not limited to: location, experience and qualifications. Tucows believes in a total rewards offering that includes fair compensation and generous benefits. 

Want to know more about what we stand for? At Tucows we care about protecting the open Internet, narrowing the digital divide, and supporting fairness and equality.

We also know that diversity drives innovation. We are committed to inclusion across race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability status. We celebrate multiple approaches and diverse points of view.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

We use AI-enabled tools throughout our recruitment process to help us work more efficiently and consistently. These tools support our hiring teams by organizing and reviewing information, while final hiring decisions are always made by people.

Tucows and its subsidiaries participate in the E-verify program for all US employees.

Learn more about Tucows, our businesses, culture and employee benefits on our site here.

Tucows (NASDAQ:TCX, TSX:TC) is possibly the biggest Internet company you’ve never heard of. We started as a simple shareware site in 1993 and have since grown into a stable of businesses: Tucows Domains, Ting Internet and Wavelo.

We embrace a people-first philosophy that is rooted in respect, trust, and flexibility. We believe that whatever works for our employees is what works best for us. It’s also why the majority of our roles are remote-first, meaning you can work from anywhere you can connect to the Internet!

Today, over one thousand people work in over 20 countries to help us make the Internet better. If this sounds exciting to you, join the herd! 

About the opportunity

We are seeking an experienced and versatile Analytics Engineer to join our dynamic team. In this role, you will apply your advanced analytics expertise to extract actionable insights from raw data. The ideal candidate will have a strong background in data engineering, analytics, and machine learning, with the ability to drive data-driven decision-making across the organization. 

What You Will Be Doing

• Data Modeling & Pipelines: Design, develop, and maintain complex data models in our Snowflake data warehouse. Utilize dbt (Data Build Tool) to create efficient data pipelines and transformations for our data platform.
  
  
• Snowflake Intelligence Integration: Leverage Snowflake Intelligence features (e.g., Cortex Analyst, Cortex Agents, Cortex Search, AISQL) to implement conversational data queries and AI-driven insights directly within our data environment. Develop AI solutions that harness these capabilities to extract valuable business insights.
  
  
• Advanced SQL & Analysis: Design and build advanced SQL queries to retrieve and manipulate complex data sets. Dive deep into large datasets to uncover patterns, trends, and opportunities that inform strategic decision-making.
  
  
• Business Intelligence (BI): Develop, maintain, and optimize Looker dashboards and LookML to effectively communicate data insights. Leverage Looker’s conversational analytics and data agent features to enable stakeholders to interact with data using natural language queries.
  
  
• Cross-Functional Collaboration: Communicate effectively with stakeholders to understand business requirements and deliver data-driven solutions. Identify opportunities for implementing AI/ML/NLP technologies in collaboration with product, engineering, and business teams.
  
  
• Programming & Automation: Write efficient Python code for data analysis, data processing, and automation of recurring tasks. Skilled in shell scripting and command-line tools to support data workflows and system tasks. Ensure code is well-tested and integrated into automated workflows (e.g., via Airflow job scheduling).
  
  
• Visualization & Presentation: Create compelling visualizations and presentations to deliver analytical insights and actionable recommendations to senior management and cross-functional teams. Tailor communication of complex analyses to diverse audiences.
  
  
• Innovation & Best Practices: Stay up-to-date with industry trends, emerging tools, and best practices in data engineering and analytics (with a focus on dbt features, Snowflake’s latest offerings and BI innovations). Develop and implement innovative ideas to continuously improve our analytics stack and practices.

What You Bring

• Education: Bachelor’s degree in Computer Science, Statistics, or a related field; Master’s degree preferred.
  
  
• Experience: 2+ years of experience in data analytics or a related field, with significant exposure to AI and Machine Learning applications in analytics.
  
  
• SQL Expertise: Advanced SQL skills with experience in writing and optimizing complex queries on large-scale datasets.
  
  
• dbt Proficiency: Hands-on experience with dbt (Data Build Tool) and its features for building, testing, and documenting data models.
  
  
• Data Modeling: Expert-level knowledge of data modeling and data warehouse concepts (e.g., star schema, normalization, slowly changing dimensions).
  
  
• Snowflake & AI Capabilities: Experience with Snowflake’s Data Cloud platform and familiarity with its advanced AI capabilities (Snowflake Intelligence – Cortex Analyst, Cortex Agents, Cortex Search, AISQL, etc.) is highly preferred.
  
  
• Business Intelligence Tools: Strong skills in Looker data visualization and LookML (including familiarity with Looker’s conversational AI and data agent capabilities) or similar BI tools.
  
  
• AI Agents & Automation: Experience with AI agents or generative AI tools to optimize workflows and service delivery (such as creating chatbots or automated analytic assistants) is a plus.
  
  
• Real-Time & Streaming Data: Experience with real-time data processing and streaming technologies (e.g., Kafka, Kinesis, Spark Streaming) for handling continuous data flows.
  
  
• Programming: Proficient in Python for data analysis and manipulation (pandas, NumPy, etc.), with the ability to write clean, efficient code. Experienced with shell scripting and command-line tools for automating workflows and data processing tasks.
  
  
• ETL/Orchestration: Familiarity with ETL processes and workflow orchestration tools like Apache Airflow (or similar scheduling tools) for automating data pipelines alongside Docker for local development and testing.
  
  
• Cloud Platforms: Experience with cloud platforms and services (especially AWS or GCP) for data storage, compute, and deployment.
  
  
• Version Control & CI/CD: Solid understanding of code versioning (Git) and continuous integration/continuous deployment (CI/CD) processes in a data engineering context.
  
  
• Agile Methodology: Familiarity with agile development methodologies and ability to work in a fast-paced, iterative environment.
  
  
• Soft Skills: Excellent communication and presentation skills, with critical thinking and problem-solving abilities. Proven track record of working effectively on cross-functional teams and translating business needs into technical solutions.
  
  
• Data Governance & Ethics: Experience implementing data governance best practices, ensuring data quality and consistency. Knowledge of data ethics, bias mitigation strategies, and data privacy regulations (e.g., GDPR, CCPA) with a commitment to compliance.

Nice to Have

• Community & Open Source: Contributions to open-source projects or active participation in data community initiatives.
  
  
• AI/ML Skills: Experience with applying Artificial Intelligence/Machine Learning techniques in analytics (e.g., building predictive models for forecasting, churn prediction, fraud detection, etc.). Practical experience deploying models and using MLOps/DataOps practices for lifecycle management.
  
  
• Statistical Background: Solid foundation in statistics and probability, with ability to apply various modeling techniques and design A/B tests or experiments.
  
  
• Additional Programming: Knowledge of additional programming or query languages (e.g., R, Scala, Julia, Spark SQL) that can be applied in analytics workflows.
  
  
• Certifications: Certifications in relevant data technologies or cloud platforms (such as Snowflake, AWS, GCP, or Looker) demonstrating your expertise.

The ideal candidate will be a self-starter with a passion for data and analytics, capable of navigating complex datasets to uncover valuable insights. They should be comfortable working in a fast-paced environment, adapting to new technologies, and driving innovation in our data practices.They should be able to navigate complex data landscapes, uncover meaningful insights, and communicate these findings effectively to both technical and non-technical audiences. The ability to stay current with industry trends and continuously learn new technologies is essential in this role.

This role offers the opportunity to make a significant impact on our organization's data strategy and contribute to critical business decisions through advanced analytics. The successful candidate will play a key role in shaping our data culture and driving the adoption of cutting-edge data technologies and methodologies.

If you are a data enthusiast with a track record of delivering impactful analytics solutions and a desire to push the boundaries of what's possible with data, we want to hear from you!

​​The base salary range for this position is $81,990 to $91,100. Range shown in $CAD for Canadian residents. Other countries will differ. Range may vary on a number of factors including, but not limited to: location, experience and qualifications. Tucows believes in a total rewards offering that includes fair compensation and generous benefits. 

Want to know more about what we stand for? At Tucows we care about protecting the open Internet, narrowing the digital divide, and supporting fairness and equality.

We also know that diversity drives innovation. We are committed to inclusion across race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability status. We celebrate multiple approaches and diverse points of view.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

We use AI-enabled tools throughout our recruitment process to help us work more efficiently and consistently. These tools support our hiring teams by organizing and reviewing information, while final hiring decisions are always made by people.

Tucows and its subsidiaries participate in the E-verify program for all US employees.

Learn more about Tucows, our businesses, culture and employee benefits on our site here.

Who you are:

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a DataOps Developer Intern who will immediately contribute to the organization and the Data and Analytics team efforts. If you love technology, and are keen to join an industry leader - we would love to hear from you!

What you'll do:

As a DataOps Developer Intern your key area of responsibility will be dedicating time to building out enterprise services, applications and tools to support Geotab’s data operation solution. The DataOps Developer Intern will be responsible for contributing to the development of data pipelines using Python and Airflow. You will need to work closely with both the Data Engineering and Data Science teams.

