About the role
This is a fully remote role within the United States. We’re looking for an experienced data engineering professional to join us in our mission to eliminate the financial complexity of healthcare. You'll join our Data Science Engineering team to own the data lifecycle of healthcare claims data including ELT/ETL, de-identification, PHI/PII reduction, data quality, observability, and integrations (e.g., APIs, web-optimized data stores).
Responsibilities Build and maintain reliable data pipelines that process raw claims data from diverse sources to enriched, standardized formats using tools including Python, SQL, PostgreSQL, Trino, ClickHouse, Airflow, Datadog, and AWS cloud services Build automated observability and monitoring into data quality Produce privacy-preserving datasets and protect PHI, implementing de-identification and PII-reduction specifications set with Security, Infrastructure, and Product Draft technical design and documentation Seek and prioritize technical and product feedback from internal customers Iterate quickly with an eye towards value What you’ll bring to
the role Bachelor's degree or equivalent experience. Non-traditional backgrounds welcome. 4+ years developing data models, pipelines, and end-to-end analytical solutions. Programming experience in Python and SQL Experience with healthcare claims data, such as EDI 837/835 files, researcher datasets (CMS VRDC, CMS Limited Data Set), commercial claims (Komodo, MarketScan), or equivalents Advanced SQL including window functions, subqueries, CTEs, performance tuning and indexes Data modeling experience in support of diverse OLAP and OLTP workflows. Whether it’s Kimball or One Big Table, you recognize the tradeoffs and know the rules well enough to break them when it matters. Comfortable with object-oriented and functional programming patterns, code organization beyond scripts, and debugging workflows Experience with ETL/ELT workflows, orchestration(e.g., Airflow), and data engineering patterns (e.g., append-only vs inplace, medallion lakehouse) Ability to design data systems with scalability, performance, and cost efficiency in mind, particularly for compute and data-intensive workloads Software engineering rigor including automated testing, version control, software and data quality Thoughtful use of AI coding agents and LLMs in development workflows Comfortable working remotely in a collaborative, technical team
