Staff PRODUCT ENGINEER (Contract)

Build it. Ship it. Own it.

Location: Argentina, Brazil, Colombia, Chile, Mexico, Dominican Republic, Peru

About Counterpart

Counterpart is an insurtech platform reimagining management and professional liability for the modern workplace. We believe that when businesses lead with clarity and confidence, they become more resilient, more innovative, and better prepared for what's ahead. That's why we built the first Agentic Insurance™ system — where advanced AI and deep insurance expertise come together to proactively assess, mitigate, and manage risk. Backed by A-rated carriers and trusted by brokers nationwide, our platform helps small businesses grow with confidence. Join us in shaping a smarter future, helping businesses Do More With Less Risk®.

The Role

As a Product Engineer at Counterpart, you own problems and drive outcomes. You iron out requirements with stakeholders, partners, and customers. You leverage AI for implementation. You verify the end-to-end result. You iterate until you move the needle.

You run projects with other Product Engineers and AI agents. You know when to delegate to a human, when to delegate to an agent, and when to do it yourself. 

YOU WILL

Own the Problem

• Take accountability for the outcome. 
• Talk directly to your users, stakeholders, and peers to discover the problem. 
• Translate business intents into functional requirements that engineers and agents can build against. Determine where human judgment is non-negotiable and where AI can augment or act autonomously. 
• Own the non-functional technical requirements. Specify the quality bar: performance, scalability, reliability, security, maintainability.

Build with AI

• Write production-quality code across the stack: Python, Django, AWS, React, and PostgreSQL. You are not afraid of any layer.
• Direct agents and review their output with domain depth. Catch what they miss across the functional and non-functional bar: correctness, edge cases, performance, security, testability, readability.
• Evaluate and improve agent output regularly. Track failure modes. Refine the prompts and patterns that lift the quality ceiling over time.
• Run projects that span other Product Engineers and agents. Coordinate work, manage dependencies, and keep things moving without micromanaging.
• Contribute to architectural decisions. Bring options and alternatives, not objections. Know when the elegant solution is the wrong call for a startup moving fast.
• Contribute to and manage the context harness as you build and deliver.

Verify and Iterate

• Surface delivery risks early. Bring solutions, not just problems. Keep your stakeholders informed without turning every risk into a meeting.
• Verify the end-to-end result yourself. Confirm the system does what the requirements say, and that the requirements were right.
• Drive adoption of what you ship. Documentation, enablement, and usage monitoring are part of the job.
• Monitor your services in production. Own the signal. Debug and fix what breaks.
• Participate in support rotations. When users run into issues with what you shipped, you investigate and fix them.
• Iterate until you move the needle. Shipping is not the finish line. Outcome is.

Raise the Bar

• Mentor engineers around you. Give direct, useful feedback. Leave things better than you found them.
• Make deliberate calls on technical debt: when to accumulate it, when to pay it down, when to refactor. Translate trade-offs into language that business leaders can act on.
• Operate to the engineering standards for quality, reliability, and operational excellence.

YOU HAVE

• 10+ years building production web applications across the full stack. The years are the floor, not the bar. What matters is whether you have actually done this: owned a problem end-to-end, written the spec, built the thing, coordinated the work, and shipped it without someone else holding the thread.
• Demonstrated experience gathering requirements directly. You have interviewed users, customers, and partner teams, and turned ambiguous intent into specs that engineers and agents can build against.
• Demonstrated experience owning both functional and non-functional requirements. You have defined what a system must do and held the line on how well it must do it.
• A track record of owning a domain or initiative without close supervision. You have navigated ambiguity, made judgment calls, and been accountable for the outcome.
• Extreme ownership and a bias for action. You do not sit and wait. You chase stakeholders, customers, and answers when you need them.
• Experience coordinating across engineers and AI agents on a shared initiative. You have managed the work without holding a formal management title.
• Strong technical judgment and the depth to catch what AI-generated code gets wrong: not syntax, but correctness, architecture, intent.
• Proficiency with Python and Django. Working knowledge of TypeScript, React, and PostgreSQL.
• Proficiency in developing with AI tools (Claude Code, Cursor, OpenAI Codex, similar) and the drive to stay ahead of how AI development practices are evolving.
• Domain curiosity. You learn the business context your work supports, not just the systems. You understand why the problem matters, not just how to solve it.
• The ability to communicate clearly across engineering, product, and business, translating technical complexity without losing meaning.
• Low ego and high Emotional Quotient (EQ). You influence without authority, take feedback well, and leave your ego out of technical decisions.
• Experience working with distributed, remote teams.

HOW THIS ROLE WORKS

Product Engineering exists to collapse the distance between a problem and a working solution. You own a problem or part of a vision. You shape it with stakeholders. You build it with AI. You verify it end-to-end. You iterate until the metric moves.

You work within the Context Architecture, a shared system of documentation, design patterns, and domain knowledge that gives you the technical context to make autonomous decisions at every step.

This is not a role for engineers who need to be told how. It is a role for engineers who can take a problem, fill in the gaps with good judgment, and ship something that works.

WHO YOU WILL WORK WITH

• Derrick Franco, Principal Engineer: After teaching himself to code at 13, Derrick went on to co-found Jumpstart in addition to building out the technical divisions at InvestorsAlley and Phinaz Media Group. Nowadays Derrick spends his time reading, with his wife, and working on his podcast The Future of Work Project.
• Julian Prokay, Principal Engineer: A veteran of the SaaS space with experience working on both student systems and construction management software. In his free time, he enjoys history podcasts and fantasy literature.
• Tony Nguyen, Engineering Manager: Tony is an engineering and platform leader with 25+ years of leadership experience at companies like Sony PlayStation Network, Intuit TurboTax, and ServiceNow. He thrives at building products, scaling teams, and leading organizations, but more so building people. He believes the best leadership and engineering comes from bringing your full identity to work. For him that's faith, family, and sports! Outside of work, you can catch him on the couch with his 3 adult children watching anime, going to Disneyland, or having game nights.
• Ron Alexessen, Engineering Manager: With experience at Hewlett-Packard, New Relic, Rackspace, and consulting agencies, Ron has built teams and shaped technical culture across different environments. He's passionate about AI-enabled development as a force multiplier and building solutions that create real business impact. Outside work, he boulders, runs, rides gravel bikes, and co-parents active teenagers, embracing structured chaos in all of it.
• Newton Truong, Engineering Manager: Newton joins Counterpart with 7+ years of experience in platform and data engineering. In his last company Thrive Market, he had a hand in building out new microservices and architecting data pipelines with Airflow and Spark. He's vegetarian and loves trying new restaurants when traveling. Nowadays he's busy keeping up with his 2-year-old and doing outdoor activities with his family.
• John Mendez, Engineering Manager: John has experience working at companies of all sizes. He enjoys startups and small-to-medium businesses, where he can wear multiple hats and help deliver solutions. Outside of work, he stays busy with his four kids and their activities. He also makes time to stay active by working out at home and running or walking with his dog.

COUNTERPART'S VALUES

• Conjoin Expectations - it is the cornerstone of autonomy. Ensure you are aware of what is expected of you and clearly articulate what you expect of others.
• Speak Boldly & Honestly - the only failure is not learning from mistakes. Don't cheat yourself and your colleagues of the feedback needed when expectations aren't being met.
• Be Entrepreneurial - control your own destiny. Embrace action over perfection while navigating any obstacles that stand in the way of your ultimate goal. Leverage AI ruthlessly. We measure success by outcomes, not lines of code written. Use every available AI tool to ship faster and think more strategically.
• Practice Omotenashi ("selfless hospitality") - trust will follow. Consider every interaction with internal and external partners an opportunity to develop trust by going above and beyond what is expected.
• Hold Nothing As Sacred - create routines but modify them routinely. Take the time to reflect on where the business is today, where it needs to go, and what you have to change in order to get there.
• Prioritize Wellness - some things should never be sacrificed. We create an environment that stretches everyone to grow and improve, which is fulfilling, but is only one part of a meaningful life.