The opportunity:

• 12 month work-term beginning September 2026.
• Full-time, paid internship: Monday - Friday, 37.5hrs/week.
• Your first week at Geotab begins with 'GEO Launch' - a one-week Employee Orientation. Click here to learn more!
• Learn more about the Geotab Campus Program here.
• This posting is for an existing vacancy.

How you'll make an impact:

• Perform software development and support of Airflow pipelines in Python, using SQL
• Understand the performance impact of the code, API use, and scalability.
• Actively participate in design discussions and document requirements and technical design.
• Perform quality assurance analysis by ensuring coding standards are followed, looking for logic errors, and other bugs.
• Analyze log files and performance data to troubleshoot and identify root cause of bugs.

What you'll bring to the role:

About Clutch

Clutch is Canada’s largest online used car retailer, delivering a seamless, hassle-free car-buying experience to drivers everywhere. Customers can browse hundreds of cars from the comfort of their home, get the right one delivered to their door, and enjoy peace of mind with our 10-Day Money-Back Guarantee.
Named one of Canada’s Top Growing Companies two years in a row and awarded a spot on LinkedIn’s Top Canadian Startups list, we’re looking to add curious, hard-working, and driven individuals to our growing team.
Headquartered in Toronto, Clutch was founded in 2017. Clutch is backed by world-class investors including Canaan, BrandProject, Real Ventures, D1 Capital, and Upper90. To learn more, visit clutch.ca

About the role

Clutch is seeking a Senior Analytics Engineer to design, maintain, and evolve our analytics data stack. In this role, you will work closely with business teams, own critical data domains, and mentor other analytics engineers while driving data quality, governance, and modern analytics practices.

What you’ll do

• Lead the design and evolution of Clutch’s analytics data stack, including scalable ELT pipelines, dimensional models, and semantic layers using dbt, Snowflake, and Airflow

• Partner with Marketing, Product, Operations, and Finance teams to define metrics frameworks, improve reporting fidelity, and build trusted data products

• Drive best practices in data engineering: testing, observability, CI/CD, and code review for production-ready analytics code

• Own critical data domains end-to-end, ensuring models and pipelines are reliable, performant, and trusted

• Mentor and guide other analytics engineers, elevating technical practices and modeling approach

• Shape Clutch’s data governance strategy by contributing to documentation, standards, and overall data culture

• Experiment with emerging tooling in orchestration, data quality, and self-serve analytics to keep Clutch at the forefront of modern data practices

What we’re looking for

• 6–8+ years of experience in analytics engineering, data analytics, or data engineering in high-growth or fast-paced environments

• Proven track record building and maintaining complex data models with dbt in a production environment (CI, testing, documentation)

• Deep SQL expertise, with experience designing scalable dimensional models and working with large, normalized datasets

• Hands-on experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift

• Experience building semantic layers and enabling self-serve BI (Looker, Sigma, Tableau)

• Strong communication skills with the ability to influence stakeholders and lead discussions around metrics and data strategy

• Strong sense of ownership, accountability, and craftsmanship for data quality, performance, and maintainability

• Experience with Python and orchestration tools (Airflow, Prefect, Dagster) for data pipelines and transformations is a plus

• Prior mentorship or leadership experience in a data team is strongly preferred

Why you’ll love it at Clutch

• Autonomy & ownership – create your own path and own your work

• Competitive compensation and equity incentives

• Generous time off program

• Health & dental benefits

Employment Type

Existing, Permanent Full-Time

Compensation & Benefits

• Salary: $120,000 - $160,000 annually 

• Equity incentives

• Comprehensive healthcare, dental, vision, prescriptions, life insurance, and dependent coverage

• Pay decisions reflect skills, performance, market benchmarking, responsibilities, and working conditions

Clutch is committed to fostering an inclusive workplace where all individuals have an opportunity to succeed. If you require accommodation at any stage of the interview process, please email talent@clutch.ca

The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science solutions. We provide the shared components and services that enable our teams to develop, deploy, and scale robust ML models. This includes a performant data API, configurable featurization, reliable forecasting systems, highly parallel optimization engines, and scalable training pipelines, and deep experimentation capabilities. As our product suite and customer base grow, so does the scale and complexity of what our platform needs to support, gracefully accommodating predictions and simulations across various time scales (hours, days, weeks), complex data hierarchies (pallets on a truck, shelves of mangos in a store, chunks of fruit in a bowl), and endless configuration possibilities (average shelf fullness, backroom loads, truck capacities).

About the Role

As a Staff ML Platform Engineer on the ML Platform Engineering team, you will be instrumental in elevating our core ML platform to its next level of performance, reliability, and scalability. You'll work on the critical infrastructure that directly enables all of Afresh's Machine Learning and Applied Science teams to innovate faster and deliver impact. Your contributions will empower our product suite, including our flagship Prediction Engine, to power replenishment decisions on more than 15% of all produce sold in the United States.

What You'll Do:

• In your first 3 months, you'll partner with ML, Applied Science, and engineering leadership to identify the highest-leverage gaps in our ML platform and shape a multi-quarter technical strategy to close them.
• By the end of your first 6 months, you will have driven a platform-level initiative that meaningfully changes what's possible at Afresh — establishing the architecture for real-time inference across the company, redesigning model configuration and deployment end-to-end, or rebuilding our distributed inference layer for an order-of-magnitude growth in scale — while raising the bar across the ML org through design reviews and mentorship of other senior engineers.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 7+ years of professional software development experience with a proven track record of shipping high-quality applications and services.
• Experience working collaboratively with machine learning engineers, data scientists, or applied scientists on large-scale software projects involving machine learning models.
• You possess a genuine curiosity about ML modeling (e.g., demand forecasting, state estimation, ordering policy). You aren't just building "pipes"; you want to understand what is flowing through them. 
• You have a deep understanding of how scientists work and build tools that bridge the gap between a research notebook and production-grade software. 
• Technical leadership experience and a demonstrated ability to mentor junior engineers.
• Deep expertise in library design, API design, data structures, and algorithms.
• Strong familiarity with the Python ecosystem (NumPy, Pandas, Torch, PySpark). While our stack is Python-heavy, we value engineers who are stack-agnostic and focus on solving fundamental distributed systems problems. 
• Proven ability to architect high-throughput distributed inference systems using Spark, Dask, or Ray. 
• Experience in engineering robust data architectures with a focus on schema evolution and performance tuning. 
• Experience with end-to-end orchestration (lineage tracking, resource scheduling, self-healing pipelines).

Tech Stack: Our backend is pure Python (NumPy, Pandas, Torch, PySpark, Cython, orchestrated in Airflow). We use Databricks as our data warehouse. While we'd like you to have very good familiarity with Python, many of our problems are stack-agnostic.

This position is not eligible for company sponsorship.

Salary Band in Canada (CAD): $148,000 - $242,000

The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science solutions. We provide the shared components and services that enable our teams to develop, deploy, and scale robust ML models. This includes a performant data API, configurable featurization, reliable forecasting systems, highly parallel optimization engines, and scalable training pipelines, and deep experimentation capabilities. As our product suite and customer base grow, so does the scale and complexity of what our platform needs to support, gracefully accommodating predictions and simulations across various time scales (hours, days, weeks), complex data hierarchies (pallets on a truck, shelves of mangos in a store, chunks of fruit in a bowl), and endless configuration possibilities (average shelf fullness, backroom loads, truck capacities).

About the Role

As a Senior ML Platform Engineer on the ML Platform Engineering team, you will be instrumental in elevating our core ML platform to its next level of performance, reliability, and scalability. You'll work on the critical infrastructure that directly enables all of Afresh's Machine Learning and Applied Science teams to innovate faster and deliver impact. Your contributions will empower our product suite, including our flagship Prediction Engine, to power replenishment decisions on more than 15% of all produce sold in the United States.

What you will do:

• In your first 3 months, you might deliver a project that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems.
• By the end of your first 6 months, you will have owned the design and implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 5+ years of professional software development experience with a proven track record of shipping high-quality applications and services.
• Experience working collaboratively with machine learning engineers, data scientists, or applied scientists on large-scale software projects involving machine learning models.
• You possess a genuine curiosity about ML modeling (e.g., demand forecasting, state estimation, ordering policy). You aren't just building "pipes"; you want to understand what is flowing through them. 
• You have a deep understanding of how scientists work and build tools that bridge the gap between a research notebook and production-grade software. 
• Technical leadership experience and a demonstrated ability to mentor junior engineers.
• Deep expertise in library design, API design, data structures, and algorithms.
• Strong familiarity with the Python ecosystem (NumPy, Pandas, Torch, PySpark). While our stack is Python-heavy, we value engineers who are stack-agnostic and focus on solving fundamental distributed systems problems. 
• Proven ability to architect high-throughput distributed inference systems using Spark, Dask, or Ray. 
• Experience in engineering robust data architectures with a focus on schema evolution and performance tuning. 
• Experience with end-to-end orchestration (lineage tracking, resource scheduling, self-healing pipelines).

Tech Stack: Our backend is pure Python (NumPy, Pandas, Torch, PySpark, Cython, orchestrated in Airflow). We use Databricks as our data warehouse. While we'd like you to have very good familiarity with Python, many of our problems are stack-agnostic.

