About the Role

This is the first dedicated data hire on Together AI's Finance team. You will own the data layer that Finance runs on — building from scratch the models, pipelines, and reporting infrastructure that allow Strategic Finance, FP&A, and Accounting teams to get reliable answers quickly. The person in this role will have direct exposure to every part of the Finance organization and a real opportunity to shape how data-driven decision-making develops here as the company scales.

A significant portion of your work will touch the data behind the economics of Together's infrastructure, which sits behind nearly every financial question we ask. You will work closely with Together's Data and Commerce engineering team, which owns the underlying billing pipelines and data warehouse. Your job is to define and build the modeling and reporting layer that turns raw operational data into finance-grade datasets — aligning on data contracts, representing Finance's requirements in data design decisions, and ensuring the metrics Finance depends on are correct, documented, and trusted.

Responsibilities

• Own and evolve the dbt transformation layer for Finance: design, build, test, document, and maintain models covering billing, financial performance, compute unit economics, and operational metrics
• Author and maintain Airflow DAGs (Astronomer-managed) that orchestrate Finance dbt runs, data quality checks, and downstream dependencies reliably
• Deliver dashboards and reporting in Hex for the executive team covering financial performance, utilization, and key operating metrics
• Partner with Strategic Finance, FP&A, and Accounting teams on the data infrastructure behind forecasting, cost modeling, and other financial analyses
• Set data quality standards across Finance data products and own incident response when Finance-critical pipelines break
• Build the data foundation – clean, well-structured, documented, and reliably maintained – that enables Finance team to self-serve data analysis and supports AI automation and agentic workflows 

Requirements

• 5+ years in an analytics engineering or data engineering role, with meaningful time supporting Finance or business operations teams
• Expert SQL and production-grade dbt experience — models, tests, docs, snapshots, incremental strategies; you've designed a dbt project from scratch and maintained it
• Hands-on experience with Snowflake and Airflow in production; familiarity with Astronomer and the Cosmos dbt integration is a strong plus
• Solid dimensional modeling fundamentals: star schemas, slowly changing dimensions, fact table grain — you can apply Kimball principles without needing to look them up
• Strong dashboarding skills with a track record of executive-facing reporting that people actually use; experience with Hex or a comparable notebook/BI tool
• Clear communicator who can align stakeholders on metric definitions and translate between engineering tradeoffs and Finance requirements in both directions
• High comfort with ambiguity; this role will require scoping problems from scratch, not executing against pre-defined specs

Nice to Have

• Experience with financial data or billing data such as ARR, usage-based billing, invoice reconciliation, revenue recognition patterns, etc.. We operate a usage-based billing system and this context transfers directly.
• Experience with PII handling, data masking, access-tier modeling, or compliance work (SOC 2, ISO 27001, GDPR, CCPA).
• Familiarity with lakehouse patterns (Iceberg, Delta, Hudi) and hybrid warehouse/lake architectures.
• Python for data tooling: automation, data quality frameworks, custom dbt macros or operators.
• Experience with Hex, Metabase, or similar notebook/BI tooling that sits on top of your dbt models.
• Prior experience in a high-growth AI/ML infrastructure or platform company

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200k -$240k + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Please see our privacy policy at https://www.together.ai/privacy.  

DevOps Lead: 

Top consumer brands like Nestle, Kimberly Clark, Natures Bounty, Johnson & Johnson, Mondelez, Kellogg to name a few rely on CommerceIQ suite of products to make efficient business decisions on a daily basis. It is critical to have services which run high quality data and algorithms that drive business decisions for our customers in a timely manner. As a DevOps you are responsible for building scalable, extensible, secure infrastructure on cloud for CommerceIQ’s applications and data science teams. 

In this role, you will work with a couple of Infra/DevOps SDEs and partner with engineering managers to achieve CommerceIQ’s QoQ goals. As a DevOps lead, you will play a crucial role in upkeep security, cost, deployment infrastructure and maintain/develop services owned by Infra/DevOps team.

A successful candidate will be obsessed with technology and relentlessly raise the bar on the architecture, design and quality of code delivered while aggressively pursuing optimizations to meet cost and scale SLAs. The candidate should be capable of managing a fast-paced delivery schedule and influence and drive a high-level engineering strategy with the leadership, as well as take a hands-on approach to implementing that strategy.

Functional level Expectations

• You should possess advanced knowledge of AWS and other software design approaches to guide the DevOps team in designing infrastructure that caters to scale, concurrency. Preferably AWS certified.
• You should be able to work in situations where you can use your prior expertise and judgment to determine goals, identify constraints, and propose an actionable plan. 
• Your work is typically focused on working with multiple teams’ architecture and product solutions. 
• You should be able to lead the design and implementation that are extensible and scalable
• For continuous integration and deployment
• For multi cloud deployments
• For DR of various services with CiQ infrastructure
• You should be able to influence as a “team lead”, management decisions and priorities and actively mentor to create force multipliers
• You should drive teams to adhere to engineering best practices in SDLC like code coverage, acceptance testing, CI/CD and design patterns that ensure consistency and standardization of architecture
• You should proactively simplify code, identify bottlenecks and resolve team architecture deficiencies. 
• You should be able to work along with other SDEs on their team, build relationships with stakeholders including customers, product managers, cross functional partners and external partners and integrate for a cohesive launch. 

