Senior QA Data Engineer at Gradient AI

Job description

**This is a fully remote position. Gradient AI: Gradient AI is a leading provider of AI solutions for the Group Health and P&C insurance industries. Our solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Gradient AI's SaaS platform leverages a vast industry data lake comprising tens of millions of policies and claims, providing insurers with high resolution, data-driven insights. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. Founded in 2018, Gradient has experienced strong growth every year, and recently raised $56 million in Series C funding from top Insurtech investors. About the role: We are looking for a detail-oriented and creative thinker to join our team as a Senior QA Data Engineer. In this role, you will establish the next generation of data quality and testing at Gradient AI. The successful candidate will be responsible for ensuring the quality and trustworthiness of the customer data that powers our products by designing, building, and running automated data quality checks across our data pipeline. This individual will collaborate with engineering and customer-facing teams to catch data issues before they reach production. The ideal candidate is meticulous, comfortable working in an agile environment, a team player, a strong communicator, and ready to hit the ground running at a fast-growing AI startup. How will you make an impact: Build and maintain an automated data quality suite that runs 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, including baseline checks so that new pipelines get coverage by default. Validate loads for completeness, sequencing, and schema before processing, and halt or quarantine failures. Reconcile data across Databricks layers and against downstream outputs such as pricing tools, at the portfolio, group, member, and claim level, using tolerance thresholds. Detect anomalies, edge cases, and unexpected changes in historical data between loads. Define severity levels and per-customer overrides and produce a data quality report for each customer that gates promotion to downstream consumers. Track quality metrics over time to retire flaky checks and close coverage gaps. Partner with engineers so that new pipelines ship with checks built in, and give engineers, stakeholders, and project managers timely feedback on quality issues and risk. Skills needed to succeed: 5+ years of professional experience in quality assurance with a 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 a similar lakehouse environment (Delta Lake). Familiarity with version control and CI/CD. Solid understanding of testing principles, including functional, integration, and regression testing, applied to data pipelines. Excellent analytical, debugging, and root-cause 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 such as Cypress or Playwright. Tools we use: dbt, Soda, SQL (Spark SQL), Python, Airflow, Databricks (Delta Lake) What we offer: A fun, team-oriented startup culture. Generous stock options - we all get to own a piece of what we're building. Unlimited vacation days. Flexible schedule that supports working from home. Full benefits package including medical, dental, vision, 401k, and more. Ample opportunities to learn and take on new responsibilities. We are an equal opportunity employer. Salary Range: $1 30 ,000- 1 50 ,000 base salary annually. This role is also eligible for an annual performance bonus, equity grant, and a comprehensive benefits package.  In accordance with  the Massachusetts Pay Transparency Law, we are  providing  a good-faith salary range for this position at the time of posting. The actual salary offered will depend on the level at which the candidate is hired, as well as their experience, skills, qualifications, and location. Compensation may grow over time through merit-based increases, promotions, and company-wide adjustments.  If your salary expectations fall outside this range, we still encourage you to apply so we can have a conversation.

Requirements

Must-haves

This is a fully remote position.
Build and maintain an automated data quality suite that runs 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, including baseline checks so that new pipelines get coverage by default.
Validate loads for completeness, sequencing, and schema before processing, and halt or quarantine failures.
Reconcile data across Databricks layers and against downstream outputs such as pricing tools, at the portfolio, group, member, and claim level, using tolerance thresholds.
Detect anomalies, edge cases, and unexpected changes in historical data between loads.
Define severity levels and per-customer overrides and produce a data quality report for each customer that gates promotion to downstream consumers.
Track quality metrics over time to retire flaky checks and close coverage gaps.
Partner with engineers so that new pipelines ship with checks built in, and give engineers, stakeholders, and project managers timely feedback on quality issues and risk.
5+ years of professional experience in quality assurance with a strong focus on backend automated testing.
Hands-on experience building automated data tests with Soda, dbt, or similar frameworks.
Experience with Airflow or similar orchestration tools, and with Databricks or a similar lakehouse environment (Delta Lake).
Familiarity with version control and CI/CD.
Solid understanding of testing principles, including functional, integration, and regression testing, applied to data pipelines.
Excellent analytical, debugging, and root-cause 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.
Tools we use: dbt, Soda, SQL (Spark SQL), Python, Airflow, Databricks (Delta Lake)

Nice-to-haves

Strong SQL skills (Spark SQL a plus) and familiarity with Python.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiagilesqlpythonci/cd

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401k