Data Quality Engineer

6-month initial contract | Remote, US Eastern/Central Overlap | Full-time hours

A US technology company we're working with are looking for an experienced Data Quality Engineer to own the quality of its data day to day. The test coverage, the observability stack and the release process already exist. Your role is to run them well, extend them as new work ships, set and hold the quality standards, and be the person who gives the go-ahead before a change reaches production.

What you'll own:

• QA for data pipelines, ETL and integration workflows on Snowflake: maintaining the existing test plans and cases and adding coverage as new models land

• Release sign-off for the data team: reviewing changes, confirming validations pass, and giving the go/no-go on deploys (including semantic layer changes)

• Data observability: triaging alerts in a dbt-based observability tool, confirming or dismissing anomalies, and routing real issues to the right engineer

• Data governance in practice: applying and improving the team's quality standards, data ownership, and access rules for sensitive personal data

• Reviewing AI-generated alert analysis and root-cause suggestions, correcting them, and feeding that back so they improve

• End-to-end validation from source to destination when changes land or incidents hit

• Keeping test procedures, sign-off checklists and runbooks current, and reviewing code with a data quality eye

Experience & Skills You Need:

• 5+ years in QA, with current experience testing data: pipelines, warehouses, ETL or migrations

• Strong hands-on SQL and Snowflake, day to day

• Production experience with a data quality or observability tool (Elementary, Monte Carlo, Great Expectations, Soda, dbt tests with alerting or similar)

• Real examples of work you personally owned: a release you stopped, an alert you traced to root cause, a quality standard you set or enforced

• Working knowledge of data governance: quality standards, ownership, and handling of sensitive personal data

• Unit testing habits and comfort with basic statistics (distributions, thresholds, outliers)

• dbt is a plus

• You use AI tools every day and can tell when they are wrong

• You can hold a sign-off line with engineers without slowing the team down