Staff Data Engineer, Cityblock Health

• 6+ years of experience as an engineer who builds trustworthy systems
• Hands-on experience with dbt and SQL at production scale including orchestration with Dagster, Airflow, or equivalent
• Experience owning operational or reverse-ETL data systems that other applications depend on in production
• Experience designing or maintaining a semantic layer or a metrics layer
• An understanding of how LLMs consume structured context and fluent daily use of AI assistants tooling (Claude, Cursor, Codex, or equivalent) for engineering and within data analytics
• A demonstrated ability to scale AI adoption to accelerate collaborative work with your peers
• Drawn to mission-oriented work, and comfortable partnering with BI, clinical, and product stakeholders whose expertise differs from yours

Desirable

• Cityblock delivers value-based care to individuals with complex needs with a particular focus on Medicaid beneficiaries and those who are dually eligible, people the healthcare system has historically underserved
• Increasingly, whether a care or operational decision drives a positive outcome depends on whether a human or an AI agent can reason correctly over our data
• This role owns major parts of the foundation that makes that possible. You'll take ownership across our data pipeline that will be leveraged at scale to enable people and models to arrive at the same answers that drive the care our members receive
• You'll work in a dbt Mesh on BigQuery (GCP), with ingestion orchestrated through Dagster. The data you handle spans claims (medical and pharmacy), eligibility and enrollment rosters, EHR data, and HIE/FHIR feeds
• You’ll work with data from our internally developed care management platform, vendor systems, and pull that data through to our semantic layer. You will treat data quality as the product
• Across operational data systems:
• You'll improve the quality of member identity and eligibility data that determines who is an active member and gates every downstream clinical and financial workflow
• Build and maintain the ingest and transformation systems that turn raw partner data into something dependable, from correctness through freshness to reliability
• Own the reverse-ETL layer that feeds the operational data that directly impacts the efficiency and effectiveness of the execution of Cityblock's care model
• Support clinical performance evaluation and value-based contracts through a quality measurement pipeline that produces HEDIS and Stars metrics
• Across the semantic and metrics layer:
• Define and maintain the semantic and metrics layer so each metric has one correct definition, applied consistently across every dashboard and every consumer
• Make metrics machine-readable and well-constrained, so an AI agent querying them gets a correct answer because the layer bounds what it can conclude
• Build self-serve analytics so stakeholders can answer their own questions against definitions they can trust
• Across teams:
• Feed the Applied AI product work the grounded, trustworthy context its features depend on
• Partner with BI and clinical stakeholders so the definitions in the semantic layer match how the business actually reasons about itself
• Work with the platform team so agents query your data through governed, well-understood paths
• How we define success:
• Operational data is correct, fresh, and reliable, and the systems that depend on it trust it without manual checks
• We can rely on our AI systems to leverage data correctly
• Stakeholders increasingly answer their own questions through self-serve analytics