WE OFFER

1. Work from Anywhere - Counterpart is a fully distributed company, meaning there is no office. We allow employees to work from wherever they do their best work, of course prioritizing meetings that often happen in US time zones.
2. Language Classes - We sponsor up to $100 per calendar year to be used towards language classes to help support your English language skills.
3. Paid Vacation and Time Off
4. Parental Leave - Development Partners will be eligible for unpaid Parental Leave to bond with a newborn, newly-adopted, or newly placed child. Under this policy, development partners and independent contractors will be eligible for up to 8 weeks of unpaid parental leave. Any time off will be subject to approval and must be requested at least 45 days in advance.
5. Computer - We reimburse you for your locally procured MacBook Pro. If you are still with the company after one year, it is yours to keep!

Our estimated pay range for this role is USD $90,000 - $110,000 annually. Contractor compensation is determined by a variety of factors, including but not limited to, market data, location, internal equitability, and experience.

We are committed to being a welcoming and inclusive workplace for everyone, and we are intentional about making sure people feel respected, supported and connected at work, regardless of who you are or where you come from. We value and celebrate our differences and we believe being open about who we are allows us to do the best work of our lives.

We are an Equal Opportunity Employer. We do not discriminate against qualified applicants or employees on the basis of race, color, religion, gender identity, sex, sexual preference, sexual identity, pregnancy, national origin, ancestry, citizenship, age, marital status, physical disability, mental disability, medical condition, military status, or any other characteristic protected by federal, state, or local law, rule, or regulation.

Scale's Robotics business unit is dedicated to solving the data bottleneck in Physical AI across Robotics, Autonomous Vehicles, and Computer Vision. In this role, you'll be a key contributor building production systems for robotics data collection, model training pipelines, and evaluation infrastructure. You'll have the opportunity to own critical parts of our robotics platform, work directly with cutting-edge robotics and AV customers, and shape the future of embodied AI systems.

You Will:

• Own and architect large-scale data processing pipelines for robotics and autonomous vehicle datasets
• Build ML training and fine-tuning pipelines using Scale's robotics data
• Work across backend (Python, Node.js, C++), and frontend (React, TypeScript) stacks to build end-to-end solutions
• Develop tools and real-time systems for robotics data collection, teleoperation, model evaluation, data curation, and data annotation
• Interact directly with robotics and AV stakeholders to understand their technical needs and drive product development
• Design comprehensive monitoring and evaluation frameworks for robotics models and data quality
• Solving complex, late-stage industry challenges in concurrent and real-time robotic systems, with strict attention to timing constraints and data integrity. This often involves deep investigation, reviewing academic papers, and direct collaboration with robotics vendors
• Collaborate with ML engineers and researchers to bring robotics research into production
• Deliver features at high velocity while maintaining system reliability and performance

Ideally, You Have:

• 3+ years of high-proficiency software engineering experience, with a strong background in complex systems and the ability to independently research, analyze, and unblock hard technical problems.
• Strong programming skills in Python and TypeScript/Node.js for production systems
• Experience with React and modern frontend development for 3D interfaces
• Concurrent and real-time systems, with special attention to timing constraints
• Understanding of distributed systems, workflow orchestration, and cloud infrastructure (AWS, Temporal, Kubernetes, Docker)
• Experience with databases (MongoDB, PostgreSQL) and data processing at large scale
• Track record of working with cross-functional teams including ML engineers, researchers, and customers
• Strong communication skills and ability to operate with high autonomy

Nice to Haves:

• Experience with C++
• Experience with robotics hardware platforms (robotic arms, mobile robots, perception systems) with a focus on time synchronization
• Background in computer vision, SLAM, motion planning, or imitation learning
• Familiarity with autonomous vehicle data, lidar technologies, or 3D data processing
• Experience with ML model deployment and serving frameworks
• Knowledge of teleoperation systems (ALOHA, UMI, hand tracking) or VR interfaces
• Experience with workflow orchestration systems (Temporal, Airflow)
• Published research or open-source contributions in robotics or autonomous systems

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

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.

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

On-call work may be necessary occasionally to help address bugs, outages, or other operational issues, with the goal of maintaining a stable and high-quality experience for our customers.

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

Role Description

Dropbox is looking for a Data Engineer to join our Analytics Data Engineering (ADE) team within Data Science & AI Platform. In this role you will build large, scalable analytics pipelines using modern data technologies. This is not a “maintain existing platform” or “make minor tweaks to current code base” kind of role. We are effectively building from the ground up and plan to leverage the most recent Big Data technologies. If you enjoy building new things without being constrained by technical debt, this is the job for you!

Responsibilities

• Define company data assets (data model), Spark, SparkSQL jobs to populate data models
• 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

On-call work may be necessary occasionally to help address bugs, outages, or other operational issues, with the goal of maintaining a stable and high-quality experience for our customers.

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

Preferred Qualifications

• 7+ years of SQL experience 
• 7+ years of experience with schema design, dimensional data modeling, and medallion architectures
• Experience with Airflow or other similar orchestration frameworks
• Experience building data quality monitoring using MonteCarlo or similar tools

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

Seeking to hire a Contractor based out of Mexico or Argentina for Lead Analytics Engineering Data Team

• We are looking for a Lead Analytics Engineer to join our data team.
• Reporting to the Hiring Manager, you will be responsible for applying data engineering best practices to analytics code to transform, test, and document data.
• You will provide clean and organized data sets to end users.
• You will be provide expert code reviews and mentorship to other engineers in the data space
• You will be responsible for building and maintaining composable data models, as well as optimizing SQL query performance for the models you build.
• You will transform raw data into business insights, working closely with stakeholders and developing analyses to answer critical business questions.
• You will create data visualizations and help stakeholders explore and understand the data visualization tools available to them.

Why you'll love this role:Data Modeling and Transformation

• Build new analyses and support existing ones using SQL and Python.
• Apply software engineering principles like version control and continuous integration to the analytics codebase.
• Expand our data warehouse with clean data ready for analysis.

Data Quality and Testing

• Apply advanced data testing strategies to ensure resulting datastores are aligned with expected business logic.
• Implement validation checks and automated testing procedures to manage data quality in your ETL/ELT pipelines.

Collaboration and Communication

• Work with stakeholders to define business logic and data expectations.
• Help drive a change in the usage of data by actively surfacing insights to stakeholders.
• Lead initiatives and problem definition, scoping, design, and planning.

Infrastructure and Automation

• Build tools and automation to run data infrastructure.
• Manage large-scale data migrations in relational datastores.

Why you're a great fit:

• 8+ Years experience working with data in a software environment. 
• Required Skills: Mastered proficiency in SQL and Python; advanced experience managing business semantic layer tooling, data catalog tooling and  data integrity testing frameworks. Experience with dbt orchestration and best practices.
• You have a track record of working autonomously and proactively, with deep domain knowledge of data systems.
• Required Tech Stack: SQL, Python, relational datastores, DAG tooling (like Dagster or Airflow), dbt and Tableau.
• Experience with cloud-based infrastructure (AWS, GCP, Terraform) and document, graph, or schema-less datastores.