This position is not eligible for company sponsorship.

Salary Range for Canada in CAD: $137,000 - $205,000

About SecurityScorecard:

SecurityScorecard is the global leader in cybersecurity ratings, with over 12 million companies continuously rated, operating in 64 countries. Founded in 2013 by security and risk experts Dr. Alex Yampolskiy and Sam Kassoumeh and funded by world-class investors, SecurityScorecard’s patented rating technology is used by over 25,000 organizations for self-monitoring, third-party risk management, board reporting, and cyber insurance underwriting; making all organizations more resilient by allowing them to easily find and fix cybersecurity risks across their digital footprint. 

Headquartered in New York City, our culture has been recognized by Inc Magazine as a "Best Workplace,” by Crain’s NY as a "Best Places to Work in NYC," and as one of the 10 hottest SaaS startups in New York for two years in a row. Most recently, SecurityScorecard was named to Fast Company’s annual list of the World’s Most Innovative Companies for 2023 and to the Achievers 50 Most Engaged Workplaces in 2023 award recognizing “forward-thinking employers for their unwavering commitment to employee engagement.”  SecurityScorecard is proud to be funded by world-class investors including Silver Lake Waterman, Moody’s, Sequoia Capital, GV and Riverwood Capital.

About the Team:

Be part of an exciting and growing Data Engineering team at the core of everything we do. SecurityScorecard's ratings platform processes enormous volumes of data — and the reliability, performance, and integrity of our data pipelines directly determines the quality of our product. Our team operates with a strong emphasis on collaboration, shared ownership, and engineering excellence. You will be hands-on across the full software development life cycle, working alongside a global team that values both technical depth and clear communication. Check out our tech blog and let us know if you're our next star.

https://techblog.securityscorecard.com/

About the Role:

We are looking for a Staff Engineer to join our Data Engineering team. This is a senior individual contributor role with real technical leadership scope — you will serve as a technical anchor for the team, raising the bar on engineering quality, mentoring engineers at earlier career stages, and owning complex, high-impact systems end-to-end. Our data infrastructure is Scala and Spark-first, processing data at significant scale to power security ratings for over 2 million companies worldwide. If you thrive in environments where you own hard problems, make independent architectural decisions, and can operate with minimal handholding, this role was built for you.

This role is open to candidates based in Canada (Eastern time zone preferred) or LATAM.

What You Will Do:

• Serve as a technical lead and de facto anchor for the data engineering team — setting the standard for code quality, system design, and engineering judgment
• Own complex, ambiguous data engineering problems end-to-end, from architecture through deployment and ongoing reliability
• Mentor and level up junior and mid-level engineers through code reviews, design discussions, and direct feedback
• Partner with Product Managers and cross-functional stakeholders to translate business requirements into scalable, maintainable technical solutions
• Drive architectural decisions across infrastructure, application, and data tiers — and be accountable for their outcomes
• Identify and lead initiatives to improve system performance, reliability, and engineering efficiency across the team
• Participate in all stages of the software development life cycle, from design and development through deployment and maintenance
• Implement comprehensive testing and performance tuning to ensure system stability and uptime

Responsibilities:

• Define technical approaches for large-scope projects, estimate effort, and help unblock teammates at all levels
• Write clean, maintainable, production-grade code that sets the example for readability, design patterns, reusability, and testing
• Own end-to-end systems across infrastructure, application, and data tiers — not just individual components
• Lead code reviews with a focus on raising team standards, not just catching bugs
• Continuously monitor performance metrics across owned systems and proactively drive improvements and refactors
• Contribute to a culture of documentation, knowledge sharing, and engineering rigor

Our Tech Stack:

• Scala, Apache Spark, Python
• Airflow
• ScyllaDB, PostgreSQL, ClickHouse, Hive, Redis, Kafka, Kafka Connect
• Docker, Jenkins, Terraform, AWS

Technical Skills:

• 8+ years of experience in the Data Engineering space, building and maintaining large-scale data pipelines
• Deep proficiency in Scala — our data platform is Scala-first and this is the primary language for this role
• Expert-level experience with Apache Spark and distributed data processing at scale
• Strong system design and architecture skills — you can reason about trade-offs, design for reliability, and communicate your decisions clearly
• Experience designing, building, and maintaining ETL pipelines and data infrastructure in production environments
• Comfort with high-throughput, high-volume systems (TB-scale data processing)
• Familiarity with strongly-typed and functional programming principles
• Track record of mentoring engineers and elevating team technical quality
• Proficiency with version control tools, particularly Git

Nice To Have:

• Experience with a variety of data formats (structured/unstructured, flat files, XML, JSON, relational, Parquet)
• Experience in the cybersecurity domain or working on security-adjacent data products
• Prior experience in a Staff or Principal IC role with cross-team technical influence

Benefits:

The estimated total compensation range for this position is $140,000 - $160,000 CAD (base plus bonus). Actual compensation for the position is based on a variety of factors, including, but not limited to affordability, skills, qualifications and experience, and may vary from the range. In addition to base salary, employees may also be eligible for annual performance-based incentive compensation awards and equity, among other company benefits.

SecurityScorecard is committed to Equal Employment Opportunity and embraces diversity. We believe that our team is strengthened through hiring and retaining employees with diverse backgrounds, skill sets, ideas, and perspectives. We make hiring decisions based on merit and do not discriminate based on race, color, religion, national origin, sex or gender (including pregnancy) gender identity or expression (including transgender status), sexual orientation, age, marital, veteran, disability status or any other protected category in accordance with applicable law. 

We also consider qualified applicants regardless of criminal histories, in accordance with applicable law. We are committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or accommodation due to a disability, please contact talentacquisitionoperations@securityscorecard.io.

Any information you submit to SecurityScorecard as part of your application will be processed in accordance with the Company’s privacy policy and applicable law. 

SecurityScorecard does not accept unsolicited resumes from employment agencies.  Please note that we do not provide immigration sponsorship for this position.   #LI-DNI

Mercury is redefining *banking for ambitious companies and behind every great financial platform is a data system that people can actually trust.

As Mercury scales, our revenue systems generate an enormous amount of information: signals from remote and in-person meetings, automation tools, product usage, lifecycle events, and analytics pipelines. Turning that activity into clear, reliable intelligence — without brittle pipelines or constant rework — is critical to how we grow.

We’re looking for a Data Strategy & Operations leader to own the data foundations that power revenue execution. This role ensures that revenue data is reliable, interpretable, scalable, and usable as the business evolves and that teams can act on what they see with confidence.

In this role, you will report to the Head of Platforms & Infrastructure and play a central role in shaping how Mercury models, governs, and operationalizes GTM data. You’ll partner closely with Data Engineering, Data Science, Solution Architecture, Platform Engineering. etc.

*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.

Here are some things you’ll do on the job:

• Own the definition, structure, and reliability of data originating from revenue platforms (e.g., Salesforce, GTM tools, automation systems)
• Serve as the primary decision owner for GTM-sourced tables and views used for revenue execution, forecasting inputs, lifecycle tracking, and signal-based workflows
• Design and evolve core GTM data models across Salesforce, ETL, and analytics layers
• Partner with Data Engineering to align GTM schemas with enterprise data models and define clear data contracts between source systems and downstream consumers
• Partner with Data Science / Analytics to ensure revenue data is interpretable, statistically sound, and reflects how the business actually operates
• Own clarity around data ownership boundaries, shared dependencies, and escalation paths when upstream or downstream changes impact revenue integrity
• Define and uphold data quality, freshness, consistency, and documentation standards for revenue platforms
• Monitor and improve pipeline reliability, performance, and scalability, proactively identifying fragile or redundant transformations
• Identify opportunities to automate manual or error-prone data workflows and reduce operational overhead
• Act as a data thought partner to Platforms & Infrastructure, Revenue Operations, Analytics, and Security — advising on feasibility, tradeoffs, and sequencing for data-heavy initiatives

You Should:

• Have 7+ years of experience in data engineering or data systems roles within SaaS or technology companies
• Have deep experience designing and operating production data pipelines
• Be highly proficient in SQL and experienced in data modeling
• Have hands-on experience with modern data stacks (e.g., Snowflake, BigQuery, Redshift)
• Have experience with ETL / ELT tooling (e.g., dbt, Airflow, Census, or similar)
• Understand Salesforce data models and common GTM system architectures
• Be able to translate business concepts into durable, well-structured data models
• Communicate clearly with both technical and non-technical partners

Preferred:

• Experience supporting revenue, sales, or customer lifecycle data
• Familiarity with event-based data platforms (e.g., Data Cloud or equivalents)
• Experience working alongside platform engineering and security teams
• Exposure to data governance, access controls, and compliance considerations
• Experience mentoring or guiding other data practitioners

Compensation:

The total rewards package at Mercury includes base salary, equity, and benefits.

Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.

Our target new hire base salary ranges for this role are the following:

• US employees in New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $158,400 - $198,000
• US employees outside of New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $142,600 - $178,200
• Canadian employees (any location): $149,700 - 187,100 CAD

Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.

