Key Responsibilities:
Develop and implement test strategies, test cases, and automation scripts to validate data pipelines in Azure and Databricks environments.
Perform data validation, reconciliation, and comparative analysis between source and target systems.
Validate ETL/ELT pipelines built using ADF and Databricks.
Collaborate with Data Engineers and Product Owners to understand STM (Source-to-Target Mapping) and ensure transformation logic is correctly implemented.
Monitor and validate data quality across Delta tables, and Data Warehouses.
Identify data anomalies, document defects, and drive them to resolution with the engineering team.
Support CI/CD pipelines by integrating data testing into DevOps workflows.
Contribute to test data management, metadata validation, and regression testing.
Provide regular reporting on test execution results, defect metrics, and QA health.
Required Skills:
Proven experience in data QA/validation in cloud-based data platforms.
Strong knowledge of Azure Data Factory, Databricks.
Proficiency in SQL and scripting languages such as Python.
Hands-on experience with data profiling, data reconciliation, and schema validation.
Understanding of SCD Type 2 and data transformation logic.
Familiarity with DevOps tools like Azure DevOps or GitHub Actions for CI/CD integration.
Experience working with large datasets, performance testing, and data lineage tools.
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