BI QA Automation Engineer

Job title:- Sr BI QA Automation Engineer

Location: Wilmington, DE (Onsite)

Overall experience – 10 years

JD:-

Seeking for a Sr BI QA Automation Engineer (6–10 yrs) with strong ETL/Data Warehouse & BI testing expertise, advanced SQL skills, and hands-on Playwright automation for BI dashboards such as Power BI/Tableau/Looker. This is not a standard QA requirement; the role is focused on testing BI dashboards.

Key Skills:- BI QA, Data QA, ETL Testing, Data Warehouse Testing, SQL, Playwright, Power BI, Tableau, Looker, Snowflake, Databricks.

Key Responsibilities

• BI Strategy and Test Design: Formulate, document, and execute end-to-end BI and Data QA strategies spanning source-to-target mapping, ETL data transformations, data mart models, and front-end visualization layers.
• UI and Report Automation: Build and maintain automated test frameworks using Playwright (TypeScript/Python) to validate interactive dashboard elements, dynamic filters, visual regressions, drill-downs, and export functions in enterprise reporting platforms (Power BI, Tableau, Looker, etc.).
• Data and ETL Pipeline Validation: Write complex SQL scripts to perform source-to-target data reconciliation, duplicate detection, schema validation, data type checks, and Slowly Changing Dimensions (SCD Type 1 and Type 2) verification.
• Security and Governance Testing: Test Row-Level Security (RLS), Column-Level Security (CLS), user role access controls, and data privacy compliance across reports and data marts.
• Cross-functional Collaboration: Partner closely with Data Engineers, BI Developers, Data Architects, and Product Owners to define acceptance criteria, analyze root causes of data drift, and resolve technical blockers.

Required Skills & Experience

• Experience: 6–10 years of dedicated experience in QA/Software Testing, with at least 5+ years focused on Data Warehouse, ETL, and BI Report Testing.
• Database and SQL Expertise: Expert-level mastery of SQL (window functions, CTEs, complex joins, indexing, and query plan optimization).
• Data Warehouse Platforms: Experience with cloud databases like Snowflake, Databricks, Amazon Redshift, Google BigQuery, or Oracle DW.
• BI Platforms: Hands-on testing experience with platforms such as Power BI, Tableau, Looker, Qlik Sense, or MicroStrategy.
• Agile and CI/CD: Proficiency in Git, Jira/Azure DevOps, and integrating automated tests into build pipelines.

Good to Have (Interpersonal and Soft Skills)

• Excellent Communication: Strong verbal and written communication skills to articulate complex data discrepancies, bug reports, and quality risks clearly to non-technical business stakeholders.
• Strong Interpersonal Skills: Proven ability to build collaborative relationships across software development, data engineering, and product management teams.
• Team Player and Mentor: A collaborative mindset with a track record of mentoring junior engineers, conducting peer code reviews, and contributing to overall QA process improvement.
• Analytical Mindset: High attention to detail with an investigative nature to isolate edge-case data anomalies and underlying data pipeline bottlenecks.