Please note that given the nature of the contract, this role will not be eligible to participate in company-sponsored benefits

About Newsela:

Newsela is a leading education technology company dedicated to meaningful classroom learning for every student. We deliver integrated, AI-powered solutions designed to unlock student engagement, empower teachers, and drive meaningful learning outcomes. Our suite of products supports knowledge and skill development, writing practice, daily instruction, assessment, and data-informed decision-making across K–12 classrooms. Grounded in learning science research, Newsela’s solutions integrate content, assessment, and analytics to help educators track progress, understand student outcomes, and deliver high-impact instruction that supports every learner.

#LI-Remote

The salary range for this role is negotiable, the range being $6,000-$12,500 per month (Gross in USD), based on location and experience level.  

About Sezzle:

With a mission to financially empower the next generation, Sezzle is revolutionizing the shopping experience beyond payments, blending cutting-edge tech with seamless, interest-free installment plans that make shopping smarter and more accessible. We’re not just transforming payments; we’re redefining how people discover, interact with, and purchase the things they love while driving real impact on merchant sales through increased conversions and higher order values. As we continue to shape the future of fintech and retail, we’re building an innovative, dynamic team passionate about creating more than just a transaction but a truly unique shopping journey. If you’re excited about pushing boundaries in tech and delivering a game-changing experience for consumers and merchants alike, come join us at Sezzle and help create the future of shopping!

Compensation:

For this principal development role, with 12+ years of experience, the compensation range is  $6,000 - $12,500 USD based on location and experience level per month and in gross amount. This range acknowledges the extensive expertise, leadership capabilities, and significant contributions expected at this level, offering a competitive salary to reflect the value of advanced skills and experience

About the Role: 

We are seeking a talented and motivated, best-in-class Principal Engineer for our Data Infrastructure. This role presents an exciting opportunity to thrive in a dynamic, fast-paced environment within a rapidly growing team, with abundant prospects for career advancement.

Sezzle is growing; our data generation and consumption is growing with us at an increasing scale. Data is extremely valuable, and we want to empower the business, the engineers, and the rest of the organization to be able to read, write, and analyze large volumes of it quickly and efficiently.

Sezzle is a heavy consumer of MySQL, Postgres, and Redshift, leveraging AWS tooling with Aurora RDS and DMS. We use DBT-based transformations and similar toolkits to populate a rapidly growing data lake that supports multiple data warehouses. It’s scaled well with us so far, and we’re looking to continue improving it, but we also want to step into new tooling and technologies.

What you’ll do: 

• End to end ownership of our database and data warehousing infrastructure, its KPIs and its SLAs, including MySQL, Postgres, CDC capture, and Redshift.
• Lead optimizations in database and query performance.
• ​​Build and maintain tooling to help manage and upgrade our data infrastructure, continually improving with migrations and optimizations.
• Partner with Development to ensure we ship solid queries against a battle-tested schema to meet the needs of our products and business.

• Help Sezzle evolve our data systems beyond the current scale and stack with whatever tooling is necessary to handle processing high volumes of writes and events while performing complex ETL.
• Evaluate and integrate new technologies, guiding the evolution of Sezzle’s data infrastructure.
• Help data engineers and analysts optimize Redshift and warehouse performance, including query tuning, modeling improvements, and cost management.

What We look for: 

• 12+ years of experience in DBA, SRE, or Data Engineering roles, with a strong track record of scaling production-grade systems.
• Deep expertise with MySQL, Postgres, AWS Redshift or similar products, including performance tuning, table design, and workload management.
• Advanced proficiency in SQL.
• Strong hands-on experience with data replication and ETL/ELT frameworks, especially DBT, AWS DMS, or similar tools.
• Strong understanding of data modeling, distributed systems, and warehouse/lake design patterns.
• Ability to work in a fast-paced, collaborative environment with excellent communication and documentation skills.

Preferred Knowledge and Skills:

• Prior experience in high-growth, data-intensive fintech or similar regulated environments.
• Knowledge of lakehouse architectures and modern stacks such as Snowflake, Databricks, Iceberg, or Delta Lake.
• Some background in designing scalable, fault-tolerant data pipelines using modern orchestration tools (Airflow, Dagster, Prefect, etc.) processing anywhere from 100GB to 1 TB of new data a day
• Familiarity with streaming technologies (Kafka, Kinesis, Flink, Spark Streaming).
• High enthusiasm for using AI to be as productive as possible.

About You: 

• You have relentlessly high standards - many people may think your standards are unreasonably high. You are continually raising the bar and driving those around you to deliver great results. You make sure that defects do not get sent down the line and that problems are fixed so they stay fixed.
• You’re not bound by convention - your success—and much of the fun—lies in developing new ways to do things
• You need action - speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk-taking.
• You earn trust - you listen attentively, speak candidly, and treat others respectfully.
• You have backbone; disagree, then commit - you can respectfully challenge decisions when you disagree, even when doing so is uncomfortable or exhausting. You have conviction and are tenacious. You do not compromise for the sake of social cohesion. Once a decision is determined, you commit wholly.
• You deliver results - you focus on the key inputs and deliver them with the right quality and in a timely fashion. Despite setbacks, you rise to the occasion and never settle.

Sezzle’s Technology Stack:

• Languages: Golang, Typescript, Python
• Frontend: Typescript - React and React Native
• Backend: Golang
• Database: MySQL, Postgres, Elasticsearch
• DevOps & Cloud: AWS, Kubernetes
• Version Control: Git
• CI/CD: Gitlab
• Testing: Developer and AI-driven, focus on automated end-to-end, integration, and unit tests
• Open Source: Sezzle is focused on using open source, and we build what we can before buying!

What Makes Working at Sezzle Awesome? 

At Sezzle, we are more than just brilliant engineers, passionate data enthusiasts, out-of-the-box thinkers, and determined innovators; we are skilled musicians, yogis, cyclists, chefs, golfers, dog-lovers, and rock-climbers. We believe in surrounding ourselves with not only the best and the brightest individuals, but those that are unique and purpose-driven in all that they do. Our culture is not defined by a certain set of perks designed to give the illusion of the traditional startup culture, but rather, it is the visible example living in every employee that we hire.

#Li-remote #full-time

Important Information

Experience: +6 years

Job Mode: Full-time

Work Mode: Remote

ID: 21205

Job Summary

Supporting the analytics platform across GCP (BigQuery) and Tableau development. Focus is on data pipeline stability, reporting creation and maintenance, and light migration support from legacy systems (Hive/Presto and Tableau). Work is SLA-based, following defined SOPs from onshore teams. 

Key Responsibilities 

1. GCP Data Support (BigQuery)

• Create, maintain, and update tables/datasets in BigQuery  

• Support migration of tables from Hive/Presto to GCP  

• Convert and validate SQL logic (Hive/Presto → BigQuery)  

• Ensure data accuracy and consistency post-migration  

• Maintain curated datasets for reporting use  

1. Tableau Reporting & Migration

• Build and maintain Tableau dashboards on BigQuery datasets  

• Support migration of dashboards to a new Tableau instance  

• Perform validation and regression testing post-migration  

• Ensure dashboard accuracy, refresh stability, and performance  

1. Data Ingestion & Stability

• Support automated data ingestion pipelines into GCP  

• Monitor scheduled jobs and resolve ingestion failures  

• Troubleshoot data issues and coordinate fixes with Data Engineering  

• Ensure data availability meets SLA requirements  

1. Production Support

• Handle BAU fixes, data updates, and dashboard changes  

• Follow SLA timelines for reporting and migration tasks  

• Provide status updates and escalate issues as needed  

Required Skills 

• 2–5 years in BI / data engineering support roles  

• Strong SQL (BigQuery + Hive/Presto exposure preferred)  

• Experience with GCP (BigQuery)  

• Hands-on Tableau dashboard development & maintenance  

• Experience with data validation and reconciliation  

• Exposure to data migration projects (preferred)  

Preferred Skills 

• Experience with Freight & Logistics data 

• GTM Business exposure 

• Python (basic automation)  

• Airflow / workflow orchestration tools  

• Tableau migration experience  

• Performance tuning in BigQuery  

Success Metrics 

• Stable and accurate production datasets in GCP  

• Successful Hive/Presto → BigQuery migration support  

• Smooth Tableau dashboard migration with no regression  

• SLA adherence for BAU and migration tasks  

• Minimal data defects in reporting outputs  

About Encora 

Encora is a global company that offers Software and Digital Engineering solutions. Our practices include Cloud Services, Product Engineering & Application Modernization, Data & Analytics, Digital Experience & Design Services, DevSecOps, Cybersecurity, Quality Engineering, AI & LLM Engineering, among others. 

