Data Engineer

Must-Haves:

• 3-6 years of professional data engineering or analytics engineering experience, with at least 2 years building and owning dbt projects in production
• Strong SQL, including window functions, complex joins, set operations, and query performance tuning on large datasets
• Demonstrated command of dbt beyond the basics: incremental strategies, materializations, snapshots, macros, packages, tests, and project structure and naming conventions
• Practical experience with Git and a pull-request-based CI/CD process, and a clear understanding of promoting data changes from development through staging to production
• Experience with a CI platform such as CircleCI, GitHub Actions, GitLab CI, or Azure DevOps, including configuring automated checks for a dbt project
• Hands-on experience with a cloud lakehouse or warehouse Databricks strongly preferred (Unity Catalog, Delta Lake, Spark SQL); Snowflake or BigQuery experience translates
• Proficiency in Python for ingestion, orchestration, and tooling
• Experience with a workflow orchestrator (Prefect, Airflow, Dagster, or Databricks Workflows)
• Working knowledge of dimensional modeling and when to depart from it
• Familiarity with data governance, access control, security, and HIPAA-compliant handling of PHI
• Clear written communication — you can explain a modeling decision or an incident to both an engineer and a clinical operations leader
• Comfort working in a fast-paced, ambiguous, and evolving environment with limited process scaffolding
• Healthcare data experience: claims, EHR (athenaOne a plus), HEDIS and CMS quality programs, risk adjustment and RAF/HCC, or value-based care and capitated payment models

Plusses

• Experience with Postgres as a serving layer, including indexing and query tuning
• Data quality and observability tooling (dbt artifacts-based monitoring, Elementary, Monte Carlo, or similar)
• Infrastructure as code (Terraform) and containerization (Docker)
• Experience with identity resolution or master data management (EMPI or similar)
• Exposure to streaming or near-real-time ingestion patterns
• Experience mentoring analysts or junior engineers on SQL, dbt, and code review