We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024.

[Please see the independent bias audit report covering our use of Covey for more information.] 

#LI-SN1

Opportunité 

À Valtech, vous trouverez un environnement propice à l’apprentissage continu, à l’impact concret et à la croissance professionnelle. Que vous développiez de nouvelles solutions digitales, remettiez en question les idées reçues ou construisiez la prochaine génération d’expériences client, votre travail contribuera à transformer les secteurs d'activité. 

Nous sommes fiers de: 

• Notre travail et l'innovation que nous encourageons 
• Nos valeurs : « share, dare, care » 
• Une culture d'entreprise qui favorise la créativité, la diversité et l'autonomie 
• Notre structure mondiale sans frontières, qui permet une collaboration fluide 

La maîtrise de l'anglais est requise, car le rôle implique des communications régulières avec des clients et des collègues situés à l'extérieur du Québec.

Rôle 

Veuillez noter que nous acceptons uniquement les candidatures provenant des provinces de l’Ontario, de la Colombie-Britannique (Vancouver) et du Québec pour ce poste. Pour les candidats basés au Québec, une maîtrise de l’anglais est nécessaire, car le poste implique une collaboration avec des équipes situées dans le reste des Amériques et occasionnellement en Europe.

Nous recherchons un(e) Architecte de données principal(e) (niveau Principal) possédant une solide expertise en architecture de données d’entreprise, en habilitation de l’IA et en paradigmes de développement cloud-native. Ce rôle va au-delà de l’ingénierie pratique : il requiert un(e) leader capable de définir la stratégie de plateforme, la posture de sécurité, d’influencer des clients de haut niveau et d’accompagner des équipes sur plusieurs programmes menés en parallèle.

En plus de solides compétences sur Microsoft Fabric et Azure, le/la candidat(e) idéal(e) possède une expérience sur AWS ou GCP, ainsi qu’avec Databricks, et une compréhension globale des architectures de données modernes. Il/elle doit être à l’aise pour soutenir les démarches commerciales, porter la vision architecturale et garantir l’intégrité technique des solutions tout au long de leur mise en œuvre.

Afin de gérer efficacement ce projet et son écosystème, le/la candidat(e) idéal(e) démontre une expertise pratique avérée en SQL, Python, systèmes distribués, gouvernance des données et architecture cloud. Il/elle doit être à l’aise pour accompagner les clients, cadrer et résoudre les écarts architecturaux afin de répondre aux exigences des solutions, et définir une stratégie à long terme en matière de données et d’intelligence artificielle.

Responsabilités du poste

Architecture et stratégie

• Concevoir et piloter des architectures de données complexes à l’échelle de l’entreprise sur MS Fabric, Azure, GCP, AWS ou dans des environnements Databricks (serverless ou hébergés).
• Définir et faire respecter les standards architecturaux, les modèles et les cadres de gouvernance couvrant l’ingestion, la modélisation, la traçabilité (lineage), la sécurité et l’orchestration.
• Structurer des approches architecturales intégrant l’IA, incluant les fondations de données pour le ML, le feature engineering et des pipelines d’opérationnalisation à faible latence.
• Agir en tant que conseiller(ère) principal(e) auprès des responsables techniques côté client afin de définir la stratégie à long terme, les feuilles de route et les initiatives de modernisation.
• Diriger l’orientation architecturale lors des phases d’avant-vente, incluant la conception de solutions, le cadrage, l’estimation et les présentations auprès des instances dirigeantes.
• Anticiper les impacts en aval des décisions architecturales ; conserver la responsabilité lorsque les équipes de delivery ou certaines contraintes nécessitent des écarts par rapport à la conception initiale.

Leadership en ingénierie des données

• Concevoir des pipelines de données hautement disponibles, distribués et tolérants aux pannes, supportant des charges de travail batch et streaming.
• Superviser la migration et l’intégration de sources de données complexes et hétérogènes vers des plateformes Fabric, Azure, GCP ou Databricks.
• Définir des modèles de type medallion/lakehouse (zones Bronze/Silver/Gold) ou leurs équivalents cloud.
• Établir des standards d’entreprise pour les couches ingestion → transformation → exposition (serving) dans des environnements multi-cloud.
• Optimiser les performances des traitements de données à grande échelle sur Spark, Databricks et les moteurs natifs de Fabric.
• Assurer le leadership sur 2 à 3 projets simultanés avec une allocation variable, en garantissant la cohérence architecturale et la qualité de livraison, tout en contribuant à des travaux techniques approfondis lorsque nécessaire.

Exécution technique et contribution pratique

• Développer des transformations avancées, des pipelines et des frameworks en utilisant SQL, Python, dbt et les outils cloud-native de Fabric/Databricks.
• Mettre en œuvre l’automatisation via des API REST, Data Factory, des services d’orchestration et des outils de workflows multi-cloud.
• Encadrer les ingénieur(e)s sur l’architecture distribuée, la conception des métadonnées, les cadres de qualité des données et les bonnes pratiques de gestion de plateforme.
• Réaliser des analyses de causes racines sur des problématiques complexes de plateforme ou de performance, et mettre en place des remédiations durables et évolutives.

Collaboration et influence

• Agir en tant que principale autorité technique lors des revues d’architecture, de la conception de solutions et de la planification transversale.
• Conduire des échanges stratégiques avec des parties prenantes de niveau direction et exécutif.
• Traduire des architectures complexes en impacts métiers clairs pour des audiences non techniques.
• Encadrer les équipes d’ingénierie, les architectes techniques, les chefs de projet et les architectes juniors afin de renforcer les capacités organisationnelles.
• Représenter l’architecture lors des échanges d’avant-vente, des présentations de capacités et des cycles de propositions.

Qualifications requises 

Afin d’être éligible à ce poste, vous devez posséder les qualifications essentielles suivantes: 

• Plus de 10 ans d’expérience en ingénierie des données, architecture de données ou ingénierie de plateformes.
• Expérience dans la conception ou la mise en place de plateformes de données d’entreprise sur au moins l’un des environnements suivants : Azure, GCP ou AWS, ainsi que Databricks.
• Expertise approfondie en SQL, Python, traitement distribué des données et conception de données cloud-native.
• Expérience significative avec les modèles d’architecture medallion/lakehouse.
• Solide connaissance des plateformes de données modernes : Databricks, Azure Synapse, Microsoft Fabric, Delta Lake, BigQuery, etc.
• Expérience avérée dans la direction architecturale de grands programmes et de plusieurs projets menés en parallèle.
• Expérience en automatisation et intégration d’entreprise via des API REST.
• Excellentes compétences en communication et capacité à interagir avec assurance auprès de la direction et des clients.
• Expérience en avant-vente, en conception de solutions techniques ou en leadership architectural orienté client.

Qualifications souhaitées

• Maîtrise des outils de reporting Tableau, Power BI et Microsoft Fabric.
• Expérience dans la conception d’architectures de données intégrant l’IA et de pipelines de données pour le machine learning.
• Expérience démontrée dans la définition et la mise en œuvre des meilleures pratiques DevOps.
• Expérience avec Spark, Hive, Impala et autres frameworks Big Data.
• Connaissance des composants d’architectures multi-cloud (Azure, GCP, AWS).
• Expérience avec des outils d’automatisation de pipelines tels qu’Airflow, Azure Data Factory et Prefect.
• Expérience dans la direction d’équipes techniques et la gestion de projets data / analytics / machine learning.
• Maîtrise des stacks applicatives front-end et back-end ainsi que de la conception d’API (REST/GraphQL).

Si vous ne possédez pas toutes les qualifications mentionnées ou si vous avez des lacunes dans votre parcours, nous vous encourageons à postuler. À Valtech, nous reconnaissons la richesse des talents et valorisons la pluralité des perspectives ainsi que la soif d’apprendre. 

Engagement pour l’inclusion 

Nous concevons des expériences qui conviennent à toutes les personnes — et cela commence par nos équipes. À Valtech, nous avons à cœur de bâtir une culture inclusive où chaque individu se sent soutenu pour grandir, s'épanouir et atteindre ses objectifs. Quel que soit votre parcours, vous avez votre place. Explorez notre site Diversité et Inclusion pour en savoir plus sur nos actions en faveur d’un Valtech plus équitable. 

Bénéfices et avantages 

Il s’agit d’un poste en temps plein basé à Québec, Canada.