At Encora, we hire professionals based solely on their skills and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality. 

About Us

SimplePractice is headquartered in Los Angeles, California, but we have team members who work and live across the United States, Dominican Republic, Mexico City, and Ukraine.

At SimplePractice, we are improving access to quality care by equipping health and wellness clinicians with all the tools they need to thrive in private practice. 

More than 250,000 providers trust SimplePractice to build their business through our industry-leading software with powerful tools that simplify every part of practice management. From admin work to clinical care, our suite of innovative solutions work together to reduce administrative burden—empowering solo and small group practitioners to thrive alongside their clients. 

Award-winning and people-first, SimplePractice is shaping the future of health tech.  Recognized by MedTech Breakthrough, the Digital Health Awards, Best Places to Code in Mexico and Built In's Best Places to Work.

Our Culture

At SimplePractice, culture is our foundation. It influences the way we work, how we serve our customers, and how we approach accomplishing our mission. We have five core values that we strive to embody every day:

• We think big
• We take simplicity seriously
• We come as we are
• We act with humility
• We are built on trust

Culture is everyone’s responsibility at SimplePractice. Our culture is what drives us to do better for our teammates and customers.

Connection and collaboration are also key to our success. You will work with our talented multi-national teams and have opportunities to participate in onsites in both the US and Mexico.

The Role

We're looking for a Senior Data Engineer to help lead the evolution of our data stack—from pipelines to platform. In this role, you'll build the infrastructure that powers everything from product intelligence to financial reporting and self-serve analytics in product

Our customers are clinicians in small to mid-sized private practices, and the data you shape will directly help them run more efficient, effective businesses. By enabling accessible insights—from no-show trends to client engagement—you’ll give practitioners the tools to make smarter, faster decisions about how they manage care.

Internally, your work will fuel everything from executive reporting and revenue forecasting to product analytics and AI evaluation loops. You’ll collaborate across Data Analytics, ML, Product, and Engineering teams to build scalable, reliable systems that make data a first-class product at SimplePractice.

Responsibilities

• Partner with Product, Analytics and Engineering to build scalable systems that help unlock the value of data from a wide range of sources such as backend databases, event streams, and marketing platforms
• Lead technical vision and architecture with holistic point of view on both short-term and long-term horizons 
• Work with analytics to create company wide alignment through standardized metrics across the company
• Work with Product and Engineering teams to support internal use cases such as financial reporting, product analytics and operational metrics
• Enable external use cases like customer-facing dashboard, self-serve analytics, and next best action in product 
• Manage the complete data stack from ingestion through data consumption
• Build tools to increase transparency in reporting company wide business outcomes
• Work with DevOps to deploy and maintain data solutions leveraging cloud data technologies, preferably in AWS
• Help define data quality and data security framework to measure and monitor data quality across the enterprise. Define and promote data engineering best practice

Desired Skills & Experience

• Education & Experience
  • BS/MS in Engineering, Computer Science, Mathematics, or related field
  • 7+ years in Data or Analytics Engineering
  • Strong problem-solving and communication skills; comfortable in fast-paced, cross-functional environments
• Data Engineering & Pipelines
  • Enterprise architecture and enterprise data architecture (data modeling and enterprise dimensional modeling)
  • Expert in SQL and data modeling (relational, dimensional, semantic)
  • Proven experience in data warehouse design, implementation, and maintenance (Snowflake)
  • Hands-on with DBT for modular, testable transformations
  • Experience with orchestration and ingestion tools: Airflow, Prefect, Airbyte, Fivetran, Kafka
  • Familiar with ELT, schema-on-read, DAGs, and performance optimization
• Cloud & Infrastructure
  • Experience with AWS (S3, RDS, Redshift, etc.)
  • Familiar with Terraform, Docker, and containerized workflows (bonus)
  • Skilled in handling structured, semi-structured (e.g., JSON), and columnar formats (e.g., Parquet, ORC)
• Analytics & Enablement
  • Experience building and supporting semantic layers for self-serve analytics
  • Proficient with BI tools like Looker, Tableau, or Sisense
  • Comfortable standardizing metrics and enabling trusted, consistent access to data
• Programming & Scripting
  • Proficient in Python and Unix/Linux scripting
  • Comfortable working with APIs (e.g., using curl)

Bonus Points

• AWS DevOps - Terraform, Kubernetes, Docker
• Project & Change Management skills especially experience working in an Agile (SCRUM, Kanban) environment/team focusing on sprint by sprint deliveries
• Real-time ETL - Kafka streaming, AWS Kinesis

Benefits

We offer a competitive benefits program including:

• Privatized Medical, Dental & Vision Coverage
• Work From Home stipend 
• Flexible Time Off (FTO), wellbeing days, paid holidays, and Summer Fridays
• Monthly Meal Reimbursement
• Holiday Bonus, 15-day Aguinaldo
• Hybrid Work Schedule & Catered Lunch
• A relocation bonus for candidates joining us from a different city 
• Employee Resource Groups (ERGs)

California Job Applicant Privacy Notice

Thank you for your interest in opportunities at SimplePractice LLC (“SimplePractice” or “us” or “we” or “our”). Please note that when you submit your resume or application materials to us for employment purposes, you are subject to the SimplePractice California Job Applicant Privacy Notice. 

For more information about our privacy practices, please contact us at privacy@simplepractice.com.

Sobre Nosotros

SimplePractice tiene su sede en Los Ángeles, California, pero contamos con miembros del equipo que viven y trabajan en Estados Unidos, República Dominicana, Ciudad de México y Ucrania.

En SimplePractice, trabajamos para mejorar el acceso a una atención de calidad, brindando  a los profesionales de la salud y el bienestar todas las herramientas que necesitan para prosperar en la práctica privada.

Más de 250,000 proveedores confían en SimplePractice para desarrollar su negocio con nuestro software líder en la industria, que ofrece potentes herramientas que simplifican cada aspecto de la gestión de la práctica. Desde las tareas administrativas hasta la atención clínica, nuestro conjunto de soluciones innovadoras trabaja de manera integrada para reducir la carga administrativa, permitiendo a los profesionales, tanto individuales como en pequeños grupos, prosperar junto con sus clientes.

Ganador de premios y centrado en las personas, SimplePractice está dando forma al futuro de la tecnología de la salud. Reconocido por MedTech Breakthrough y los Premios de Salud Digital, y como uno de los Mejores Lugares para Programar en México y uno de los Mejores Lugares para Trabajar según BuiltIn.

Nuestra Cultura

En SimplePractice, la cultura es nuestra base. Influye en la manera en que trabajamos, cómo servimos a nuestros clientes y cómo abordamos el cumplimiento de nuestra misión. Contamos con cinco valores fundamentales que buscamos encarnar cada día:

• Pensamos en grande

• Tomamos la simplicidad en serio

• Venimos tal como somos

• Actuamos con humildad

• Estamos construidos sobre la confianza

La cultura es responsabilidad de todos en SimplePractice. Es lo que nos impulsa a hacerlo mejor para nuestros compañeros y clientes.

