QA Lead - Data Testing (Snowflake, Databricks, Azure)

What You ll Do:

• Lead hands-on QA efforts for data engineering and analytics solutions, with a primary focus on back-end data testing, ETL/ELT pipelines, data transformations, data quality, and business-rule validation.
• Write and execute SQL-based test cases to validate complex datasets and perform source-to-target reconciliation between new and legacy systems using mapping documentation.
• Validate data across Snowflake, Databricks, Azure Data Factory, Azure Data Lake, and medallion architecture layers, identifying data quality issues and supporting root-cause analysis.
• Help establish a new QA workstream from the ground up, contribute to testing strategy and architecture discussions, and support the transition from manual testing toward reusable data-testing automation and cross-system reconciliation.
• Lead and mentor junior QA resources, review test cases and Jira tickets for completeness and edge-case coverage, and represent QA during Agile ceremonies, business discussions, and go-live activities.

What Gets You the Job:

• 7+ years of QA experience with a strong background in data warehouse, ETL/ELT, data quality, or back-end data testing, including prior experience leading or mentoring QA team members.
• Advanced hands-on SQL skills with the ability to independently write queries and create test cases for complex data validation, transformation, reconciliation, and business-rule testing.
• Strong hands-on Snowflake experience along with experience testing modern data platforms using technologies such as Databricks, Azure Data Factory, and Azure Data Lake.
• Strong experience with source-to-target mapping, data reconciliation, data transformation validation, data quality testing, and modern data architectures such as bronze, silver, and gold layers.
• Experience with test automation and the ability to help move a manual-heavy QA environment toward greater automation; exposure to Streamlit, Python/PyTest, CI/CD, or other automation technologies is highly valued.