Valtech offre un programme d’avantages sociaux complet, en vigueur après trois mois de service continu :

• Un régime d’assurance complet, avec la possibilité de choisir le module qui répond le mieux à vos besoins — Or, Argent ou Bronze. Selon le module sélectionné, l’employeur peut contribuer jusqu’à 80 % de votre couverture. Ce régime comprend des assurances invalidité de courte et de longue durée.
• Dialogue via Sun Life vous donne accès à des services de soins de santé virtuels, vous permettant de consulter un professionnel de la santé pour des urgences, des renouvellements d’ordonnance, et plus encore. Vous avez également accès au Programme d’aide aux employés et à leur famille, ainsi qu’à un programme complet de soutien en santé mentale.
• Un Compte de dépenses personnel de $500, pouvant être utilisé pour des remboursements de soins de santé, des abonnements au gym, des passes de transport en commun, des fournitures de bureau ou des contributions à votre REER par l’intermédiaire de Valtech.
• Un régime de retraite dans lequel Valtech égalera 100% de vos contributions à votre REER par l’entremise d’un Régime de participation différée aux bénéfices (RPDB), jusqu’à un maximum de 4 %. Vous pouvez commencer à cotiser à votre REER immédiatement, et au RPDB après 3 mois. L’acquisition des droits du RPDB est complétée après 24 mois de service.
• L’accès à un programme de vacances flexibles selon la politique de Valtech pour soutenir votre équilibre travail-vie personnelle, comprenant 5 jours disponibles pendant votre période de probation et un montant au prorata pour le reste de l’année.
• Un remboursement de technologie personnelle — $30/mois offert à chaque employé dès le premier jour.
• Nous fermons durant les vacances d’hiver et offrons des horaires flexibles tout au long de l’année, afin que vous puissiez profiter des après-midi ensoleillées du vendredi — pourvu que vos heures hebdomadaires soient complétées.

Processus de recrutement 

Une fois votre candidature envoyée, notre équipe Talent Acquisition l’examinera sous quelques jours. Si vos compétences et votre expérience correspondent au poste, nous vous contacterons pour les prochaines étapes. 

⚠️  Attention aux fraudes : n’interagissez qu’avec des adresses e-mail officielles se terminant par @valtech.com. 

Nous nous engageons en faveur de l’inclusion et de l’accessibilité. Si vous avez besoin d’un aménagement raisonnable pendant le processus de recrutement, veuillez l’indiquer dans votre candidature ou en informer votre Talent Partner. 

À propos de Valtech 

Valtech est une entreprise d’innovation et d’expérience qui a pour vocation d’offrir une meilleure façon d'appréhender le monde. En combinant expertises, secteurs et cultures, nous aidons les marques à créer de la valeur dans un monde de plus en plus digital. 

À l’intersection des données, de l’IA, de la créativité et de la technologie, nous accompagnons la transformation de grandes entreprises telles que L’Oréal, Mars, Audi, P&G, Volkswagen Dolby, et bien d’autres. 

À Valtech, la transformation ne se limite pas aux discours. Nous la concrétisons. Nos collaborateurs sont au cœur de notre succès, et nous cultivons un environnement où chaque talent peut s’épanouir, évoluer et innover. 

Prêt·e à imaginer la suite ? Rejoignez-nous. 

Conformément à la loi allemande sur l'égalité de traitement et à son équivalent français, les offres d'emploi doivent être publiées de manière non sexiste. Nous utilisons la mention pour préciser que ce poste est ouvert à tous les genres dans ces pays, même si le titre du poste lui-même peut être traduit ou interprété comme « masculin » en allemand ou en français. 

On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve.

What you’ll do

- You will develop and iterate on fraud prediction models using a mix of approaches for tabular and behavioral data

- You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.

- You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.

- You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.

- You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.

- You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.

What we look for

- You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field.

- Strong Python skills and experience writing production-quality code.

- Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar).

- Experience with a deep learning framework (PyTorch preferred).

- Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar).

- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).

- Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.

- You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.

- You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.

- Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.

- You have strong verbal and written communication skills that support effective collaboration with our global engineering team.

Pay Grade - L
Equity Grade - 5

Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. 

Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company).

CAN base pay range per year: $125,000 - $175,000

#LI Remote

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are focused on delivering meaningful results. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

Robinhood’s Analytics Engineering team, part of the Data Science organization, builds the data foundation that supports decision-making across the company. The team develops data products that support product development, regulatory requirements, and business operations. You will work closely with product managers, engineers, and data scientists to transform raw data into reliable, usable datasets.

As an Analytics Engineer, you will design and build ETL pipelines, data models, and analytics tools that support key business decisions. You will contribute to defining metrics, improving dataset quality, and establishing standards for analytics development. This role offers the opportunity to influence how teams access and use data across Robinhood while supporting systems that operate at scale!

This role is based in our Toronto, ON office, with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

What you’ll do

• Work with product, engineering, and data science teams to define metrics and deliver analytics solutions, from data ingestion to reporting
• Design and maintain scalable ETL pipelines and data models using tools such as Airflow and Spark
• Build analytics products that support business, product, and regulatory reporting needs
• Develop internal tools and frameworks to improve data accessibility and usability across teams
• Partner with data scientists to turn raw data into insights that inform product and business decisions
• Promote consistent analytics practices and support teams in using data effectively

What you bring 

• You have 3+ years of experience in analytics engineering, data engineering, data science, or a related field
• You have strong experience with SQL, Python, and Apache Spark for data processing
• You have experience building and maintaining ETL pipelines using tools such as Airflow or similar
• You have experience creating dashboards using tools such as Superset, Looker, or Tableau
• You are familiar with version control systems like Git and modern development workflows
• You can work effectively with partners across engineering, product, and data teams

What we offer

• Challenging, high-impact work to grow your career
• Performance driven compensation with multipliers for outsized impact, bonus programs, and equity ownership
• Top tier benefits to fuel your work, including supplemental health insurance, ancillary insurance, and mental health support programs
• Lifestyle wallet - a highly flexible employer-paid benefits spending account expenses beyond traditional benefits such as wellness, childcare, learning, and more.
• Time off to recharge including company holidays, paid time off, sick time, paid volunteer time off, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.
• Monthly commuter stipend to help offset in-office commuting costs

Our team is committed to providing an inclusive and welcoming interview experience for all candidates. If you require a specific accommodation during the application or interview process due to a physical or mental condition, please complete this Applicant Accommodation Form to notify our team. The form should only be completed if you need a specific accommodation.

AI Usage Disclosure: Robinhood uses artificial intelligence (AI) tools to support parts of our recruiting process. These tools enhance the efficiency and consistency of our hiring process; however, all hiring decisions are made by our hiring teams.

Vacancy Notice: This job posting represents an existing vacancy that we are actively seeking to fill.

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold  thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Customer Intelligence and AI (CIAI) team is a specialized Data Science group within Robinhood’s Corporate Strategy organization. Our mission is to unlock deeper insights into customer behaviors, satisfaction, and market performance through Net Promoter Score (NPS), benchmarking, and advanced analytics. We apply data science, machine learning, and AI to improve customer experiences, drive operational efficiency, and inform company-wide strategy.

We partner with teams across Robinhood—including Product, Data, Engineering, Marketing, Finance, and Research. Our work powers strategic decision-making, enhances operational performance, and integrates into data science and AI pipelines across the company.

As a Sr. Analytics Engineer on the CIAI team, you will strengthen and expand Robinhood’s Corporate Strategy and benchmarking capabilities. This includes designing and improving measurement frameworks, conducting deep-dive analyses, and shaping how Robinhood tracks and improves customer experience relative to competitors. You’ll contribute to both recurring programs and new technical projects that advance our ability to measure company performance and improve decision-making efficiently.

This role is based in our Menlo Park, CA office, with in-person attendance expected at least [3] days per week. 

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

What you’ll do

• Partner cross-functionally with product, engineering, and business teams to scope and deliver high-impact analytics initiatives, from metric definitions to fully automated reporting solutions.
• Design and maintain reliable, scalable pipelines and data models using modern data tools (e.g., Airflow, Spark), ensuring performance and accuracy at scale.
• Lead end-to-end development of analytics products—from ingestion to visualization—that meet critically important business, product, and regulatory needs.
  Build internal frameworks and tooling to make high-quality data more accessible and actionable across the organization.
• Support competitive benchmarking efforts, providing technical expertise for competitive intelligence and contextualizing Robinhood’s performance relative to peers.
• Advocate for customers by helping establish best practices in how we measure, interpret, and act on customer feedback.

What you bring

• 5+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field.
• Strong expertise in advanced SQL, Python scripting, and Apache Spark (PySpark, Spark SQL) for data processing and transformation.
• Proficiency in building, maintaining, and optimizing ETL pipelines, using modern tools like Airflow or similar.
• Experience in building polished and performant dashboards using tools like Superset, Looker, Tableau.
• Strong familiarity with version control (GitHub), CI/CD, and modern development workflows.
• A strong business acumen around business strategy and competitive landscape.
• Ability to work in a fast-paced, and highly multi-functional environment.

What we offer

• Challenging, high-impact work to grow your career
• Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
• Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
• Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.

About the team + role 

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Data Engineering team builds and maintains the foundational datasets that power decision-making across Robinhood. We design reliable, scalable data systems that support product analytics, growth strategy, financial reporting, experimentation, and machine learning. The team partners closely with Product, Engineering, Data Science, and Finance to ensure accurate, well-modeled data is available to teams across the company. Our work directly influences how Robinhood measures performance, improves customer experience, and scales its products.

As a Senior Data Engineer, you will design, build, and evolve core datasets that track product performance and company-wide metrics. You will develop scalable data pipelines that ingest application events and database snapshots into our data lake, ensuring high data quality and reliability. You’ll collaborate with application engineers to improve data generation patterns and with analytics teams to design intuitive, well-documented data models. This is an opportunity to shape the technical foundation that supports data-informed decisions across the organization!