La conexión y la colaboración también son clave para nuestro éxito. Trabajarás con equipos multinacionales talentosos y tendrás oportunidades de participar en encuentros presenciales tanto en EE. UU. como en México.

🚀 ¡Encuéntranos en la Ciudad de México!

¿Quieres conocer al equipo antes de postularte? Acompáñanos el 28 de mayo en nuestro encuentro "Más allá de la app: Construyendo el futuro de la tecnología con IA y datos", en colaboración con RubyCDMX.

¡Habla de IA, datos e ingeniería con nuestros líderes mientras disfrutas de una bebida! 🍺 👉 Confirma tu asistencia aquí: https://luma.com/32jeyopv

El Rol

Estamos buscando un Senior Data Engineer para ayudar a liderar la evolución de nuestro stack de datos, desde los pipelines hasta la plataforma. En este rol, construirás la infraestructura que impulsa todo, desde inteligencia de producto hasta reportes financieros y analítica self-serve dentro del producto.

Nuestros clientes son profesionales clínicos en prácticas privadas pequeñas y medianas, y los datos que estructures les ayudarán directamente a operar negocios más eficientes y efectivos. Al habilitar insights accesibles, como tendencias de inasistencias o niveles de interacción de pacientes, brindarás a los profesionales herramientas para tomar decisiones más inteligentes y rápidas sobre la gestión de la atención.

Internamente, tu trabajo impulsará desde reportes ejecutivos y proyecciones de ingresos hasta analítica de producto y ciclos de evaluación de modelos de IA. Colaborarás con equipos de Data Analytics, Machine Learning, Producto e Ingeniería para construir sistemas escalables y confiables que conviertan los datos en un producto de primera clase dentro de SimplePractice.

Responsabilidades

• Colaborar con Producto, Analytics e Ingeniería para construir sistemas escalables que desbloqueen el valor de los datos provenientes de múltiples fuentes como bases de datos backend, flujos de eventos y plataformas de marketing.
• Liderar la visión técnica y la arquitectura con una perspectiva integral tanto a corto como a largo plazo.
• Trabajar con el equipo de Analytics para crear alineación a nivel compañía mediante métricas estandarizadas.
• Apoyar casos de uso internos como reportes financieros, analítica de producto y métricas operativas.
• Habilitar casos de uso externos como dashboards para clientes, analítica self-serve y recomendaciones dentro del producto.
• Gestionar el stack completo de datos desde la ingestión hasta el consumo.
• Construir herramientas que aumenten la transparencia en el reporte de resultados de negocio.
• Colaborar con DevOps para desplegar y mantener soluciones de datos en la nube, preferentemente en AWS.
• Definir marcos de calidad y seguridad de datos para medir y monitorear la calidad en toda la organización, promoviendo mejores prácticas de ingeniería de datos.

Habilidades y Experiencia Deseadas

Educación y Experiencia

• Licenciatura o Maestría en Ingeniería, Ciencias de la Computación, Matemáticas o campo relacionado.
• Más de 7 años en Data Engineering o Analytics Engineering.
• Fuertes habilidades de resolución de problemas y comunicación; cómodo trabajando en entornos dinámicos y multifuncionales.

Ingeniería de Datos y Pipelines

• Experiencia en arquitectura empresarial y modelado dimensional.
• Experto en SQL y modelado de datos (relacional, dimensional, semántico).
• Experiencia comprobada en diseño, implementación y mantenimiento de data warehouses (Snowflake).
• Experiencia práctica con DBT para transformaciones modulares y testeables.
• Experiencia con herramientas de orquestación e ingestión: Airflow, Prefect, Airbyte, Fivetran, Kafka.
• Familiaridad con ELT, schema-on-read, DAGs y optimización de rendimiento.

Nube e Infraestructura

• Experiencia con AWS (S3, RDS, Redshift, etc.).
• Familiaridad con Terraform, Docker y flujos de trabajo contenerizados (deseable).
• Manejo de datos estructurados, semi-estructurados (JSON) y formatos columnares (Parquet, ORC).

Analítica y Habilitación

• Experiencia construyendo y soportando capas semánticas para analítica self-serve.
• Dominio de herramientas BI como Looker, Tableau o Sisense.
• Experiencia estandarizando métricas y habilitando acceso confiable y consistente a los datos.

Programación y Scripting

• Dominio de Python y scripting en Unix/Linux.
• Experiencia trabajando con APIs (por ejemplo, usando curl).

Puntos Extra

• AWS DevOps (Terraform, Kubernetes, Docker).
• Experiencia en gestión de proyectos y cambio, especialmente en entornos ágiles (SCRUM, Kanban).
• Experiencia en ETL en tiempo real (Kafka streaming, AWS Kinesis).

Beneficios

Ofrecemos un programa competitivo de beneficios que incluye:

• Seguro médico, dental y de visión privado
• Apoyo para trabajo desde casa
• Tiempo libre flexible (FTO), días de bienestar, días festivos pagados y Summer Fridays
• Reembolso mensual de alimentos
• Bono navideño y 15 días de aguinaldo
• Esquema híbrido de trabajo y comidas patrocinadas
• Bono de reubicación para candidatos que se muden desde otra ciudad
• Grupos de Recursos para Empleados (ERGs)

Type: Contract, per-project.
Location: Remote — within LATAM with ET ±1 hour
Availability: Contractor (40 hours per week)

Job Title: Data / Platform Engineer (AWS & Data Pipelines)

We are looking for a highly motivated Data / Platform Engineer to join our team and help design, build and operate scalable data pipelines and cloud-based solutions.

In this role, you will work closely with engineering and product teams to build both streaming and batch data pipelines, contribute to system design, and help drive automation and monitoring across our data platform.

Key Responsibilities

• Design, build, maintain and primarily operate scalable streaming and batch data pipelines, with a strong focus on maintenance, monitoring, troubleshooting and continuous improvement of existing pipelines.

• Work with AWS services, including Redshift, EMR and ECS, to support data processing and analytics workloads.

• Develop and maintain data workflows using Python and SQL.

• Orchestrate and monitor pipelines using Apache Airflow.

• Build and deploy containerized applications using Docker and Kubernetes.

• Break down high-level system designs into well-defined, deliverable tasks with realistic estimates.

• Collaborate with cross-functional teams in a fast-paced and distributed environment across the US and Europe.

• Drive automation, observability and monitoring to improve reliability, performance and operational efficiency.

• Support knowledge transfer and ownership handover as part of the planned transition to the consuming team.

Required Qualifications

• Strong professional experience with Python and SQL.

• Hands-on experience with AWS, specifically Redshift, EMR and ECS. AWS experience is mandatory (other cloud providers are not considered equivalent for this role).

• Proven experience building and operating both streaming and batch data pipelines.

• Professional experience with Apache Airflow, Docker and Kubernetes.

• Ability to translate high-level system designs into actionable technical tasks and realistic estimates.

• Comfortable working in dynamic and fast-paced environments and in distributed teams.

• Strong interest in automation and monitoring.

• Strong hands-on experience with Apache Spark.

• Senior-level profile with strong autonomy, communication skills and ability to work effectively in distributed teams.

• Proven ability to transfer knowledge and support ownership handovers.

• Fluent or professional working proficiency in English (both written and spoken).

Nice to Have

• Previous experience in the telecom industry.

• Experience with machine learning systems and/or event-driven architectures.

• Experience with Apache Iceberg.
  
  (*) SOUTHWORKS only hires individuals from countries that are not blocked or sanctioned by the United States, including those identified on the United States Office of Foreign Asset Control (OFAC).