Required Skills

• At least 6-10 years of technology experience including 3+ years of working experience in cloud 
• Ideal candidate should have handled operations, deployment and security of multiple SaaS/B2C products
• Deep knowledge of Python, Cloud or GoLang and one of the public clouds
• Good knowledge of log collection designs like EFK and metric collection tools like newrelic, Prometheus/Grafana or SignalFx
• Good knowledge in design, creation and consumption of RESTful API, Micro service architectures on public clouds preferably AWS
• Excellent analytical, communication and coding skills is a must.
• Thorough orientation towards code reviews, coding/design standards and documentation
• Good knowledge in designing with messaging systems such as SQS, Kafka.
• Good knowledge in designing with ETL pipelines using AWS Step, Azkaban, Airflow
• Good knowledge of any big data engine like Spark would be an advantage.

Below is a brief description of the charter for Infra team (subject o product/business deliverables)

Prometheus / Grafana 

K8s metrics and deployment metrics

We use newrelic(NR) for log collection and operational metrics. But certain shortcomings and solved problems in Prometheus/Grafana might force us to move away from NR. Apart from the prometheus track, the lead will have to work with all. stakeholders [apps/DS] to migrate from OCD to K8s, with all balance and checks in place

EFK stack

Log collection and analysis is via newrelic and it’ll cost us as we move more applications to K8s. We need to optimise or explore options like EFK stack of logs or sumo logic etc 

Security audit

We need to be proactive and do frequent security audits of our applications. We need a security center with all our documentation. Place to store the docs/questionnaires we filled for our clients etc. 

BCP 

We need to audit all our services for disaster recovery and develop a BCP across the company.

CI/CD 

The lead is expected to enforce git branching and set code cov, style check guidelines across teams and projects. E.g Publishing the guidelines for Java project, NodeJS project, Python project. Establishing average code cov metrics, running static code analysis, findbug and other security code scanning tools

Infra services

The Infra team also has many services, especially crucial services like BSS (CSS) . These infra services should set the benchmark for DevOps best practices in the org. 

Azkaban

Azkaban is our ETL orchestrator. It's run in-house and has custom code deployed. Maintenance of Azkaban to match growth is needed. Exploration and guidance to use other orchestrators like airflow, step function, astronomer is also expected

AWS admin and access / SF admin and access / AWS/SF cost , unit cost for client

Constant efforts to identify and reduce costs

AWS partnership [case studies, blogs, trainings, tech conf talks]

Cost savings across all our infrastructure.

SOC2 and other compliance 

Automate our soc2 process with diligence. This is more important as we expand to other pillars of soc2 and ISO.

The Data Scientist I supports statistical analyses of large datasets, the development and deployment of Machine Learning (ML), multimodal Deep Learning (DL), and Artificial Intelligence (AI) solutions that improve the relevance and value of ads across our Ad Exchange, Contextual Platform, and Attention Measurement Platform. This role focuses on applying strong analytical foundations, building and evaluating ML and DL models, leveraging general AI concepts and techniques, while also partnering closely with the Engineering, Product, and Data Science team to improve ad-serving performance, operational decision-making and lead development of new AI products that best serve the business. 

The ideal candidate is curious, data-driven, and eager to develop a strong understanding of the advertising domain and the systems that power large-scale decision-making. You will work with large datasets, contribute to production AI systems, and gain hands-on experience across the ML lifecycle — from exploration to monitoring.

Note: GumGum fosters a flexible work environment, offering GumGummers the ability to work either in-office or remotely/from home. For this position, in-person/office collaboration is required 2 days per week, supporting a balanced approach to flexibility and team engagement.

What You'll Achieve

• Support the translation of business and product requirements into data-driven analyses and ML solutions
• Partner with Engineering team members and senior Data Scientists to develop, test, and deploy ML and DL models
• Conduct exploratory data analysis to inform feature development and modeling approaches
• Build, run, and maintain regular pipelines to analyze production data, generate KPIs, and prepare automatic retraining of existing models
• Query, clean, and structure large datasets using SQL, Spark, and cloud data platforms
• Train, evaluate, and iterate on traditional ML models and multimodal deep learning models under guidance from senior team members
• Design and maintain Looker dashboards and other Business Intelligence (BI) tools to track Key Performance Indicators (KPI) for key stakeholders
• Develop and deploy agentic pipelines and other LLM-powered applications, including prompt engineering, tool use, and evaluation of model outputs
• Contribute to existing Machine Learning Engineering (MLE) workflows for model training, deployment, and monitoring
• Document analyses, models, and broader learning to support knowledge sharing across the team and non-technical audiences
• Continuously expand on statistical and AI foundations while learning new AI/ML techniques, tools, and advertising-domain concepts

Skills You'll Bring

• Bachelor’s degree in a quantitative field (e.g., Statistics, CS, Math, Physics, or Economics).
• 1–2+ years in a data-driven role such as Analytics, Data Science, or ML Engineering.
• Proficiency in Python and experience applying ML/DL methods using libraries like scikit-learn, PyTorch, HuggingFace, or OpenCV.
• Dependable SQL skills and experience designing pipelines or DAGs using tools like Airflow or Astronomer.
• Exposure to cloud environments (AWS/GCP, Databricks, Snowflake) and large-scale query tools like Spark or Snowpark.
• Strong grasp of A/B testing, experimental design, and statistical concepts (regression, classification, optimization).
• Experience with LLM prompting and interest in frameworks like RAG, LangChain, or agentic systems.
• Ability to design dashboards for diverse audiences and collaborate effectively with Product and Engineering teams.

