Data Quality Engineer

Duties :

The ideal candidate brings 5+ years of data quality engineering experience with a strong analytical mindset and the ability to investigate datasets holistically-understanding how data relates to itself and to adjacent datasets, formulating the right questions, and designing targeted analyses to uncover issues or validate accuracy.

They should be proficient in Databricks and SQL Server testing environments, with hands-on experience using data validation frameworks such as Collibra or Great Expectations.

Strong Python and SQL skills are essential for building data quality checks, along with expertise in data profiling and anomaly detection techniques.

The candidate must be comfortable reading and interpreting code written by others, enabling them to trace identified data issues back to their source within pipeline logic and communicate findings clearly to engineering teams.

Experience with test automation frameworks and quality process development is expected, and healthcare data validation experience is preferred. Strong documentation skills and the ability to define and track quality metrics round out the profile.

Skills :

• Databricks and SQL Server testing
• Data validation frameworks (Colibra, Great Expectations)
• Test automation frameworks - Python and SQL for data quality checks
• Data profiling and anomaly detection
• Documentation and quality metrics

Experience : - 5+ years data quality engineering - Healthcare data validation experience preferred - Test automation and quality process development