Validate end-to-end ETL workflows, including data extraction, ingestion, transformation, loading, and reporting.
Review and validate source-to-target mappings, data transformation rules, business rules, and data quality requirements.
Design, develop, and execute comprehensive ETL test cases for:
Real-time and near-real-time data pipelines
Data migration initiatives
Data integration processes
Perform hands-on database testing across Oracle Database and MongoDB environments.
Write and execute complex SQL queries for:
Source-to-target validation
Record-count verification
Referential integrity checks
Data completeness and accuracy checks
Transformation-rule validation
Aggregation and reporting validation
Develop and maintain reusable test automation frameworks and scripts using Python and PyTest.
Use Python to automate data extraction, comparison, reconciliation, validation, and test reporting activities.
Validate data files and datasets stored, transferred, or processed through AWS S3.
Test REST APIs and verify data flow, payload structure, response codes, error handling, and system-to-system integration.
Use tools such as Postman and Swagger to perform API validation and troubleshooting.
Perform end-to-end testing of data ingestion, transformation, storage, integration, and reporting processes.
Execute functional, integration, regression, system, and data validation testing.
Test data warehouse components, including fact tables, dimension tables, staging layers, aggregates, historical data, and reporting outputs.
