Senior ETL Test Data Engineer

We are seeking a highly skilled Senior QA Engineer who brings not only strong technical expertise but also proven leadership experience. The ideal candidate is hands‑on with automation frameworks, capable of designing, enhancing, and maintaining robust end‑to‑end automation solutions. This role requires someone who is self‑driven, proactive, and able to take full ownership of quality initiatives.

The candidate must have demonstrated experience in defining and implementing QA strategies, driving best practices, and influencing both technical and non‑technical stakeholders. We are looking for someone who can mentor team members, lead quality discussions, and help elevate the overall testing discipline across the organization.

Skills

• ETL pipelines Testing, ingestion pipelines testing, jobs testing, logical and physical data models testing – Must
• Python for Data Analysis (Pandas/NumPy/Matplotlib) – Must
• Databricks – Must
• SQL (Selects, Joins, stored procedures, Create, Delete, DDL) etc. – Must
• PySpark – Must
• Power Center – Good to Have
• IDQ – Good to Have
• Pytest – Good to Have

Key Responsibilities:

• Lead and execute comprehensive Data Quality and ETL testing efforts across the Cost & Margin domain.
• Design and implement robust test plans, test cases, and test data to ensure accuracy and completeness of cost-related data pipelines.
• Perform advanced SQL querying to validate transformations and trace root causes of data discrepancies.
• Analyze large datasets for data profiling, balancing, auditing, and reconciliation in production systems.
• Work with legacy and modern platforms (e.g., RMS, Manhattan, Blue Yonder) to identify and resolve data-related issues in real-time.
• Collaborate closely with Data Engineers, Product Owners, and other domain stakeholders to address data production issues and implement effective solutions.
• Participate in the architectural understanding of systems and interpret system & architecture diagrams to identify potential problem areas.
• Summarize test findings and quality metrics into clear, insightful reports and dashboards.
• Embrace agile, product-centric processes and self-organize within cross-functional teams to deliver results.