The Mission

This is a senior role for someone who leads from both directions at once — deeply technical on customer engagements, and fully accountable for the growth and performance of a team of ML engineers and architects. You will report to the Director of AI/ML.

You own hiring, development, and team health alongside leading complex customer engagements, shaping architecture, and driving pre-sales. Both parts of this job are real and ongoing. The right candidate will find energy in that combination, not tension.

Your Assignment

Leading Your Team

• Hire and build: Set the technical bar for ML roles on your team, lead or oversee technical assessments, and make hiring decisions you can stand behind. Build a team that raises the practice's overall standard.
• Develop people: Run regular structured 1:1s, provide candid feedback at meaningful milestones, and actively invest in each person's growth — whether they are early in their career or highly experienced.
• Manage performance: Recognize strong contributors and address performance gaps directly and early. Partner with HRBPs and the Director of AI/ML when situations require a structured path, and advocate for your team when they deserve it.
• Stay close to staffing: Understand how your team is utilized across engagements, keep the staffing team informed of each person's skills evolution and preferences, and ensure people are placed in work that stretches them appropriately.

Strategic Advisory

• Lead ML assessments: Evaluate customer environments end-to-end — infrastructure, data pipelines, model lifecycle, and organizational readiness — and produce recommendations that drive executive decisions and open the door to the next engagement.
• Shape architecture: Serve as the senior technical authority on engagements, setting architectural direction, ensuring technical quality across the team, and making the calls that matter when tradeoffs are hard.
• Advise on ML operations: Help customers build ML systems they can actually own and sustain — translating MLOps, LLMOps, and production monitoring complexity into standards their engineering teams can execute and their leadership can act on.
• Drive pre-sales: Partner with sales and solutions teams during scoping and proposal phases, contributing the technical depth needed to scope work accurately and give prospects confidence in Caylent's ability to deliver.

Hands-On Delivery

• Lead engagements end-to-end: Drive architecture and solution design from kickoff through delivery — setting technical direction, unblocking the team on hard problems, and ensuring the work meets Caylent's quality standards.
• Own the technical relationship: Depending on the engagement, you are either the primary client contact owning all architect-level outcomes, or the senior technical authority providing oversight across the team. The expectation is the same in both cases — you are the person the engagement depends on technically.

Growing the Practice

• • Raise the bar internally: Mentor engineers and architects through real work, contribute to technical interviews, and build reference architectures and accelerators that make the broader ML practice better.

Your Qualifications (non-negotiables)

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Demonstrated people management experience — hiring, performance calibration, career development, and the ability to have difficult conversations directly and constructively.
• Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
• Deep expertise in at least two or three ML domains — whether classical ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
• Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards that engineering teams can execute and executives can act on.
• Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization, and distributed training — combined with RAG and agentic system design, including multi-agent architectures, MCP integration, and human-in-the-loop patterns on AWS.
• Proven ability to operate independently in complex, ambiguous customer environments — navigating competing priorities, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.

Strong differentiators

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
• Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
• Exposure to varied industries and problem types in a consulting or client-facing context.
• Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments.
• Fluency in AIOps patterns — designing agentic workflows for anomaly detection, automated root cause analysis, and remediation across observability platforms — and the ability to translate AI operations outcomes into measurable business value for customers.

Technical Stack

Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the experience to know how open-source tooling strengthens it.

• ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI
• AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation)
• Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts
• AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly
• Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue
• Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate
• Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama
• MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness
• LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation
• Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo

Benefits

• 100% remote work
• Medical Insurance for you and eligible dependents
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

NEORIS es un acelerador Digital que ayuda a las compañías a entrar en el futuro, teniendo 20 años de experiencia como Socios Digitales de algunas de las mayores compañías del mundo. Somos más de 4,000 profesionales en 11 países, con nuestra cultura multicultural de startup en donde cultivamos innovación, aprendizaje continuo para crear soluciones de alto valor para nuestros clientes.

Estamos en búsqueda de Arquitecto / Ingeniero de Datos  - Datalake,

Principales responsabilidades:

• Diseñar arquitecturas de Data Lake escalables y gobernadas.
• Definir e implementar frameworks de ingesta de datos (ETL/ELT) estandarizados.
• Modelado y almacenamiento (zonas: raw, curated, trusted).
• Catalogación y linaje de datos.
• Exposición y consumo (APIs, BI, data sharing).
• Implementar controles de calidad de datos, validaciones y monitoreo.
• Asegurar cumplimiento de políticas de gobierno, seguridad y regulación (data privacy, auditoría, trazabilidad).
• Optimizar costos y performance en entornos AWS.Colaborar con equipos de negocio, arquitectura y seguridad para alinear soluciones a estándares empresariales.

Requerimientos:

• Experiencia requerida: +3-5 años en Data Engineering / Data Architecture.
• Experiencia comprobable diseñando e implementando Data Lakes productivos en AWS.
• Participación en proyectos con requerimientos normativos/regulatorios (financiero, retail, telecom, etc.).
• Experiencia definiendo estándares y mejores prácticas (no solo implementación técnica).
• Trabajo con grandes volúmenes de datos y múltiples fuentes (estructurados y no estructurados).
• Stack tecnológico (AWS)
• Almacenamiento: Amazon S3
• Procesamiento: AWS Glue, AWS Lambda, Amazon EMR
• Orquestación: AWS Step Functions / MWAA (Airflow)
• Catálogo y gobierno: AWS Glue Data Catalog, AWS Lake Formation
• Consulta: Amazon Athena, Redshift Spectrum
• Streaming (deseable): Amazon Kinesis / MSK
• Seguridad: IAM, KMS, políticas de acceso
• Arquitecturas Lakehouse y patrones modernos de datos.
• Modelado de datos (dimensional y orientado a analítica).
• Data Governance (catálogo, linaje, calidad, ownership).
• Data Quality frameworks (validación, profiling, reglas).
• Optimización de costos en AWS (storage tiering, particionamiento).
• Buen entendimiento de seguridad y compliance de datos.

Ofrecemos:

• Esquema 100% Nominal
• Prestaciones de Ley
• Vales de despensa
• Beneficios adicionales
• Plan de desarrollo profesional
• Colaboración multicultural

Te invitamos a conocernos en http://www.neoris.com, Facebook, LinkedIn, Twitter o Instagram: @NEORIS.

Cynthia Valadez

#LI-CV1

The mission:

The Senior Credit Risk Data Scientist at Baubap is responsible for designing, implementing, and improving predictive models that directly impact our credit decisions and portfolio performance. This role will play a critical part in generating accurate forecasts, building data-driven methodologies, and continuously iterating based on real-world learning—always grounded in a deep understanding of the business context.

The expected outcome:

• Develop, deploy, and maintain machine learning models that improve the accuracy of forecasts across key business levers such as approval rate, disbursement rate, loss rate, and average loan amount.
• Deliver short-, mid-, and long-term forecasts for key portfolio and business indicators to guide strategic decision-making.
• Continuously improve models and data pipelines based on new insights, feedback loops, and shifts in portfolio dynamics.
• Build methodologies that reflect a clear understanding of the business and customer behavior—combining data science best practices with practical, real-world constraints.

