About 9amHealth

9amHealth is an AI-enabled virtual specialty care platform focused on managing high-cost chronic conditions at scale. The company partners with employers, health plans, and pharmacy benefit managers to deliver comprehensive, cost-effective medical care for individuals living with obesity, diabetes, hypertension, and dyslipidemia. Members receive access to specialized clinicians, including endocrinologists, obesity medicine specialists, and clinical pharmacists, at-home lab testing, prescription medications, and lifestyle support.

9amHealth was founded in 2021 and is backed by leading healthcare investors like Define Ventures, SemperVirens, 7Wire Ventures, and The Cigna Group Ventures.

About The Role

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.

What It Is

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.

Raise the team's technical standards directly, 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.

What You Need

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.