This role is based in our Menlo Park, CA office, with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

What you’ll do

• Help define and build key datasets across all Robinhood product areas. Lead the evolution of these datasets as use cases grow.
• Build scalable data pipelines using Python, Spark and Airflow to move data from different applications into our data lake.
• Partner with upstream engineering teams to enhance data generation patterns.
• Partner with data consumers across Robinhood to understand consumption patterns and design intuitive data models.
• Ideate and contribute to shared data engineering tooling and standards.
• Define and promote data engineering best practices across the company.

What you bring

• 5+ years of professional experience building end-to-end data pipelines
• Hands-on software engineering experience, with the ability to write production-level code in Python for user-facing applications, services, or systems (not just data scripting or automation).
• Expert at building and maintaining large-scale data pipelines using open source frameworks (Spark, Flink, etc)
• Strong SQL (Presto, Spark SQL, etc) skills.
• Experience solving problems across the data stack (Data Infrastructure, Analytics and Visualization platforms)
• Expert collaborator with the ability to democratize data through actionable insights and solutions.

What we offer

• Challenging, high-impact work to grow your career
• Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
• Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
• Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.

We are TRADER, a Canadian leader in digital automotive solutions. Our flagship brands - AutoTrader.ca, AutoSync, Dealertrack Canada and CMS - help Canadians buy, sell, and finance vehicles with confidence. 

AutoTrader.ca is Canada’s largest automotive marketplace, with over 25 million monthly visits.Through AutoSync, we provide software solutions to 3,500+ dealers, streamlining their operations, marketing,and sales. Dealertrack Canada is the country’s top automotive financing portal, processing more than 6.5 million credit applications each year. Collateral Management(CMS) is anational tech solution that boosts lien and registration services,recovery services, and insolvency management solutions for Canadian Lenders. 

Learn more at tradercorporation.com. 

ABOUT THE ROLE 

 
As a Senior Analytics Engineer, you will own and shape the analytical data architecture and modelling that powers enterprise reporting, self-service analytics, and AI-driven insights. You will define modeling standards, semantic layers, and data foundations across the organisation and act as a technical leader for reporting, analytics and AI use cases. You will work closely with data platform, data governance, and analytics teams across a modern cloud data stack. This position is permanent and based in Toronto, Canada. 

WHAT YOU’LL DO 

• define and evolve enterprise analytical data models and architecture using AWS technology  

• build and own semantic layers to align KPIs and business logic 

• lead AI-driven analytics use cases such as text-to-SQL and conversational BI 

• Set modeling standards, documentation, and best practices across teams 

• Collaborate with data platform engineering , data governance and the business stakeholders on scalable pipelines 

• Contribute to data modeling best practices  

YOU COME WITH 

• 7+ years in analytics engineering or data engineering roles 

• Advanced SQL skills and strong experience with data modeling 

• Strong experience with AWS, Glue, Athena, S3, and cloud analytics 

• Knowledge of orchestration tools (Airflow, Prefect, Dagster) 

• strong Python and PySpark skills for large-scale data processing 

• experience with AI-driven analytics and semantic modeling 

WHY TRADER 

We support your growth, well-being, and long-term success. Our benefits are designed to help you perform at your best - both at work and in your personal life. Benefits from Day One:  

• Gym membership discounts 

• Employee and Family Assistance Program 

• Virtual wellness sessions 

• Budget for conferences and external training 

• Regular internal learning programs 

• Financial Well-Being 

• 3% employer pension matching 

• Comprehensive income-protection options 

• Compensation 

• Competitive base salary 

• Annual performance bonus 
• 3% CPP matching 

The base salary range for this position is CAD $90k- $140k

This range reflects the expected compensation at the time of posting. The final offer may vary and can be higher based on relevant skills, experience, location, and market conditions. Based on the role the total rewards package may also include benefits, bonus, and other employee offerings.

As part of AutoScout24 group, Europe’s largest online car marketplace, we’re shaping the future of automotive retail in Canada and beyond. 

Narvar is growing! We’re building the data infrastructure behind the post-purchase experiences of hundreds of millions of consumers. When a shopper tracks a package, initiates a return, or gets a delivery notification through one of our 1,500+ brand partners, our data systems are doing the work behind the scenes. We’re looking for a Senior Data Engineer to own and evolve the pipelines, platforms, and data products that power Narvar’s analytics, ML, and merchant-facing products.

You’ll work across the full stack of our data infrastructure, from ingestion and transformation to the analytics surfaces our merchants use every day. You’ll make architectural decisions, ship production systems at scale, and we expect you to work in an AI-native way, using agentic coding tools to increase your leverage and ship faster.

Day-to-day

• Design, build, and operate data pipelines that process terabytes of transactional data daily using Airflow/Composer and BigQuery
• Own end-to-end data models and transformations that power merchant analytics, operational reporting, and ML features
• Build and maintain embedded analytics infrastructure — the data products our merchants interact with directly
• Evolve our data platform on GCP, including BigQuery, Cloud SQL, AlloyDB, and CDC datastreams
• Improve data quality and reliability through testing, observability, alerting, and validation frameworks
• Own data lineage, metadata, and documentation, and help prepare our data layer for agentic and LLM-powered use cases with semantic clarity and standardized metric definitions
• Collaborate cross-functionally with product, ML, and GTM teams, and contribute to technical direction through design docs and architecture decisions

What We’re Looking For

We care about judgment and ownership over credentials.

You’re likely a strong fit if you:

• Have 5–8 years of experience building and operating production data systems
• Have strong SQL skills and are proficient in Python, with flexibility to pick up other languages as needed. Comfortable building and maintaining APIs.
• Have worked with modern data stacks on cloud platforms (GCP preferred, but AWS or Azure transfers well), including cloud data warehouses like BigQuery, ELT patterns, and orchestration with Airflow
• Understand data modeling deeply — dimensional modeling, slowly changing dimensions, incremental processing
• Treat data quality, lineage, and observability as first-class engineering concerns
• Communicate clearly with technical and non-technical stakeholders and are comfortable working cross-functionally
• Already use AI and agentic coding tools as a core part of how you work for planning, code generation, debugging, and code review.

Signals That You’ll Thrive Here

These aren’t hard requirements, but strong indicators:

• You’ve worked in startup or high-ownership environments where you wore multiple hats
• You’ve built or maintained embedded analytics or multi-tenant data products
• You’re excited about making data accessible to AI systems, whether that’s through better metadata, semantic layers, or preparing datasets for agentic workflows
• You care about cost optimization and data governance

Why Data Engineering at Narvar?

Because post-purchase is one of the most data-rich and underserved problem spaces in e-commerce.

We process 10+ billion consumer interactions across 1,500+ retailers, 38 countries, and 55 languages. That data spans tracking, returns, claims, notifications, and customer care. Most data engineering roles have you building pipelines for internal dashboards. Here, the data products you build are the product — merchants make decisions from the analytics we surface, and our agentic AI (Navi) reasons over the data layer you help shape. We’re at an inflection point, investing in embedded merchant analytics and LLM-readiness. You’ll get startup-level ownership with platform-level impact, on a team that values thinking, judgment, and responsibility.

Why Narvar?

We're on a mission to simplify the everyday lives of consumers. Post-purchase is a critical phase of the customer journey. That's why we created Narvar - a platform focused on driving customer loyalty through seamless post-purchase experiences that allow retailers to retain, engage, and delight customers. If you've ever bought something online, there's a good chance you've used our platform!

From the hottest new direct-to-consumer companies to retail’s most renowned brands, Narvar works with GameStop, Neiman Marcus, Sonos, Nike, and 1300+ other brands. With hubs in San Francisco, Atlanta, London, and Bangalore, we've served over 125 million consumers worldwide across 10+ billion interactions, 38 countries, and 55 languages.

Pioneering the post-purchase movement means navigating into the unknown. Our team thrives on this sense of adventure while nurturing a mindset of innovation. We're a home for big hearts and we leave our egos at the door. We work hard but we always make time to celebrate professional wins, baby showers, birthday parties, and everything in between.

We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

#LI-BB1

#LI-Remote

As Manager of Data Infrastructure, reporting to the Director of Data Engineering, you will lead 5-6 engineers operating critical data platforms that power our AI and analytics strategy. This newly created hands-on leadership role combines technical depth with people management, requiring you to mentor your team, drive agile delivery, and apply product thinking to infrastructure. You'll execute on our data strategy while ensuring operational excellence for platforms processing billions in monthly transaction data.

We work Flexible First. This role can be performed remotely anywhere within the provinces of Ontario and British Columbia, Canada. Quarterly travel to the United States is required. We’d love for you to join us!

