Senior Data QA Automation Engineer

Project Description
Our customer (originally the Minnesota Mining and Manufacturing Company) is an American multinational conglomerate operating in the fields of industry, worker safety, and consumer goods. Based in the Saint Paul suburb of Maplewood, the company produces over 60,000 products, including adhesives, abrasives, laminates, passive fire protection, personal protective equipment, window films, paint protection film, electrical, electronic connecting, insulating materials, car-care products, electronic circuits, and optical films.

Job Description

• 5+ years in Quality Assurance with a proven track record in Data Warehousing and ETL testing.
• Advanced ability to write complex queries to audit data logic.
• Proficiency in Python to automate data validation tasks and compare large datasets programmatically.
• Deep understanding of data-specific concepts like schema evolution, data truncation, null handling, and referential integrity.
• Solid experience working in fast-paced Scrum environments and understanding the QA lifecycle within short delivery sprints.
• High-level professional proficiency in English (written and verbal) for technical documentation and stakeholder communication.
• Strong problem-solving skills with the ability to identify "the needle in the haystack" within millions of rows of data.

Nice-to-Haves:

• Hands-on experience using Databricks for data exploration, running SQL/Python notebooks, and verifying complex table schemas.
• Experience using Azure DevOps (AzDO) or Jira for test case management and bug tracking.
• Familiarity with Azure services (Function apps, ADLS etc).
• Experience in testing web services/APIs.

Job Responsibilities

• Create comprehensive manual and automated test plans tailored to ETL workflows and Data Warehouse architectures.
• Perform rigorous testing to ensure data flows correctly and accurately from diverse source systems to target tables
• Design and implement automation scripts to validate data integrity, completeness, and transformation logic at scale.
• Identify and track data discrepancies, collaborating closely with Data Engineers to perform root-cause analysis on failures.
• Actively participate in Scrum ceremonies to refine requirements, estimate data-related tasks, and provide quality-based recommendations