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Salary: USD 153,000 - 210,000 / annual
Cityblock Health is a value-based care company focused on serving complex-needs populations, particularly Medicaid beneficiaries and dually eligible individuals. This Staff Data Engineer role owns critical parts of the data foundation that enables both human decision-makers and AI agents to reason correctly over healthcare data at scale.
You will take ownership of Cityblock's data pipeline, working across a dbt Mesh on BigQuery (GCP) with Dagster orchestration. The data spans claims (medical and pharmacy), eligibility and enrollment rosters, EHR data, and HIE/FHIR feeds from internal care management platforms and vendor systems, flowing through to the semantic layer.
Key responsibilities include: improving member identity and eligibility data quality that gates clinical and financial workflows; building and maintaining ingestion and transformation systems that turn raw partner data into dependable, correct, and fresh outputs; owning the reverse-ETL layer that feeds operational data directly impacting care execution; supporting clinical performance evaluation and value-based contracts through quality measurement pipelines (HEDIS and Stars metrics); defining and maintaining the semantic and metrics layer so each metric has one correct definition applied consistently; making metrics machine-readable and well-constrained so AI agents query them correctly; building self-serve analytics for stakeholders; and partnering with Applied AI, BI, clinical, and platform teams to ensure data trustworthiness.
You'll need 6+ years building trustworthy systems, hands-on production-scale dbt and SQL experience with orchestration tools (Dagster, Airflow, or equivalent), ownership of operational or reverse-ETL data systems in production, semantic or metrics layer design/maintenance experience, understanding of how LLMs consume structured context, and fluency with AI assistant tooling (Claude, Cursor, Codex). You should be mission-driven, comfortable partnering across disciplines, and able to scale AI adoption. Nice-to-haves include CS degree, regulated/compliance environment experience (HIPAA, SOC 2), BigQuery expertise, healthcare/clinical data exposure, and self-serve analytics adoption experience.