The Impact You'll Have

• Lead 5-6 engineers through hands-on technical mentoring, performance management, and career development while fostering operational excellence.
• Own data lake infrastructure operations (S3, Airflow, Kafka/MSK, Spark/Glue) maintaining 99.5% uptime and managing on-call rotation.
• Drive agile delivery with sprint planning, capacity planning, and requirements analysis to ship infrastructure improvements quarterly.
• Apply product thinking by gathering customer requirements from data teams, prioritizing platform features, and measuring adoption success.
• Execute technical roadmaps that deliver on data strategy, translating vision into concrete project plans with measurable outcomes.
• Partner with security/compliance on governance controls and work with vendors on technical integrations and platform optimization.

Who You Are

• 6+ years in data engineering/infrastructure with 2+ years leading technical teams at scale.
• Deep hands-on expertise with AWS data services (S3, EMR/Glue), Airflow, Kafka/MSK, and data catalog tools.
• Strong product thinking with experience translating customer requirements into platform capabilities.
• Proven agile delivery skills including capacity planning, sprint execution, and requirements analysis.
• Track record of operational excellence: on-call management, incident response, and achieving SLAs.
• Excellent mentoring skills with passion for developing engineers and building high-performing teams.
• Must be authorized to work in Canada with ability to travel quarterly to the United States.

Nice-To-Haves

• Experience with payments or the FinTech space.

Typical Process

• Application Submission
• Recruiter video call
• Hiring manager video call
• Virtual “Onsite” consisting of four 45-60 min video calls
• Offer!

This position is for an existing vacancy.

Compensation and Benefits

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office. To support Flex First, we calibrate pay to a competitive value according to working location. 

When determining salaries, we consider several factors including, but not limited to, skills, prior experience, and work location. The new-hire base salary range for this position, reflected in CAD,  is: 156,800 - 196,000.

We also believe in recognizing the contributions of our people. That's why we award annual bonuses to eligible employees, rewarding both individual performance and the success of the entire company.

Along with monetary compensation, Marqeta offers

• Multiple health insurance options
• Flexible vacation time
• Retirement savings program with company contribution
• Equity in a publicly-traded company 
• Monthly stipend to support our remote work model
• Annual “development dollars” to support our people growth and development
• Family-forming benefits and up to 20 weeks of Parental Leave

As Manager of Data Infrastructure, reporting to the Director of Data Engineering, you will lead 5-6 engineers operating critical data platforms that power our AI and analytics strategy. This newly created hands-on leadership role combines technical depth with people management, requiring you to mentor your team, drive agile delivery, and apply product thinking to infrastructure. You'll execute on our data strategy while ensuring operational excellence for platforms processing billions in monthly transaction data.

We work Flexible First. This role can be performed remotely in the United States, only in one of our National locations, which you can review here.

The Impact You'll Have

• Lead 5-6 engineers through hands-on technical mentoring, performance management, and career development while fostering operational excellence.
• Own data lake infrastructure operations (S3, Airflow, Kafka/MSK, Spark/Glue) maintaining 99.5% uptime and managing on-call rotation.
• Drive agile delivery with sprint planning, capacity planning, and requirements analysis to ship infrastructure improvements quarterly.
• Apply product thinking by gathering customer requirements from data teams, prioritizing platform features, and measuring adoption success.
• Execute technical roadmaps that deliver on data strategy, translating vision into concrete project plans with measurable outcomes.
• Partner with security/compliance on governance controls and work with vendors on technical integrations and platform optimization.

Who You Are

• 6+ years in data engineering/infrastructure with 2+ years leading technical teams at scale.
• Deep hands-on expertise with AWS data services (S3, EMR/Glue), Airflow, Kafka/MSK, and data catalog tools.
• Strong product thinking with experience translating customer requirements into platform capabilities.
• Proven agile delivery skills including capacity planning, sprint execution, and requirements analysis.
• Track record of operational excellence: on-call management, incident response, and achieving SLAs.
• Excellent mentoring skills with passion for developing engineers and building high-performing teams.

Nice-To-Haves

• Experience with payments or the FinTech space.

Typical Process

• Application Submission
• Recruiter video call
• Hiring manager video call
• Virtual “Onsite” consisting of four 45-60 min video calls
• Offer!

Compensation and Benefits

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office. To support Flex First, we calibrate pay to a competitive value according to working location. Compensation is aligned according to three tiers within the United States:

• National: A baseline tier that applies to most of the geographic territory of the United States.
• Premium: Slightly elevated from the National tier, and oriented toward a narrower set of higher cost-of-living areas, such as Los Angeles CA and Seattle WA
• Premium Plus: A tier for the most expensive working areas, like the San Francisco Bay area and New York City.

Visit this page or consult with a Recruiter to determine which tier would be applicable to you.

When determining salaries, we consider several factors including, but not limited to, skills, prior experience, and work location. The new-hire base salary range for this position is:

• National: $174,700 - $218,400

We also believe in recognizing the contributions of our people. That's why we award annual bonuses to eligible employees, rewarding both individual performance and the success of the entire company.

Along with monetary compensation, Marqeta offers

• Multiple health insurance options
• Flexible time off – take what you need
• Retirement savings program with company contribution and after tax contributions
• Equity in a publicly-traded company and an Employee Stock Purchase Program
• Family-forming benefits, fertility support, and up to 20 weeks of Parental Leave
• Free therapy sessions, financial and professional coaching, and legal advice
• Monthly stipend to support our remote work model
• Annual “development dollars” to support our people growth and development
• Through Flex First, the freedom to live and work wherever you and your family thrive

As Manager of Data Infrastructure, reporting to the Director of Data Engineering, you will lead 5-6 engineers operating critical data platforms that power our AI and analytics strategy. This newly created hands-on leadership role combines technical depth with people management, requiring you to mentor your team, drive agile delivery, and apply product thinking to infrastructure. You'll execute on our data strategy while ensuring operational excellence for platforms processing billions in monthly transaction data.

We work Flexible First. This role can be performed remotely anywhere within the provinces of Ontario and British Columbia, Canada. Quarterly travel to the United States is required. We’d love for you to join us!

The Impact You'll Have

• Lead 5-6 engineers through hands-on technical mentoring, performance management, and career development while fostering operational excellence.
• Own data lake infrastructure operations (S3, Airflow, Kafka/MSK, Spark/Glue) maintaining 99.5% uptime and managing on-call rotation.
• Drive agile delivery with sprint planning, capacity planning, and requirements analysis to ship infrastructure improvements quarterly.
• Apply product thinking by gathering customer requirements from data teams, prioritizing platform features, and measuring adoption success.
• Execute technical roadmaps that deliver on data strategy, translating vision into concrete project plans with measurable outcomes.
• Partner with security/compliance on governance controls and work with vendors on technical integrations and platform optimization.

Who You Are

• 6+ years in data engineering/infrastructure with 2+ years leading technical teams at scale.
• Deep hands-on expertise with AWS data services (S3, EMR/Glue), Airflow, Kafka/MSK, and data catalog tools.
• Strong product thinking with experience translating customer requirements into platform capabilities.
• Proven agile delivery skills including capacity planning, sprint execution, and requirements analysis.
• Track record of operational excellence: on-call management, incident response, and achieving SLAs.
• Excellent mentoring skills with passion for developing engineers and building high-performing teams.
• Must be authorized to work in Canada with ability to travel quarterly to the United States.

Nice-To-Haves

• Experience with payments or the FinTech space.

Typical Process

• Application Submission
• Recruiter video call
• Hiring manager video call
• Virtual “Onsite” consisting of four 45-60 min video calls
• Offer!

This position is for an existing vacancy.

Compensation and Benefits

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office. To support Flex First, we calibrate pay to a competitive value according to working location. 

When determining salaries, we consider several factors including, but not limited to, skills, prior experience, and work location. The new-hire base salary range for this position, reflected in CAD,  is: 156,800 - 196,000.

We also believe in recognizing the contributions of our people. That's why we award annual bonuses to eligible employees, rewarding both individual performance and the success of the entire company.

Along with monetary compensation, Marqeta offers

• Multiple health insurance options
• Flexible vacation time
• Retirement savings program with company contribution
• Equity in a publicly-traded company 
• Monthly stipend to support our remote work model
• Annual “development dollars” to support our people growth and development
• Family-forming benefits and up to 20 weeks of Parental Leave

ANALYTICS

What We Do

Our Data & Analytics teams build powerful stories and visuals that inform the games we make, the technology we develop, and business decisions that drive Epic.

What You'll Do

We are seeking a Senior Data Scientist to independently own and drive analytics initiatives for Family Experience (FamXP), Investigation analytics and Trust and Safety, with a focus on impacting team and product outcomes. You'll apply your expertise to transform ambiguous business questions into actionable insights while handling highly sensitive data with care and discretion. This role requires leading cross-functional projects, building strong stakeholder relationships across multiple teams, and maintaining the highest standards of data privacy and security. The role is expected to build foundational re-usable data models as well as end reporting and ad hoc analysis to support teams from FamXP, a product team, to Legal.

