Databricks QA Engineer

Responsibilities: — Own test strategy and release sign-off on engagements, including documented test plans covering scope, risk, and coverage — Build and maintain automated test suites in Python and PySpark (pytest), integrated into CI pipelines — Validate data pipelines end to end: ingestion, Medallion layer transformations, Delta Lake tables, and ELT workflows — Implement data quality rules as code, including Lakeflow expectations and reusable validation frameworks — Run regression validation after code changes and maintain CI quality gates that block merge or promotion on failure — Partner with engineers to identify automation candidates and raise the coverage bar across the practice Requirements: — 5+ years in QA automation or test engineering, including meaningful time testing data pipelines rather than applications alone — A data engineering background: ability to read a pipeline, reason about a transformation, and write SQL to verify it — Expert-level SQL — Experience with Medallion architecture, Delta Lake, and ELT-based data platforms — Experience building automated suites that run in CI and gate deployment — Demonstrable hands-on experience working on Databricks or similar platforms Preferences: — Healthcare or life sciences experience preferred but not required — Existing Databricks certification(s) — Hands-on experience with Unity Catalog, Lakeflow pipelines, or Databricks Asset Bundles — Experience with Azure DevOps pipelines, or equivalent CI tooling (GitHub Actions, GitLab CI) — Familiarity with contract-based data validation standards such as ODCS — Experience using LLM agents or MCP-connected tooling to run validations, triage failures, or draft defect reports — Consulting or client-facing background Benefits: — Contract engagement rather than a salaried employee role — Pay rate: $70 – $80 per hour (1099 independent contractor) — Open to contractors anywhere in the United States.