What We Offer

At GumGum, competitive base pay is a part of a total rewards package, which also includes benefits, an emphasis on recognition, development, and wellness. The reasonable estimated base pay range for this role is from $128,000-$130,000 annually. The actual amount may be higher or lower. Individual compensation will vary based on factors including, but not limited to, relevant qualifications, work location, and labor market conditions.

The total rewards package offered also includes an employer-matched 401(k) retirement plan, and, depending on the role, participation in a bonus, commission, or stock incentive program. Your recruiter can share more specifics during the hiring process. Learn more about our U.S. benefits & perks package at gumgum.com/benefits.

We are interested in every qualified candidate who is eligible to work in the United States. However, we are not able to sponsor visas or take over sponsorship at this time.

About the role:

We live and breathe data at Enova - and this role sits at the center of it. As a Senior Business Intelligence Engineer, you will design, develop, and maintain robust reporting solutions and analytic applications that power decision-making across Finance, Marketing, Operations, Compliance, and more.

You will partner directly with business owners to gather requirements, translate them into scalable BI solutions, and take ownership of the full project lifecycle – from scoping through delivery and ongoing support. You will also play a key role in advancing our use of AI across the BI and data development workflow, leveraging modern tools to accelerate insight delivery and drive strategic impact.

Responsibilities:

• Partner with stakeholders across all our PnLs, Marketing, Operations, Accounting, and Finance to understand data needs and deliver actionable reporting solutions
• Lead technical initiatives end-to-end from architecting the solution to collaborating with peers on execution
• Design, implement, and maintain complex, multi-layered SQL and Python processes for reporting and analytics
• Architect and enhance ETL/ELT pipelines using Astronomer Airflow and other modern orchestration tools
• Operates as a senior member of the team: leads projects end-to-end from scoping and design through delivery and support
• Build, optimize, and support data marts and data warehouse solutions using Snowflake
• Develop and maintain dashboards and reports in Strategy(MSTR) and/or comparable BI tools
• Proactively identify and implement AI use cases across various business units, leveraging Enova’s AI tech stack(Claude, AWS Bedrock, Gemini, and more) to drive adoption
• Advise on best practices, innovative designs, and emerging BI technologies

Requirements:

Experience designing, developing, and working with data marts is a must.

• Expert-level SQL and database skills - writing high-quality, performant queries is a core part of this role
• 5-7+ years of experience with relational databases and cloud data warehouses (Snowflake, Databricks, BigQuery, or AWS Redshift)
• Strong grasp of data warehousing and data mart concepts, including dimensional modeling (star schema, slowly changing dimensions, etc.)
• 4+ years designing and implementing ETL/ELT pipelines; hands-on experience with Astronomer (Apache Airflow) or similar orchestration tools
• 4+ years working with enterprise BI and visualization tools – MicroStrategy, Tableau, Looker, PowerBI, or equivalent
• Proficiency in Python for data transformation, pipeline automation, and analytics
• Experience leveraging AI tools in day-to-day work - coding assistants, agents, and identifying new use cases (e.g., Claude, Gemini, AWS Bedrock) - with a curiosity to push adoption further
• Strong business acumen – ability to understand domain context and translate business needs into technical data solutions
• Proven experience working directly with senior management and cross-functional stakeholders to gather requirements and define success

Compensation:

This position includes various levels within our career ladder. The actual annual salary will be determined based on qualifications, skills, experience, and level assessed during the hiring process and may fall outside of the ranges shown. 

Budgeted annual salary ranges:

Senior BI Engineer I: $72,000-105,000
Senior BI Engineer II: $80,000-115,000

Additional compensation for this role may include a bonus. All full-time employees are eligible to participate in Company benefits, described in more detail here.

About the Role

As the Data Engineering Manager on the Core Data Engineering team, you'll lead the team responsible for integrating customers onto Afresh's platform and continuously improving the processes and code that make those integrations faster and more reliable. Your team will also build core dataflows that power new Afresh products as they launch.

This means owning the full lifecycle of customer data integrations: from raw data ingestion through transformation and delivery to downstream product and ML teams, across both new and existing grocery customers. Afresh's customer base is growing fast! We want to integrate customers across all of our products quickly and reliably. You'll drive integration execution, integration velocity, improve data quality and pipeline reliability, and build tooling and processes that make this process seamless.

This is a hands-on management role. You'll be deeply involved in technical decisions while also coaching your team, managing execution across multiple customer projects, and collaborating closely with Solutions Engineering, Product, and other engineering teams.

What You'll Do

• Lead and grow a team of data engineers, providing mentorship, technical guidance, and career development support.
• Own execution of customer integrations across multiple product lines (Store Ordering, RecsAPI, DC, Production Planning, and more), ensuring integrations are delivered on time and at a high quality bar.
• Build core dataflows that power new Afresh products, working closely with Product and ML teams to understand requirements and deliver reliable data foundations.
• Drive integration velocity: improve the speed and predictability of getting new customers from raw data landing to production-ready, leveraging AI tooling, standardized architecture, and strong contractor enablement.
• Continuously improve integration processes and shared code: identify patterns across customer integrations and invest in abstractions, templates, and automation that reduce per-customer effort over time.
• Collaborate deeply with Solutions Engineers, Product Managers, and customer-facing teams to scope integration work, unblock dependencies, communicate progress, and ensure integrations meet our customers’ needs.
• Improve data quality and pipeline reliability by investing in better alerting, self-healing patterns, and resilience to messy or incomplete customer data.
• Champion AI-forward engineering practices: evaluate and adopt AI tools and agentic workflows that accelerate development, automate repetitive tasks, and push the team to the bleeding edge of modern data engineering.
• Contribute technically: reviewing code and architecture decisions, pairing with engineers on complex problems, and staying close to the work.