In this role, you will

• Own and deliver end-to-end analytics projects for Family Experience and Trust & Safety that drive team outcomes and measurably influence product/org outcomes, from problem framing through impact measurement; can range from telemetry implementation to reporting to automation (from Workato to ML)
• Solve ambiguous Family Experience and Trust & Safety problems by designing analyses, evaluating options/tradeoffs, and making independent decisions that keep work moving in a dynamic environment
• Execute team and product/org strategy through analytics planning: translate strategy into metrics, roadmaps/backlogs, and clear success criteria; contribute to setting team goals
• Build and maintain production-quality metrics, dashboards, and data models that monitor safety, parental controls, and age-appropriate experiences across Epic’s ecosystem
• Drive alignment around approach and outcomes with Trust & Safety, Product, Programming, Legal, and Policy partners across the team and organization, managing expectations and trade-offs
• Handle sensitive data related to minors and safety incidents with discretion, applying privacy/security best practices and Epic data governance requirements
• Mentor others on the team and within the product/org: share best practices, review work, and raise the bar for how analytics is done

What we're looking for

• 6-10 years of demonstrated ability to own and deliver analytics projects that execute team outcomes and influence product/org outcomes
• Strong SQL skills (Python, PySpark a plus) and experience working with large-scale datasets; comfort in modern data environments (e.g., Snowflake/Databricks/Spark); familiarity with version control and workflow orchestration tools (Airflow or similar) is a plus
• Proven ability to solve ambiguous problems and make independent decisions on team projects with minimal/no oversight
• Experience turning messy data into production-quality metrics, dashboards, and data models that support operational monitoring and decision-making
• Strong judgment handling sensitive or regulated data; working knowledge of privacy/security practices and relevant compliance concepts
• Ability to execute team + product/org strategy via planning and prioritization; can translate goals into measurable outcomes and help set team goals
• Track record of driving alignment and collaboration across immediate/adjacent teams and the broader product/org
• Clear communicator who can align stakeholders through concise updates and documentation that drive decisions and action
• Evidence of mentoring others on the team/product org (coaching, reviews, raising standards)
• Domain strength in Trust & Safety and/or family experiences online (e.g., moderation, account safety, community health, fraud, or safety feature measurement)

EPIC JOB + EPIC BENEFITS = EPIC LIFE

We pay 100% for benefits for both employees and dependents and offer coverage for supplemental medical, dental, vision, critical illness, telemedicine, Life and AD&D, long term disability insurance as well as weekly indemnity (short term disability) and a retirement savings plan with a competitive employer match. In addition to the EAP (employee assistance program), we also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents.

Job Summary: 

We are seeking a highly skilled and self-motivated Senior Embedded DevOps Engineer to support our engineering  teams. This role will focus on driving changes and ensuring adherence to company-established standards for data infrastructure and CI/CD pipelines. The ideal candidate will have strong experience working with AWS and/or GCP,  cloud-based data streaming and processing services, containerized application deployments, infrastructure automation, and Site Reliability Engineering (SRE) best practices for performance and cost optimization.

What You'll do:

• Drive initiatives to implement and enforce best practices for data streaming, processing, analytics and monitoring infrastructure.
• Deploy and manage services on Kubernetes-based platforms such as Amazon EKS and Google Kubernetes Engine (GKE).
• Provision and manage cloud infrastructure using Terraform, ensuring best practices in security, scalability, and cost-efficiency.
• Maintain and optimize CI/CD pipelines using Jenkins, ArgoCD, and GitHub Enterprise Actions to support automated deployments and testing.
• Work with cloud-native data services such as AWS Kinesis, AWS Glue, Google Dataflow, and Google Pub/Sub, BigQuery, BigTable
• Familiarity with  workflow orchestration services such as Apache Airflow and Google Cloud Composer.
• Develop and maintain automation scripts and tooling using Python to support DevOps processes.
• Monitor system performance, troubleshoot issues, and implement proactive solutions to enhance reliability and efficiency.
• Implement SRE practices to improve service reliability, scalability, and cost-effectiveness.
• Analyze and optimize cloud costs, identifying areas for improvement and implementing cost-saving strategies.
• Ensure compliance with security policies and best practices in cloud environments.
• Drive adoption of company standards and influence data teams to align with best DevOps and SRE practices.
• Collaborate with cross-functional teams to improve development workflows and infrastructure.

What you bring:

• 7+ years of experience in a DevOps, Site Reliability Engineering, or Cloud Infrastructure role.
• Strong experience with AWS and GCP data services, including Kinesis, Glue, Pub/Sub, and Dataflow.
• Proficiency in deploying and managing workloads on Kubernetes (EKS/GKE) in production environments.
• Hands-on experience with Infrastructure-as-Code (IaC) using Terraform.
• Expertise in CI/CD pipeline management using Jenkins, ArgoCD, and GitHub Enterprise Actions.
• Programming skills in Python for automation and scripting.
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or CloudWatch).
• Strong understanding of SRE principles, including performance monitoring, incident response, and reliability engineering.
• Experience with cost optimization strategies for cloud infrastructure.
• Self-motivated and driven, with a strong ability to influence and drive changes across multiple teams.
• Ability to work collaboratively in an agile environment and support multiple teams. 

Preffered Qualifications:

• Experience with data lake architectures and big data processing frameworks (e.g., Apache Spark, Flink, Snowflake, BigQuery).
• Familiarity with event-driven architectures and message queues (e.g., Kafka, RabbitMQ).
• Experience with workflow orchestration tools such as Apache Airflow and Google Cloud Composer.
• Knowledge of service mesh technologies like Istio.
• Experience with GitOps workflows and Kubernetes-native tooling.

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About the Role

We are looking for a highly skilled Senior Data Engineer to become part of our core Data & AI Engineering team. In this pivotal role, you will be responsible for designing and expanding enterprise-level data infrastructure that enables ZoomInfo's internal teams to interact with data comprehensively—extracting, exploring, analyzing, and generating insights—through various platforms using ZI's internal chat agent

The ideal candidate has a strong background in big data processing, pipeline orchestration, and data modeling, with a proven track record of delivering scalable and high-quality data solutions in fast-paced, data-centric product environments. Given the dynamic nature of emerging technologies, this role requires an individual who excels at exploration and embraces continuous learning as core responsibilities. You'll constantly research and implement innovative solutions while integrating vast, diverse data sources into our AI applications, including our industry-leading LLM-powered systems

What you’ll do:

• Design, develop, and maintain high-performance, product-centric data pipelines using Airflow, DBT, and Python.
• Architect and optimize the massive-scale data warehouse and lakehouse that serves as our single source of truth for all customer data, primarily using Snowflake.
• Lead the integration of diverse structured and unstructured data sources (e.g., web data, third-party APIs) into our data ecosystem, ensuring high-quality and reliable ingestion.
• Implement and enforce Model Context Protocol (MCP) or similar architectures to feed accurate and contextual data into our LLM-powered products for applications like Retrieval Augmented Generation (RAG) and advanced search.
• Collaborate with ML engineers, data scientists, and product managers to translate business needs into scalable data solutions that directly enhance customer value.
• Define, monitor, and enforce data quality SLAs across all pipelines and products, ensuring data accuracy and lineage are a top priority.
• Mentor and coach junior engineers, promoting best practices in code quality, data architecture, and operational excellence.
• Participate in architectural decisions and long-term strategy planning for our enterprise-wide data infrastructure, with a focus on cost, performance, and reliability.

What you bring:

• Expert-level SQL for building performant, scalable queries and transformations on massive datasets.
• Strong Python programming skills with a focus on distributed computing, data manipulation, and building robust APIs.
• Production-level experience for large-scale batch and streaming data processing.
• Hands-on experience with DBT (Data Build Tool) for advanced data modeling and transformations in a modern data stack.
• Deep knowledge of Snowflake data warehouse design, optimization, and cost modeling.
• Experience implementing Model Context Protocol (MCP) or similar architectures to feed structured and unstructured data into LLM-powered systems.
• Strong understanding of data architecture concepts, including data lakes, event-driven architectures (e.g., Kafka), ETL/ELT, and data mesh.
• Proficiency with cloud platforms (GCP and/or AWS) and infrastructure as code (e.g., Terraform).

Nice to Have

• Familiarity with LLMOps, LangChain, or RAG (Retrieval Augmented Generation) pipelines.
• Experience with building embedding models or pipelines for Named Entity Recognition (NER).
• Knowledge of data cataloging tools (e.g., OpenLIneage, etc.) and lineage tracking.
• Familiarity with other distributed systems and databases (e.g., DynamoDB, Flink).

Required Non-Technical Skills

• Excellent communication skills – ability to explain complex technical concepts to both engineering teams and non-technical stakeholders.
• Strategic & Product-Oriented Thinking – can translate business objectives and customer needs into scalable, high-impact data solutions.
• Leadership & Mentorship – experience guiding and uplifting engineering teams to achieve their full potential.
• Stakeholder Management – able to collaborate effectively across departments (Product, Engineering, Sales, Compliance).
• Agility & Adaptability – thrives in ambiguous, evolving environments and can rapidly prototype and iterate on solutions.
• Strong documentation habits and ability to evangelize best practices across the organization.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 8+ years of progressive experience in data engineering, with a track record of leadership and impact.

Demonstrated experience in implementing or scaling data infrastructure for a data-centric product company.

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