About the role
Responsibilities Build and maintain an automated data quality suite running on data refreshes through Airflow Define data quality checks as code (dbt tests, Soda, custom SQL) under version control and CI Validate loads for completeness, sequencing and schema, halting or quarantining failures Reconcile data across Databricks layers and downstream outputs using tolerance thresholds Detect anomalies and unexpected changes in historical data between loads Produce per-customer data quality reports that gate promotion to downstream consumers Partner with engineers so new pipelines ship with checks built in Requirements 5+ years of professional quality assurance experience with a focus on backend automated testing Hands-on experience building automated data tests with Soda, dbt or similar Strong SQL skills and familiarity with Python Experience with Airflow or similar orchestration tools and Databricks or a lakehouse environment Familiarity with version control and CI/CD Solid understanding of functional, integration and regression testing for data pipelines Strong analytical, debugging and communication skills Preferred Qualifications Experience with health insurance or healthcare data Experience with anomaly detection or data observability tools Front-end test automation experience with Cypress or Playwright Benefits Stock options, unlimited vacation, flexible remote schedule Medical, dental, vision and 401k benefits Annual performance bonus and equity grant About the Company Gradient AI provides AI solutions for Group Health and P&C insurers, improving loss ratios and claim outcomes. Founded in 2018, the company raised $56 million in Series C funding.