What Makes You a Great Fit

We encourage all highly-qualified candidates to apply, even if they don't meet every listed qualification.

• 3+ years of engineering management experience
• Strong technical background in data engineering: experience with Python, PySpark, SQL, dbt, Airflow, and modern data platforms (Databricks, Snowflake, or similar).
• Track record of shipping high-quality data integrations or ETL systems at scale, and a deep understanding of what makes data pipelines reliable.
• Experience managing execution across multiple concurrent projects with different customers or stakeholders, balancing competing priorities with clear communication.
• Demonstrated ability to improve team velocity through better tooling, process improvements, or automation.
• Genuine enthusiasm for AI-augmented engineering workflows. You've experimented with AI coding tools, agentic workflows, or similar, and have a vision for how they can transform data engineering and uplevel the team.
• Comfort working with messy, real-world data from enterprise customers, and the pragmatism to ship solutions that work without over-engineering.
• Strong collaboration skills: you're effective working across engineering, product, and customer-facing teams in a fast-paced environment.

Nice to Have

• Experience in grocery, retail, or supply chain data domains.
• Prior experience at a high-growth startup navigating rapid customer expansion.

Our Tech Stack

• Python, PySpark, dbt
• Databricks (Delta Lake, Unity Catalog)
• Astronomer (Airflow) for orchestration
• Claude, GitHub, Shortcut, Notion for development workflows

Why Work Here

• We are a mission-driven company that reduces millions of pounds of food waste (and growing!) in grocery stores every year.
• Work on challenging, real-world data problems that have a direct and visible impact on customers.
• Be part of an engineering culture that's genuinely AI-forward. We want to be on the bleeding edge.
• Lead a team at a pivotal inflection point: rapid customer growth means your work directly shapes how Afresh scales.
• Collaborative, supportive environment & awesome people :)

This position is not eligible for company sponsorship.

Salary Range in Canada (CAD): $157,433 - $232,996

About the Role

As the Data Engineering Manager on the Core Data Engineering team, you'll lead the team responsible for integrating customers onto Afresh's platform and continuously improving the processes and code that make those integrations faster and more reliable. Your team will also build core dataflows that power new Afresh products as they launch.

This means owning the full lifecycle of customer data integrations: from raw data ingestion through transformation and delivery to downstream product and ML teams, across both new and existing grocery customers. Afresh's customer base is growing fast! We want to integrate customers across all of our products quickly and reliably. You'll drive integration execution, integration velocity, improve data quality and pipeline reliability, and build tooling and processes that make this process seamless.

This is a hands-on management role. You'll be deeply involved in technical decisions while also coaching your team, managing execution across multiple customer projects, and collaborating closely with Solutions Engineering, Product, and other engineering teams.

What You'll Do

• Lead and grow a team of data engineers, providing mentorship, technical guidance, and career development support.
• Own execution of customer integrations across multiple product lines (Store Ordering, RecsAPI, DC, Production Planning, and more), ensuring integrations are delivered on time and at a high quality bar.
• Build core dataflows that power new Afresh products, working closely with Product and ML teams to understand requirements and deliver reliable data foundations.
• Drive integration velocity: improve the speed and predictability of getting new customers from raw data landing to production-ready, leveraging AI tooling, standardized architecture, and strong contractor enablement.
• Continuously improve integration processes and shared code: identify patterns across customer integrations and invest in abstractions, templates, and automation that reduce per-customer effort over time.
• Collaborate deeply with Solutions Engineers, Product Managers, and customer-facing teams to scope integration work, unblock dependencies, communicate progress, and ensure integrations meet our customers’ needs.
• Improve data quality and pipeline reliability by investing in better alerting, self-healing patterns, and resilience to messy or incomplete customer data.
• Champion AI-forward engineering practices: evaluate and adopt AI tools and agentic workflows that accelerate development, automate repetitive tasks, and push the team to the bleeding edge of modern data engineering.
• Contribute technically: reviewing code and architecture decisions, pairing with engineers on complex problems, and staying close to the work.

What Makes You a Great Fit

We encourage all highly-qualified candidates to apply, even if they don't meet every listed qualification.

• 3+ years of engineering management experience
• Strong technical background in data engineering: experience with Python, PySpark, SQL, dbt, Airflow, and modern data platforms (Databricks, Snowflake, or similar).
• Track record of shipping high-quality data integrations or ETL systems at scale, and a deep understanding of what makes data pipelines reliable.
• Experience managing execution across multiple concurrent projects with different customers or stakeholders, balancing competing priorities with clear communication.
• Demonstrated ability to improve team velocity through better tooling, process improvements, or automation.
• Genuine enthusiasm for AI-augmented engineering workflows. You've experimented with AI coding tools, agentic workflows, or similar approaches, and you have a vision for how they can transform data engineering and uplevel the team.
• Comfort working with messy, real-world data from enterprise customers, and the pragmatism to ship solutions that work without over-engineering.
• Strong collaboration skills: you're effective working across engineering, product, and customer-facing teams in a fast-paced environment.