The day to day tasks:

• Model development & deployment: Design and implement predictive models (classification, regression, time series, etc.) to forecast credit risk metrics and optimize decision-making.
• Model iteration & lifecycle management: Regularly retrain and improve models based on recent performance, business evolution, and new data availability.
• Forecasting: Build robust models to predict portfolio KPIs over different time horizons (daily/weekly/monthly), including loss rate, disbursed amount, average ticket size, and approval rate.
• Experimentation: Collaborate with cross-functional teams to design and evaluate A/B tests or quasi-experiments that inform modeling improvements.
• Feature engineering: Create high-quality, interpretable features from raw transactional and behavioral data.
• Data exploration & root-cause analysis: Use statistical techniques to detect anomalies, understand shifts in model performance, and identify risks or opportunities.
• Business alignment: Partner closely with Risk, Product, Finance, and Data Engineering teams to ensure that models and methodologies are aligned with business goals and operational realities.
• Documentation & reproducibility: Maintain clear documentation of models, assumptions, and decisions to ensure transparency, auditability, and future scaling.

Why YOU should apply:

• 5+ years of experience developing, validating, and deploying predictive models in a production environment, preferably within financial services or credit risk.
• Strong proficiency in SQL for data extraction and transformation.
• Advanced skills in at least one programming language commonly used in data science, such as Python or R.
• Proven ability to build and tune machine learning models (e.g., classification, regression, time series forecasting), using libraries such as scikit-learn, XGBoost, LightGBM, etc.
• Experience maintaining and iterating on models based on real-world performance and shifting data patterns.
• Comfortable working with experimentation frameworks, A/B testing, and validation pipelines.
• Ability to translate complex technical insights into clear business recommendations.
• Strong understanding of statistical concepts and their application in risk modeling.
• Fluent in English (written and spoken); able to work and communicate effectively with an international and cross-functional team.
• Bonus: experience in financial risk areas
• Bonus: experience working with version control tools (e.g., Git), workflow managers (e.g., Airflow), and cloud-based data platforms (e.g., AWS, GCP, or similar).

What we can offer:

• Being part of a multicultural, highly driven team of professionals
• 20 vacation days / year + 75% holiday bonus (Prima Vacacional)
• 1 month (proportional) of Christmas bonus (Aguinaldo)
• Food vouchers
• Health & Life insurance
• Competitive salary

Location: Remote - MX

ABOUT THE COMPANY:

Clari + Salesloft are building the next era of enterprise revenue — one where teams make confident decisions powered by AI and real signals. By combining our scale, insights, and AI innovation, we’re building the industry’s first Predictive Revenue System, enabling humans and AI to work together to make smarter decisions and drive consistent growth.

With thousands of customers using our platforms every day, we have an unmatched view into how revenue is actually won — the Revenue Context that reveals what happens, when, and with what outcome. This gives us a unique opportunity to transform an entire category and set a new benchmark for how modern revenue teams operate.

Join us to help transform how companies around the world run revenue — and build the platform that will guide leading revenue teams into the future.

THE OPPORTUNITY:

At Clari + Salesloft, our Data Engineer will be pivotal to our company’s success. You will be a key member of our fast-growing and high-performing Data & Analytics (D&A) team, helping build and maintain the data infrastructure that powers all of our internal reporting and analytics for multiple business teams. As a team, we are actively implementing the latest AI features and cutting-edge technologies to drive business insights.

On a day-to-day basis, you will be responsible for architecting and delivering end-to-end data pipelines to support internal stakeholders. Specifically, you will:

• Design, build, and maintain scalable data pipelines and services for analytics and business intelligence.
• Develop data flows and applications for machine learning, analytics, and third-party data integrations.
• Collaborate with data scientists and analysts to deploy models and analytical solutions.
• Perform heads-down development, test your code, review peer code, and ship it to production.

In addition to working with amazing colleagues who exemplify our ‘team over self’ core value, you will also have the opportunity to build and enhance the data infrastructure that provides critical insights to business leaders, directly impacting strategic decisions across the company. You will have an opportunity to make a difference.

WHAT WE’RE LOOKING FOR:

We are seeking a results-oriented and innovative Data Engineer who is passionate about building scalable data solutions that empower internal teams. You will play a pivotal role in developing the infrastructure that powers our internal reporting and analytics initiatives, and you should be excited about working with the latest in AI and data technology. We're looking for someone with a deep understanding of data architecture, a commitment to quality, and a collaborative spirit to support business stakeholders.

If you’re looking for an opportunity to learn more, do more, and become more, then becoming a Data Engineer is the career path for you!

THE TEAM:

Our  Clari + Salesloft Data & Analytics team is comprised of seasoned and up-and-coming data professionals​​ who are all aligned on one vision and mission:

• Vision: Fundamentally transform the way buyers and sellers drive repeatable outcomes
• Mission: Bring science to the art of sales 

The Data & Analytics team consists of a group of curious, collaborative, and technically-driven individuals dedicated to empowering the business with data. The engineers and analysts on our team share a few common traits: they are passionate about turning data into actionable insights, thrive on solving complex problems, and are committed to leveraging cutting-edge technology to answer critical business questions.

THE SKILL SET:

Core Qualifications

• Strong SQL skills and experience with Snowflake core features, focusing on performance optimization and efficient data warehousing practices.
• Proven ability to build scalable, tested transformation layers using DBT.
• Experience managing data pipelines with Airflow and ingestion tools such as Fivetran or Estuary.
• Comfortable working with AI-assisted development tools such as Snowflake Cortex, Gemini Enterprise, or GitHub Copilot.
• Strong Git workflow experience in Agile environments (Jira).
• Bachelor’s degree in Computer Science or a related technical field.

Nice-to-Have

• Experience with BI/analytics tools and supporting reporting workflows.
• Experience working with Salesforce or other CRM data, particularly in SaaS environments.
• Familiarity with AWS or GCP, REST APIs (Postman), or Databricks.
• Exposure to Machine Learning, AI, or Data Science frameworks.

At Clari + Salesloft, we are committed to creating an inclusive and supportive workplace where everyone belongs and can thrive. We focus on culture add, not culture fit, and believe our teams are made stronger by the unique perspectives, experiences, and identities each person brings.

We are proud to be an Equal Opportunity Employer and provide employment opportunities to all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, pregnancy, or any other characteristic protected by law.

If you’re excited about this role even though your experience may not perfectly match every requirement, we encourage you to apply. We are actively hiring across multiple geographies and would love to welcome passionate, curious, and mission-driven individuals to our growing team. Explore our open roles and consider joining us!

#LI-Remote

Please note that all official communication regarding job opportunities at Clari + Salesloft will come from an @clari.com  or @ salesloft.com email address. If you receive messages on LinkedIn or other job platforms claiming to be from Clari + Salesloft, they may not be legitimate. To verify the authenticity of any job-related communication, please visit our official Careers Page.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, notetaking, or summarizing responses. These tools assist our recruitment team but do not replace human judgment — all hiring decisions are made by people. If you would like more information about how your data is processed or prefer to opt out of any AI-assisted tools, please let your recruiter know. Opting out will not impact your experience or consideration.

We are looking for a hands-on Senior Data Engineer to join a small team (2 Data Engineers + 1 Data Analyst). You will help scale our AWS-based data infrastructure, working primarily with industrial data sources and high-volume time-series data.

Responsibilities Will Include:  

• Engage directly with end users to understand how they consume data, and design solutions that serve their needs.
• Design, develop, and maintain scalable ETL/ELT pipelines for batch and real-time processing, including high-frequency time-series ingestion.
• Evolve the Data Lakehouse—optimizing storage, performance, and data consistency.
• Manage orchestration workflows with complex dependencies and error handling.
• Implement data governance, security controls, and audit policies across the AWS ecosystem, leveraging services such as AWS Lake Formation for centralized data lake permissions and governance.
• Optimize performance and cost (FinOps); identify and resolve technical bottlenecks.
• Partner with the Data Analyst to deliver production-ready, analytics-optimized datasets.
• Build monitoring, alerting, and data quality testing for platform reliability.

