join9am Staff Data Engineer responsible for owning and developing the data platform at 9amHealth using Python and AWS. Leads architecture, technical standards, and builds scalable ETL pipelines to support data needs and AI/ML initiatives.
Responsibilities
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.
Our entire data warehouse, ETL, and reporting stack is built in Python on AWS, so deep Python experience is essential. You'll write production-quality Python and SQL, set the architecture and technical standards the team builds on, and stay comfortable working hands-on with AWS services (e.g., Lambda, 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 (e.g., views, stored procedures) in Redshift.
Operate and optimize AWS data infrastructure (e.g., Glue, S3, Redshift, 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.
Qualification
Expert-level PythonExpert-level SQLExperience handling sensitive dataExperience in health tech
Required
10+ years of professional experience in data engineering or a hybrid data/software engineering role.
Strong data or software engineering background: able to write, test, debug, and ship production code independently, using practices like code review and CI/CD.
Expert-level Python: deep experience architecting production data pipelines, ETL logic, and reporting systems. Python is our primary language, but we value engineers whose depth extends to other languages relevant to modern data engineering (Java, Scala, Go, etc.) and who can make sound tooling decisions, not just execute in one stack.
Expert-level SQL: window functions, CTEs, recursive queries, and query optimization at scale.
Hands-on experience with cloud data services, ideally AWS (e.g., Glue, S3, Redshift, Athena), including building and operating production data pipelines on them.
Production experience with a cloud data warehouse, ideally Amazon Redshift, including schema design and performance tuning.
Experience handling sensitive data, such as PHI, with appropriate access controls and compliance practices.
Solid understanding of data modeling techniques: star/snowflake schemas, slowly changing dimensions, event-based models.
Demonstrated experience setting technical direction and standards that other engineers and teams build on.
Professional use of AI-assisted development tools, with sound judgment on correctness, security, and review.
Ability to explain technical trade-offs and risks clearly to both technical and non-technical stakeholders, including executives.
Preferred
Professional-level cloud certification (e.g., AWS Solutions Architect – Professional, Google Cloud Professional Data Engineer, or Azure Solutions Architect Expert), or equivalent hands-on experience.
Experience in health tech, digital health, or another regulated industry.
Experience with CI/CD for data assets (dbt CI, Great Expectations, or similar).
Background building or contributing to AI/ML features: feature stores, training pipelines, model serving, or RAG architectures.
Comfort with infrastructure-as-code (Terraform, CloudFormation) and containerized deployments (Docker, ECS/EKS).
Prior experience in a startup or high-growth environment, owning outcomes end to end.
A track record of improving developer experience and productivity through tooling, automation, or process improvements, and of influencing engineering practice beyond your immediate team.
Experience with orchestration tools and platforms such as Apache Spark, Airflow, or equivalent.