Nice to Have

• Experience in grocery, retail, or supply chain data domains.
• Prior experience at a high-growth startup navigating rapid customer expansion.

Our Tech Stack

• Python, PySpark, dbt
• Databricks (Delta Lake, Unity Catalog)
• Astronomer (Airflow) for orchestration
• Claude, GitHub, Shortcut, Notion for development workflows

Why Work Here

• We are a mission-driven company that reduces millions of pounds of food waste (and growing!) in grocery stores every year.
• Work on challenging, real-world data problems that have a direct and visible impact on customers.
• Be part of an engineering culture that's genuinely AI-forward. We want to be on the bleeding edge.
• Lead a team at a pivotal inflection point: rapid customer growth means your work directly shapes how Afresh scales.
• Collaborative, supportive environment & awesome people :)

This position is not eligible for company sponsorship.

Salary Range in U.S.: $ 179,469 - $262,811

What You'll Be Doing:

This role will be responsible for all operational database administration duties, data engineering, and data operations within the Data Lake and Enterprise Data Warehouse (Snowflake) ecosystem including, but not limited, to the following:

• Take the lead on our daily database administration, ensuring our data environment stays healthy and reliable.
• Partner closely with our Staff EDW Architect to bring new visions to life, designing high-quality data artifacts that follow industry best practices.
• Turn business needs into smart, reusable ETL solutions that grow alongside our company.
• Design end-to-end pipelines using your expertise with data models and architecture diagrams to build automated ingestion and transformation pipelines that keep our data moving on schedule
• Analyze and produce artifacts such as Source-to-Target Mapping documents.
• Build pipelines that bring together data from everywhere—whether it’s RDBMS, APIs, JSON, or flat files—creating a seamless flow of information.
• Define the "Source of Truth" using tools like Python and ETL software to clean, integrate, and transform data into a single, reliable resource the whole company can trust.
• Provide day-to-day operational support for all the data pipelines by monitoring and investigating alerts, while troubleshooting and remediating production issues in a timely manner.
• Act as a player-coach for junior and intermediate data engineers and ETL developers as the EDO team grows.

What you’ll bring to the table:

What You'll Be Doing:

This role will be responsible for all operational database administration duties, data engineering, and data operations within the Data Lake and Enterprise Data Warehouse (Snowflake) ecosystem including, but not limited, to the following:

• Take the lead on our daily database administration, ensuring our data environment stays healthy and reliable.
• Partner closely with our Staff EDW Architect to bring new visions to life, designing high-quality data artifacts that follow industry best practices.
• Turn business needs into smart, reusable ETL solutions that grow alongside our company.
• Design end-to-end pipelines using your expertise with data models and architecture diagrams to build automated ingestion and transformation pipelines that keep our data moving on schedule
• Analyze and produce artifacts such as Source-to-Target Mapping documents.
• Build pipelines that bring together data from everywhere—whether it’s RDBMS, APIs, JSON, or flat files—creating a seamless flow of information.
• Define the "Source of Truth" using tools like Python and ETL software to clean, integrate, and transform data into a single, reliable resource the whole company can trust.
• Provide day-to-day operational support for all the data pipelines by monitoring and investigating alerts, while troubleshooting and remediating production issues in a timely manner.
• Act as a player-coach for junior and intermediate data engineers and ETL developers as the EDO team grows.

What you’ll bring to the table:

Our Company:

At impact.com we are passionate about our people, our technology, and are obsessed with customer success. Working together enables us to grow rapidly, win, and serve the largest brands in the world. We use cutting edge technology to solve real-world problems for our clients and continue to pull ahead of the pack as the leading SaaS platform for businesses to automate their partnerships and grow their revenue like never before. We have an entrepreneurial spirit and a culture where ambition and curiosity is rewarded. If you are looking to join a team where your opinion is valued, your contributions are noticed, and enjoy working with fun and talented people from all over the world, then this is the place for you!

impact.com, the world’s leading partnership management platform, is transforming the way businesses manage and optimize all types of partnerships—including traditional rewards affiliates, influencers, commerce content publishers, B2B, and more. The company’s powerful, purpose-built platform makes it easy for businesses to create, manage, and scale an ecosystem of partnerships with the brands and communities that customers trust to make purchases, get information, and entertain themselves at home, at work, or on the go. To learn more about how impact.com’s technology platform and partnerships marketplace is driving revenue growth for global enterprise brands such as Walmart, Uber, Shopify, Lenovo, L’Oreal, Fanatics and Levi’s, visit www.impact.com.

Your Role at impact.com:

impact.com's Analytics Data Platform processes terabyte-scale data for one of the world's leading partnership SaaS platforms, covering the full lifecycle from ingestion to governed access.

We're looking for a technical people leader to own and grow the Data Platform Engineering team, while driving the architecture, reliability, and evolution of impact.com's Analytics Data Platform. This is a senior player-coach role: you'll combine hands-on technical leadership with direct people management, building a high-performing team, owning delivery, and shaping platform strategy. Expect to spend roughly 40% hands-on and 60% on leadership, people, and delivery, shifting as the team matures. You'll be accountable for both the team's output and its people, ensuring engineers are supported, challenged, and growing.

As Team Lead for Data Platform Engineering, you'll work across the full data ecosystem (ingestion, processing, storage, orchestration, access, and governance) using technologies like Scala, Python, Google Cloud Dataproc, BigQuery, and Airflow. You'll shape the operating model for the team, establish engineering standards, and drive platform strategy. This role is levelled at Senior/Staff Engineer on the engineering management track and reports to the Director of Advanced Analytics. The position is based in Cape Town, hybrid, with one day per week in office.

