Staff Data Engineer

• As a Staff Data Engineer, you’ll sit within the data team and report to the VP of Data. You’ll own the data platform that powers the business’s current data needs and help shape where it goes next
• The data warehouse, ETL, and reporting stack is built in Python on AWS. You’ll write production-quality Python and SQL, set the architecture and technical standards the team builds on, and work hands-on with AWS services such as Lambda and Glue and BI tools when needed
• Define and own the architecture, technical standards, and coding practices for the data platform
• Evaluate and recommend data technologies, and contribute to the platform roadmap, including performance and cost optimization
• Design, build, and maintain scalable ETL/ELT pipelines in Python using AWS Glue
• Drive the analytical data models and semantic layer that power self-service BI and advanced analytics
• Build integrations between the data platform and internal or third-party systems, contributing backend code where needed
• Set the technical bar for AI-assisted development practices across the data team (GitHub Copilot, Claude, Cursor, or similar)
• Author, optimize, and maintain complex SQL and database objects, such as views and stored procedures, in Redshift
• Operate and optimize AWS data infrastructure, such as Glue, S3, Redshift, and Athena
• Own data quality, governance, and reliability for the platform, including monitoring, alerting, and incident response
• Support AI/ML initiatives by partnering with data science and ML teams on data prep, feature engineering, and model monitoring
• Raise the team’s technical standards through code review, architecture decisions, and hands-on mentorship of the data analyst and data engineer
• Partner with clinical operations, product, finance, and engineering to translate ambiguous business questions into reliable, well-documented data products

Benefits

• Health, vision, and dental plans
• 20 vacation days (for full-time employees)
• Flexible, hybrid working model

• Expert-level Python experience architecting production data pipelines, ETL logic, and reporting systems. Python is the primary language, and depth in other languages relevant to modern data engineering (such as Java, Scala, or Go) and sound tooling decisions are valued
• Ability to explain technical trade-offs and risks clearly to technical and non-technical stakeholders, including executives
• Professional use of AI-assisted development tools, with sound judgment on correctness, security, and review
• Demonstrated experience setting technical direction and standards that other engineers and teams build on
• Expert-level SQL, including window functions, CTEs, recursive queries, and query optimization at scale
• A solid understanding of data modeling techniques, including star and snowflake schemas, slowly changing dimensions, and event-based models
• Production experience with a cloud data warehouse, ideally Amazon Redshift, including schema design and performance tuning
• A strong data or software engineering background, with the ability to write, test, debug, and ship production code independently using practices such as code review and CI/CD
• Hands-on experience with cloud data services, ideally AWS (such as Glue, S3, Redshift, or Athena), including building and operating production data pipelines on them
• 10+ years of professional experience in data engineering or a hybrid data/software engineering role
• Experience handling sensitive data, such as PHI, with appropriate access controls and compliance practices
• Experience with CI/CD for data assets (such as dbt CI, Great Expectations, or similar)
• A track record of improving developer experience and productivity through tooling, automation, or process improvements, and of influencing engineering practice beyond your immediate team
• Professional-level cloud certification (such as AWS Solutions Architect – Professional, Google Cloud Professional Data Engineer, or Azure Solutions Architect Expert), or equivalent hands-on experience
• A background building or contributing to AI/ML features, such as feature stores, training pipelines, model serving, or RAG architectures
• Prior experience in a startup or high-growth environment, owning outcomes end to end
• Experience in health tech, digital health, or another regulated industry
• Experience with orchestration tools and platforms such as Apache Spark, Airflow, or equivalent
• Comfort with infrastructure-as-code (such as Terraform or CloudFormation) and containerized deployments (such as Docker, ECS, or EKS)
• Familiarity with BI platforms and semantic layers, such as Looker/LookML, in support of self-service analytics