Requirements:

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related discipline is required.
• At a minimum, 5+ years of experience in Data Engineering, Analytics Engineering, or related roles.
• Deep hands-on experience with core AWS data services: Glue, Athena, Step Functions, Lambda, Kinesis, and Lake Formation.
• Hands-on experience with Apache Iceberg (required).
• Expert proficiency in Python (PySpark / Python Shell) and advanced SQL (window functions, CTEs, execution plan tuning).
• Solid grasp of ETL/ELT tooling and dimensional modeling best practices (Kimball).
• Experience preparing data architectures for visualization tools and operational/management reporting.
• Practical experience with NoSQL engines (e.g., DynamoDB) and relational SQL databases.
• Infrastructure as Code: Terraform or AWS CloudFormation.

Preferred Experience:

• Apache Airflow (MWAA experience preferred) or native AWS orchestration.
• Familiarity with dbt, Airbyte, or Fivetran.
• Experience automating CI/CD for Glue jobs or Lambda deployments.

Soft Skills:

• Senior peer mindset—leads through code reviews and mentorship, not hierarchy.
• Pragmatic decision-maker who picks the right tool for cost, efficiency, and project needs.
• Strong ownership of end-to-end data quality and system availability.
• Clear communicator who can translate business needs into technical solutions. 

Note: This isn’t an active role right now, but we’re building a community of great talent for future opportunities at Caylent. If your background aligns with what we’re looking for, our team may reach out to learn more about you and explore potential future fits.

The Mission

At Caylent, a Senior Machine Learning Engineer works as an integral part of a cross-functional delivery team to design and document machine learning solutions on the AWS cloud for our customers.  We are looking for someone that has a strong understanding of the various model types and tools, and can help our customers connect their business goals with the details of feature design, model training and inference.  You will develop solutions designed by an architect.

You will participate in daily standup meetings with your team and bi-weekly agile ceremonies with the customer.  Your manager will have a weekly 1:1 with you to help guide you in your career and make the most of your time at Caylent.

Your Assignments

• Work with a team to deliver machine learning solutions on AWS for customers
• Participate in and contribute to daily standup meetings
• Develop and implement ML models, MLOps, and analytics
• Big data processing and preparation of training data for models

Your Qualifications

• Strong experience in building ML models for real world applications
• Strong experience in at least one of these:
• AWS ML Services/SageMaker
• ML libraries like Keras, Tensorflow, PyTorch, Scikit-learn
• MLOps tools such as MLflow, Kubeflow, Airflow
• Advanced analytics using time series forecasting and/or inferential statistics
• Strong experience in one or more of these data processing solutions:
• Big data processing platforms like Spark, Hadoop, or streaming platforms
• Data processing and cleansing using Python/Pandas, PySpark, Scala, SQL
• Strong understanding of feature definition, model meta-data, hyperparameter tuning, stochastic gradient descent, deep learning layer types and activation functions
• Experience in visualization using SageMaker, ggplot, matplotlib, or seaborn
• Experience with an IaC tool such as CloudFormation, Amazon CDK or Terraform
• Excellent written and verbal communication skills

Benefits 

• 100% remote work
• Medical Insurance for you and eligible dependents
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

This role may require up to 25% travel, depending on business needs.

The Opportunity

This is a senior technical client leadership role that blends deep hands-on ML expertise with strategic advisory and consulting skills. You will be the most experienced ML voice across a diverse and expanding book of customer engagements — from early-stage companies bringing ambitious ML ideas to market, to established enterprises modernizing how they build and operate AI systems on AWS.

You will shape strategy, influence architecture, and leave every team you touch better than you found it. You bring the scientific depth to design and evaluate models rigorously, the engineering depth to architect production ML systems at scale, and the consulting instincts to translate both into business value for customers.

If you have led the hard conversations, shaped the architecture decisions that mattered, and built the things others benchmark against — and you are looking to do that across a growing portfolio of varied and interesting customers — this is the role for you.

What You'll Do

• Lead end-to-end ML assessments across infrastructure, data pipelines, model lifecycle, and organizational readiness — producing recommendations that drive executive decision-making and earn Caylent the next engagement.
• Partner with sales and solutions teams through the proposal and scoping phase, contributing the technical depth needed to shape well-grounded statements of work.
• Serve as the senior technical authority on client engagements — possibly across multiple projects simultaneously — providing architectural guidance, ensuring technical quality from your project team members, and getting hands-on when the engagement demands it, without owning day-to-day implementation responsibilities.
• Own or orchestrate high-quality POCs that give customers confidence before committing to a larger initiative.
• Advise customers on ML operations standards and architecture — covering MLOps pipeline design, model lifecycle management, LLMOps patterns, and production monitoring frameworks — translating operational complexity into decisions and guardrails their teams can own and sustain.
• Shape how Caylent wins its most technically complex opportunities — contributing the architectural thinking and credibility that turns prospects into customers.
• Strengthen the ML practice from the inside — through peer guidance, technical interviews, and contributions to accelerators, reference architectures, and thought leadership content.

What You Bring

The non-negotiables

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
• Deep expertise in at least two or three ML domains — whether traditional ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
• Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards and decisions that engineering teams can execute and executives can act on.
• Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization (quantization, vLLM), and distributed training (DeepSpeed, FSDP) — combined with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS. Technical authority to prescribe the right approach and set architectural standards that teams can execute against.
• Proven ability to operate independently in complex customer environments — navigating ambiguity, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.

Strong differentiators

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
• Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
• Exposure to varied industries and problem types in a consulting or client-facing context.
• Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments.
• Hands-on experience designing and deploying SRE agents and AI-driven operations workflows in production — spanning automated incident detection, triage, and remediation — with the ability to integrate across observability platforms and translate AI operations outcomes into measurable business value.

Technical Stack

Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the experience to know how open-source tooling strengthens it.

ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI

AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation)

Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts

AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly

Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue

Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate

Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama

MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness

LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation

Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo

Benefits

• Data Pipeline Development: Support the design, development, and implementation of new data pipelines using GCP services like Data Fusion, Cloud Functions, and Dataproc to ingest data from a variety of sources. 
• SQL-Based Transformations: Author, execute, and optimize complex SQL joins and transformations within GCP BigQuery to clean, reshape, and structure raw data into high-quality, usable assets. 
• Data Modeling Support: Assist the senior engineering and analytics teams with data modeling and schema design, helping to implement the structures that support our BI and analytics use cases. 
• Pipeline Maintenance and Optimization: Monitor, troubleshoot, and enhance existing data pipelines to ensure data quality, reliability, and performance. 
• Collaboration and Implementation: Work as a key member of the engineering team to translate technical requirements into functional code and support the deployment of data solutions. 

• 2-4+ years of hands-on experience in a data engineering or similar role. 
• Strong, demonstrated proficiency in SQL for data manipulation and transformation is essential. 
• Hands-on programming experience with Python for data processing tasks. 
• Experience working within a major cloud environment (GCP is highly preferred; AWS, Azure are also valuable). 
• Experience working with complex, large-scale enterprise media data is a strong plus. 
• Familiarity with ETL/ELT principles and experience with data pipeline orchestration tools (e.g., Airflow, Data Fusion). 
• Understanding of data warehousing concepts and database design. 
• A collaborative mindset with strong problem-solving skills and attention to detail. 
• Bachelor's degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience. 

For Mexico: This position is available in a Remote or Hybrid Work Modality. The candidate must perform their work from any location within Mexico. Only Mexican nationals residing in the country or foreign nationals with a valid work permit may apply. Remote work from abroad is not permitted under any circumstances.

For Colombia: This position is in a Remote or Hybrid Work Modality and must be performed within Colombian territory. Only candidates residing in Colombia with a valid work permit may apply. Remote work contracts do not allow employment or relocation abroad.