Data Platform Engineering is one of four sub-teams within the Data Analytics Group (DAG). DPE owns ingestion, infrastructure, orchestration, and platform services. Analytics Engineering owns the dbt transformation layer and semantic layer. Data Analytics owns reporting and insights. Data Product Management owns product roadmaps and governance processes. You'll partner closely with all three, but won't own dbt models, reporting, or product prioritisation directly.

Our ideal candidate combines distributed systems expertise with proven people leadership. Someone who can make architecture decisions, run a team, and translate platform trade-offs into business language. This role is for someone who wants to build both a platform and a team, not just maintain what exists.

What you'll inherit:

The platform has strong foundations: Scala/Spark pipelines processing TB-scale data, 37+ Fivetran connectors, Airflow on Astronomer for orchestration, a mature dbt-based transformation layer, and BigQuery as the core analytical warehouse. Databricks is part of the current stack but is being transitioned off in favour of Dataproc and BigQuery-native patterns. What's greenfield: governance frameworks, data contracts, SLOs/SLAs, observability, cost management, and data quality monitoring. You'll build the team that delivers on these opportunities.

What you'll do:

• Build and run a team of data platform engineers: set direction, remove blockers, give honest feedback, and make sure people are growing in their careers.
• Create an environment where engineers do their best work: high trust, high standards, low friction.
• Turn platform strategy into a delivery plan the team can actually execute: sequence the work, balance new builds against keeping the lights on, and make the trade-offs visible.
• Own capacity and priorities across the team. Keep cross-team dependencies from becoming bottlenecks.
• Lead platform architecture decisions, design reviews, and cross-org technical coordination, defining and promoting architectural patterns, standards, and guidelines.
• Design and implement scalable pipelines using Scala, Python, and distributed frameworks (Spark on Dataproc) processing TB-scale data. Lead the migration off Databricks onto Dataproc and BigQuery-native patterns.
• Establish platform reliability practices: define SLOs/SLAs, build incident management and response processes, and drive systemic improvements across pipeline and infrastructure operations.
• Design and maintain core platform services (Dataproc clusters, BigQuery datasets, Cloud Storage) and optimise platform performance and cost at scale.
• Build and operationalise data governance, security, and compliance capabilities, including data contracts, access controls, and audit logging. Many of these practices are greenfield; you will be building them from scratch.
• Define and evolve team rituals, operating cadences, and engineering standards: code review norms, documentation practices, on-call fairness, and knowledge sharing.
• Represent the team to senior leadership and act as the single point of accountability for the team's commitments to cross-functional partners (analytics engineering, data analytics, product, finance).
• Own data source integrations and data quality across the platform, including third-party connectors (e.g. Fivetran), custom Scala/Spark ingestion pipelines, dbt orchestration via Airflow and Astronomer Cosmos, and quality monitoring and remediation practices.

Required Skills & Experience:

• 7+ years of experience in data platform engineering, data engineering, or distributed systems, including 2+ years leading engineering teams
• Strong programming skills in Scala and Python, including functional Scala patterns (cats-effect / Typelevel ecosystem), testing, code review, and production-grade engineering practices
• Deep hands-on experience with distributed computing frameworks: Apache Spark (Dataproc) and large-scale data processing. Databricks experience is useful for leading the migration off it
• Expert-level knowledge of cloud-native data platforms and services. Our stack runs on Google Cloud Platform (BigQuery, Dataproc, Cloud Storage), but equivalent depth on AWS or Azure is equally valued
• Advanced SQL skills including complex query optimisation, partitioning strategies, and performance tuning
• Experience building and scaling engineering teams, not just managing established ones: hiring, running interview loops, onboarding, and growing engineers from junior to senior
• Experience leading cross-team architecture initiatives and establishing platform-wide standards
• Experience with orchestration tools (Airflow, Astronomer, and Astronomer Cosmos for dbt orchestration) and CI/CD pipeline design
• Familiarity with legacy CI/CD tooling (e.g. Jenkins), particularly in the context of pipeline migration and modernisation
• Strong understanding of data governance, security best practices, and compliance requirements
• Experience with infrastructure as code and automated deployment practices
• Excellent communication skills, with the ability to translate technical trade-offs for non-technical stakeholders and represent the team to senior leadership
• Strong people management skills: coaching, mentorship, performance management, and career development
• Experience with monitoring, alerting, observability, and on-call operations for production data systems

Nice to Have:

• Experience in the digital marketing technology or partnership/affiliate industry
• General experience with Google Cloud Platform services beyond the core required stack (e.g. Pub/Sub, BigTable, Cloud Functions); familiarity with Dataflow is a plus but not a current platform requirement (it is under future evaluation)
• Familiarity with streaming technologies such as Apache Kafka and stream processing frameworks
• Experience standing up data quality frameworks and monitoring practices from scratch
• Experience with FinOps or cloud cost management at scale
• Experience migrating off legacy platforms (e.g. Databricks, on-prem Hadoop) to cloud-native alternatives
• Experience with organisational design, team scaling, or building engineering teams from scratch
• Awareness of AI agent architectures and how they connect to data platforms (e.g. MCP, tool-use patterns)
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)

Benefits:

• Hybrid, Casual work environment
• Responsible PTO policy
• Take the time off that you need. We are truly committed to a positive work-life balance, recognising that it is important to be happy and fulfilled in both

• Training & Development
• Learning the advanced partnership automation products
• Medical Aid and Provident Fund 
• Group schemes with Discovery & Bonitas for medical aid
• Group scheme for provident fund
• Restricted Stock Units
• 3-year vesting schedule pending Board approval
• Internet Allowance
• Fitness club fee reimbursementsTechnology stipend 
• Primary Caregiver Leave
• Mental  Health and Wellness Benefit - Including 12 Therapy/Coaching sessions + Dependent coverage 

impact.com is proud to be an equal opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

At Rockstar Games, we create world-class entertainment experiences. 

