Quality Assurance Automation Engineer

Trident Consulting is a premier IT staffing firm providing high-impact workforce solutions. They are seeking a QA Automation Engineer to develop and execute test plans and validation strategies for data projects on Databricks and AWS, ensuring data quality and performance.

Responsibilities:

• Develop and execute test plans, test cases, and validation strategies for data projects built on Databricks, AWS, and Delta Sharing platforms
• Validate data ingestion pipelines, data transformations, and Medallion Architecture layers to ensure data quality, accuracy, completeness, and performance
• Create and maintain automated data testing frameworks using SQL and automation tools to support repeatable regression testing
• Test and verify Delta Sharing, Unity Catalog, role-based access controls, and data security mechanisms to ensure secure data access and governance
• Collaborate with Development, Product Owners, and Data Engineering teams to identify defects, track quality metrics, and resolve testing issues
• Support release planning, deployment validation, and production deployments while providing clear communication on testing status, risks, and issues
• Contribute to data governance and quality initiatives, including data catalog migration, DQ framework adoption, and testing best practices across the Databricks ecosystem

Requirements:

• 7+ Years of experience
• Databricks
• AWS
• Delta Sharing
• Develop and execute test plans, test cases, and validation strategies for data projects built on Databricks, AWS, and Delta Sharing platforms
• Validate data ingestion pipelines, data transformations, and Medallion Architecture layers to ensure data quality, accuracy, completeness, and performance
• Create and maintain automated data testing frameworks using SQL and automation tools to support repeatable regression testing
• Test and verify Delta Sharing, Unity Catalog, role-based access controls, and data security mechanisms to ensure secure data access and governance
• Collaborate with Development, Product Owners, and Data Engineering teams to identify defects, track quality metrics, and resolve testing issues
• Support release planning, deployment validation, and production deployments while providing clear communication on testing status, risks, and issues
• Contribute to data governance and quality initiatives, including data catalog migration, DQ framework adoption, and testing best practices across the Databricks ecosystem