- Develop comprehensive test strategies, test plans, test cases, test scripts and test execution reports.
- Execute software and data testing including functional, end-to-end, data migration quality assurance, regression and UAT support to ensure flawless system performance and data integrity.
- Lead end-to-end QA activities throughout the Software Development Life Cycle (SDLC) and Agile methodologies.
- Test ETL/ELT pipelines developed using Databricks and cloud-based data processing frameworks, requiring hands-on experience validating Delta Tables, Notebooks,SQL and PySpark transformations.
- Ability to prepare mock data sets as required for testing scenarios.
- Verify data quality checks within ingestion and transformation pipelines.
- Perform source-to-target reconciliation with Data Migration Testing experience
- Perform report reconciliation between legacy reporting systems and new analytics platforms.
- Prepare QA metrics, test status reports and release readiness assessments.
- Improve test coverage through automation and shift-left testing practices.
- Verify KPI calculations, measures, aggregations, filters and drill-down functionality.
- Ensure complete traceability between business requirements and testing artifacts.
- Conduct Root Cause Analysis (RCA) for defects and collaborate with development teams for resolution.
- Drive defect lifecycle management using tools such as Jira, Azure DevOps or Quality Center.
- Manage defect lifecycle and coordinate issue resolution with development teams.
- Integrate automation testing within CI/CD pipelines using Jenkins, Azure DevOps or GitHub Actions.
- Participate in sprint ceremonies and release validation activities.
- Experience with Master Data Management and one or more tools like Azure DevOps, Jira, Quality Center
Mandatory Skills:
ETL/Data Testing, Data Validation and reconciliation, Strong analytical & troubleshooting skills, Hands-on exposure to validating data across enterprise ETL platforms (AbInitio, SSIS, DataStage) and automating data quality checks using Pytest or similar testing frameworks.
Good-to-Have Skills:
- Healthcare Domain Experience with Healthcare Reporting & Analytics, Provider Data Validation,Clinical Data Testing, Claims Data Validation and understanding of Databricks ecosystem
- Experience working on large-scale cloud data migration, modernization or data warehouse transformation programs.
- Ability to understand complex data workflows and troubleshoot issues within data pipelines.
- Knowledge of cloud based data platforms such as Azure or AWS.
- Strong understanding of Agile Scrum Methodologies and proactive approach towards identifying risks and quality improvements.
