Snowflake QA

Job Description Seeking a Data QA Engineer II to join the Data Engineering team supporting RIDW, our enterprise Snowflake data platform. This is a net-new headcount created to meet the growing quality and validation demands of the platform as we scale toward the DWEB Enterprise BI Go-Live and beyond. In this role, you will own the design and execution of automated data quality frameworks, validate ETL and SCD pipeline behavior across our Bronze-Silver-Gold medallion architecture, and serve as the primary quality gate for data delivered to Power BI reporting and downstream business consumers. You will work closely with data engineers, the Data Engineering manager, and business stakeholders to ensure data accuracy, reliability, and observability across all platform layers. Pipeline & ETL QA u2022u2003Design and execute test plans for Snowflake stored procedures, Task DAGs, and incremental/full-load pipeline patterns u2022u2003Validate SCD Type 1 and Type 2 logic, including active record counts, reactivation paths, and tenant code update behavior u2022u2003Test bronze ingestion across various data sources. u2022u2003Perform row count reconciliation, duplicate detection, and watermark/freshness validation across environment tiers (DEV, QA, UAT, PROD) u2022u2003Own regression testing during environment refreshes and schema promotion cycles Data Quality & Validation u2022u2003Build and maintain data quality rules and threshold definitions. u2022u2003Validate Gold layer semantic models including rule view aggregations, Dynamic Tables, and reporting views u2022u2003Identify and document data anomalies, surface them via structured QA handoff packages, and track remediation through to sign-off u2022u2003Verify referential integrity across key grain tables. u2022u2003Conduct UAT validation cycles in coordination with business stakeholders and the BI team Automation & Framework Build u2022u2003Develop automated test suites using Python, dbt tests, or Snowflake-native SQL to reduce manual regression effort u2022u2003Integrate QA checks into CI/CD pipelines via Azure DevOps, including pre-merge and post-deploy validations u2022u2003Build and maintain data observability tooling: zero-row load alerts, data freshness checks, watermark stall detection, and anomaly notifications u2022u2003Author reusable QA scripts and test fixtures that can be deployed consistently across DEV/QA/UAT environments u2022u2003Contribute to promotion tracking and CI/CD logging patterns that connect deployment events to quality outcomes We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: Skills and Requirements u2022u20033-5 years of experience in data quality, QA engineering, or data testing roles u2022u2003Hands-on proficiency with Snowflake SQL, including window functions, CTEs, MERGE statements, and stream/task patterns u2022u2003Experience testing ETL pipelines u2014 incremental loads, CDC patterns, SCD Type 1/2, and bulk reconciliation u2022u2003Ability to write structured QA test plans, acceptance criteria, and sign-off documentation u2022u2003Experience with Azure DevOps or similar CI/CD tooling for managing deployments and test automation u2022u2003Strong analytical mindset u2014 comfortable identifying root causes from large data sets u2022u2003Advanced proficiency in Microsoft Excel, including formulas, pivot tables, lookups, data validation, conditional formatting, and the ability to analyze, reconcile, and present large data sets effectively u2022u2003Experience with Power BI or DAX u2014 ability to validate report data against source-layer results u2022u2003Exposure to Snowflake Dynamic Tables, Task DAGs, or stored procedure-based ETL patterns u2022u2003Familiarity with medallion architecture (Bronze / Silver / Gold) and layered data platform patterns u2022u2003Experience with dbt tests or similar declarative data quality frameworks u2022u2003Working knowledge of Jira for test case management, bug tracking, and sprint ceremonies u2022u2003SnowPro Core certification or equivalent demonstrated Snowflake expertise

Created: 2026-03-11