Become part of a team working on some of the most rewarding, large-scale creative projects to be found in any entertainment medium - all within an inclusive, highly-motivated environment where you can learn and collaborate with some of the most talented people in the industry. 

Rockstar Games is on the lookout for a skilled Analytics Engineer with strong software development skills who is passionate about games and big data.

This is a full-time, permanent and in-office position based in Rockstar’s unique game development studio in the heart of Leeds, England. 

WHAT WE DO

• The Rockstar Games Analytics team provides insights and actionable results to a wide variety of stakeholders across the organization in support of their decision making.
• We partner with multiple departments across the company, leveraging analytics to measure and improve on the success and health of our games.
• We collaborate as a distributed team to develop innovative data pipelines, data products, data models, reports, analyses, and machine learning applications.
• The Analytics Architecture vertical within the Analytics team is tasked with building out high impact data models and internal tooling to support the wider Analytics team.

RESPONSIBILITIES

• Partner with Data Scientists and business stakeholders to understand analytical needs and translate them into robust data infrastructure and workflows for the global Analytics team to leverage for deriving insights. Collaborate with the Data Engineering team to design data models and data pipelines. 
• Design, develop and maintain scalable data pipelines. Monitor performance and optimize data systems for high performance and reliability.
• Collaborate with the Data Integrity team to build a Data Quality Framework that ensures production data meets SLAs for key stakeholders and business processes.  
• Perform timely Root Cause Analysis to troubleshoot data-related issues; assist in implementation of code and process fixes.
• Provide thought leadership and collaborate with other team members to continue to scale our architecture to evolve for the needs of tomorrow. Contribute to the establishment of best practices including code standards, version control, documentation, testing, and review processes.
• Develop and support CI/CD processes using Terraform, Github, TeamCity, Octopus, etc.
• Define and implement monitoring and alerting policies for data solutions. 

REQUIREMENTS

• 3+ years of experience in analytics engineering or data engineering Python. 
• Proven track record in building, monitoring, and optimizing large-scale data systems and analytics infrastructure.
• Experience working in Databricks & Azure environment (or other cloud equivalents).  
• Experience working with pipeline scheduling tools such as Airflow & Astronomer.
• Experience working with CI/CD tools such as TeamCity, Terraform, Github, Octopus.
• Bachelor’s degree or equivalent in an engineering or technical field such as Computer Science, Mathematics, Statistics, or strong quantitative and software background. 
• 3+ years of hands-on experience in using advanced SQL queries (analytical functions), experience in writing and optimizing highly efficient SQL queries.
• Ability to push the frontier of technology and freely pursue better alternatives.

PLUSES

Please note that these are desirable skills and are not required to apply for the position.

• Knowledge of software coding practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations.
• Streaming or NRT modeling experience.
• Experience with PySpark.
• Passion for technical mentorship. 

HOW TO APPLY

Please apply with a CV and cover letter demonstrating how you meet the skills above. If we would like to move forward with your application, a Rockstar recruiter will reach out to you to explain next steps and guide you through the process.

Rockstar is committed to creating a work environment that promotes equal opportunity, dignity and respect. In line with this commitment, Rockstar will provide reasonable accommodations to qualified job applicants with disabilities during the recruitment process in order for such applicants to be considered for the position for which they are applying, as well as to qualified employees to enable them to perform the essential functions of their roles. If you need more information about Rockstar’s reasonable accommodation policies or process, or need to request an accommodation, please notify your recruiter during the interview process. 

If you’ve got the right skills for the job, we want to hear from you. We encourage applications from all suitable candidates regardless of age, disability, gender identity, sexual orientation, religion, belief, race, or any other protected category.

#LI-CG1

Local or 100% Remote

About Point

✨ Real Impact, Real People: Our mission at Point is to make homeownership more valuable and accessible. Your work directly helps homeowners access their wealth, achieve financial flexibility, and realize life changing goals.

✨ Funding: With over $175M raised from top investors like Andreessen Horowitz, WestCap, Greylock, and Prudential, we’re scaling fast! You have the opportunity to join us at a pivotal stage.

✨ Game-changing Product: We're building a category defining company in home equity. We’ve earned a 4.7 Trustpilot rating and an A+ from the BBB, a testament to the value we provide to our 20,000+ customers.

✨ Great Place to Work: Our employees love working here! We are a Certified Great Place to Work and a Fortune Best Workplaces in the Bay Area.

✨ Remote First Culture, Genuine Connection: Work from anywhere in the U.S., while staying closely connected through virtual collaboration, team gatherings, and a people-first culture.

