Sr. Data Engineer - Healthcare Data Infrastructure

Transform healthcare with us. Qualified Health is seeking a Data Engineer to support partner integration. This isn't a role where you'll maintain legacy systems or write reports nobody reads. You'll build the data pipelines that directly power AI systems used by clinicians across major U.S. health systems — systems that help doctors identify the right diagnosis, flag patients who need intervention, and improve outcomes at scale. Every query you write, every pipeline you build, directly touches patient care. You'll work in a pod with a Director who owns the customer relationship and 1-2 peers who share the mission. The path from Senior Data Engineer to Director of your own pod is clear and real — we're growing fast enough that the opportunity is a matter of when, not if. Key Responsibilities: Build and maintain data ingestion pipelines that pull from Epic Clarity databases, FHIR APIs, and HL7 feeds into our Databricks lakehouse architecture — the technical backbone of every AI workflow we deliver Execute data quality validation and mapping verification for each new workflow deployment — ensuring AI models receive clean, accurate, clinically relevant data. Your QC catches the problems before they reach production. Support 2-4 health system partners as part of a dedicated integration pod, working closely with your Director and directly with customer data teams Develop and optimize SQL queries against complex healthcare data models (hundreds of tables, billions of rows) including clinical, billing, and operational data Contribute to defining reusable data transformation patterns that accelerate future partner onboarding — the patterns you build today are used by every pod tomorrow Required Qualifications: 4+ years as a data engineer building production ETL/ELT pipelines — you've shipped real systems, not just notebooks Strong SQL skills (complex joins, window functions, CTEs across large healthcare datasets). Healthcare SQL is its own discipline — you either know it or you're ready to master it. Proficiency with Python and PySpark for data transformation at scale Experience with cloud data platforms (Databricks preferred; Snowflake, BigQuery, or Redshift accepted — you'll learn Databricks fast if you haven't already) Familiarity with FHIR data models or willingness to learn healthcare data standards quickly — we'll invest in your ramp Azure cloud services experience (ADLS Gen2, Key Vault, networking basics) Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field Desired Skills: Epic Clarity/Caboodle experience or healthcare data background — this is a huge accelerant Delta Lake, Delta Sharing, or lakehouse architecture experience HL7 message processing or real-time healthcare data pipelines Experience working in a multi-tenant SaaS environment Technical Environment: Our data infrastructure is built on modern cloud technologies including: Azure Databricks + Data Factory (plus Fabric and Snowflake integrations) PySpark for distributed data processing GitHub Actions + Terraform for CI/CD and Infrastructure as Code Python with type-safe patterns and modern frameworks Healthcare data formats including FHIR, Epic Clarity, and other EHR schemas What Success Looks Like: High-quality data pipelines delivered on schedule with thorough testing and documentation Proactive issue identification with technical problems caught and resolved before impacting partners Reusable components that reduce implementation time for subsequent integrations Clean production deployments with minimal post-launch issues Technical credibility with partner IT teams based on quality of work Efficient troubleshooting with quick diagnosis and resolution of data quality issues Impact Growth Opportunity: As a Data Engineer at Qualified Health, you'll build the data infrastructure that powers AI-driven insights for major health systems. Your work directly enables better ... (truncated, view full listing at source)