SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 130,000 - 150,000 / annual
Gradient AI is a Series C-funded AI/SaaS platform serving the insurance industry (health and P&C). The company provides underwriting and claims risk prediction, quote automation, and intelligent claims processing to major insurance carriers, MGAs, and self-insured employers. Founded in 2018, Gradient has raised $56M and maintains a fast-growing, team-oriented startup culture.
In this Senior QA Data Engineer role, you will establish the next generation of data quality and testing infrastructure at Gradient AI. You'll be responsible for ensuring the quality and trustworthiness of customer data powering the platform by designing, building, and running automated data quality checks across the data pipeline.
Key responsibilities include:
- Build and maintain an automated data quality suite running on data refreshes through Airflow, replacing manual per-customer validation
- Define checks as code (dbt tests, Soda, custom SQL) managed through version control and CI, with baseline checks for new pipelines
- Validate loads for completeness, sequencing, and schema before processing; halt or quarantine failures
- Reconcile data across Databricks layers and against downstream outputs (pricing tools) at portfolio, group, member, and claim levels using tolerance thresholds
- Detect anomalies, edge cases, and unexpected changes in historical data between loads
- Define severity levels and per-customer overrides; produce data quality reports that gate promotion to downstream consumers
- Track quality metrics over time to retire flaky checks and close coverage gaps
- Partner with engineers to ship new pipelines with checks built in; provide timely feedback on quality issues and risk to engineers, stakeholders, and project managers
You will work independently and mentor others on testing best practices, collaborating closely with engineering and customer-facing teams to catch data issues before production.
Requirements:
- 5+ years of professional experience in quality assurance with strong focus on backend automated testing
- Hands-on experience building automated data tests with Soda, dbt, or similar frameworks
- Strong SQL skills (Spark SQL a plus) and familiarity with Python
- Experience with Airflow or similar orchestration tools, and with Databricks or similar lakehouse environment (Delta Lake)
- Familiarity with version control and CI/CD
- Solid understanding of testing principles (functional, integration, regression testing) applied to data pipelines
- Excellent analytical, debugging, and root-cause analysis skills
- Strong verbal and written communication skills, including clear defect reports and quality summaries for technical and non-technical audiences
- Ability to work independently and coach others on testing best practices
Bonus: Experience with health insurance or healthcare data, anomaly detection or data observability tools, or front-end test automation tools (Cypress, Playwright).