About the role

Point is looking for a Senior Data Engineer to join a lean, high-impact data team that powers analytics for 15+ business functions across a home equity investment company. You'll own the development and maintenance of a 1,600+ model dbt platform, build and optimize ETL pipelines in Airflow, manage Snowflake infrastructure (DDL deployments, permissioning, cost optimization), and ensure the reliability of 40+ data source integrations. This is a hands-on engineering role where your work directly shapes how the business makes decisions, and you'll also have the opportunity to contribute to emerging AI initiatives as the team expands into Cortex AI and LLM-powered workflows.

Your responsibilities

• Develop, maintain, and optimize dbt models across 20+ data marts serving business functions including marketing, production, servicing, finance, compliance, investor operations, and data science
• Build and maintain ETL/ELT pipelines using Airflow (Astronomer), including custom operators, hooks, and sensors for data ingestion from 40+ sources
• Manage Snowflake DDL deployments via schemachange — databases, schemas, warehouses, users, roles, network policies, integrations, and UDFs
• Maintain and extend Terraform-managed RBAC grants (200+ .tf files), ensuring secure, auditable access provisioning across the Snowflake environment
• Drive Snowflake cost optimization — warehouse sizing, multi-clustering configuration, query performance tuning, and monitoring to target 15–25% cost reduction
• Support CI/CD pipelines for dbt Cloud, Astronomer deployments, and automated data quality testing
• Develop and maintain AWS Lambda functions supporting data integrations, APIs, and serverless data processing workflows
• Perform code reviews, enforce engineering standards, and contribute to data model documentation (YAML configs, markdown docs) across the platform
• Collaborate with analysts and data scientists to build and maintain data marts, feature engineering tables, and semantic layer metrics that enable self-service analytics
• Contribute to the team's AI/ML initiatives — including Cortex AI semantic models, data preparation for LLM workflows, and infrastructure supporting data science deployment pipelines 

About you

• 5+ years of experience in data engineering or analytics engineering, with significant hands-on dbt and SQL development experience
• Deep Snowflake expertise — DDL management, role-based access control, warehouse configuration, performance tuning, and cost optimization
• Strong experience with Apache Airflow (or similar orchestration tools) — building DAGs, custom operators, scheduling, and failure handling
• Proficiency in Python for data pipeline development, scripting, Lambda functions, and CI/CD automation
• Experience with Infrastructure as Code — Terraform for access management, schemachange or Flyway for database migrations, version-controlled deployments
• Solid understanding of data modeling — dimensional modeling, fact/dimension tables, data mart design, and enterprise data architecture
• AWS experience — Lambda, S3, SAM/CloudFormation, and serverless architecture patterns
• Strong Git workflows — branching strategies, pull request reviews, CI/CD integration (GitHub Actions or similar)
• Excellent troubleshooting skills — ability to diagnose pipeline failures, data quality issues, and performance bottlenecks across a complex platform
• Clear communicator — can work with non-technical stakeholders to translate business requirements into data models and explain technical tradeoffs

Our benefits 

• Generous health benefits: We provide comprehensive medical, dental, and vision plans with options for flexible spending accounts (FSA) and health savings accounts (HSA).
• Unlimited paid time off: Recharge with unlimited paid time off and 10 company holidays. 
• Flexible remote and onsite work: Our teams work from many different locations and time zones. We support fully remote work and also have an amazing in-person environment in our downtown Palo Alto, CA HQ. 
• Fully paid parental leave: Point will supplement state Paid Family Leave (PFL) so employees receive 100% of their regular base pay, plus two additional weeks of fully paid leave after state PFL ends. In states without PFL, Point offers up to 8 weeks of paid parental leave. In addition, employees also receive 4 weeks of fully paid transition time, during which you may work 2–3 days per week while receiving full base pay.
• Equity: We offer meaningful equity because we believe in sharing the value you help create. Your contributions directly impact our growth, and your equity gives you a stake in our future success.
• Financial wellness:  We provide 401K retirement plans for employees as well as guaranteed life insurance and short- and long-term disability coverage.
• Extra work/life benefits: We provide monthly stipends for internet, mobile plans, wellness perks, a one-time home office reimbursement, and company provided equipment including a MacBook and monitor.

Compensation at Point will be determined by skills, experience, and geographic location. Point has identified the expected annual base salary for this role at this level based on the market by tiers (Region | Location | Market Salary):

• Tier 1 | San Francisco Bay Area, New York, and Seattle | $177,650 - $196,350 
• Tier 2 | Chicago, Austin, Denver, Boston, Washington DC, San Diego, Portland, Sacramento, Philadelphia, Los Angeles & Santa Barbara | $161,500 - $178,500
• Tier 3 | All other US metro areas | $154,850 - $171,150

This does not include any other potential components of the compensation package, including equity, benefits, and perks outlined above. At the launch of each position, we benchmark compensation to the appropriate role and level utilizing competitive compensation data from various data sources as references. At the offer stage, we use the signal we received from our interviews, coupled with your experience, location, and other job-related factors, to determine final compensation.

Location Requirement: This is a remote position. However, candidates must reside in one of Point’s states of operation: AL, AZ, AR, CA, CO, CT, DC, FL, GA, IL, KS, KY, MA, MD, MI, MN, MO, NH, NV, NJ, NY, NC, OH, OR, PA, SC, TN, TX, UT, VA, WA, WI.

Point is proud to be an equal-opportunity employer. We provide employment opportunities regardless of age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. Each individual at Point brings their own perspectives, work experiences, lifestyles, and cultures with them, and we believe that a more diverse team creates more innovative products, provides better services to customers, and helps us all grow and learn.