
Quality Assurance Automation Engineer
2 weeks ago
We are looking for a detail-oriented and innovative QA Automation Engineer to design, develop, and execute automated testing solutions and comprehensive test plans for Source Systems and Business Data Products. The ideal candidate will have strong expertise in validating the integrity, accuracy, and consistency of enterprise data across multiple business domains.
This role is responsible for ensuring the quality of data processed through ETL pipelines and data modeling scripts across platforms such as Oracle, SQL Server, and Snowflake. The QA Automation Engineer will collaborate closely with data engineering, QA, and business teams to support scalable, reliable, and high-quality data delivery across the organization.
Key Responsibilities :
1) Design and Develop Automated Data Quality Tests :
- Build reusable automated tests to validate data ingestion, transformations, and loading across Oracle, SQL Server, and Snowflake.
- Verify data integrity, completeness, schema conformity, and business logic through all layers from raw landing zone to curated data products.
2) Ensure End-to-End Data Pipeline Validation :
- Automate testing for ETL/ELT processes, including data staging, cleansing, fact and dimension population, and final consumption layers.
- Monitor transformations and data flows across platforms (Oracle ? Snowflake, SQL Server ? Snowflake, etc.).
3) Leverage Snowflake-Specific Testing Capabilities :
- Utilize Snowflake's native SQL features (e.g., streams, tasks, time travel, variant types) in test development.
- Automate regression and functional testing for Snowflake data models, stored procedures, and materialized views.
4) Build and Maintain a Cross-Platform Test Framework :
- Extend or integrate automation frameworks compatible with Oracle, SQL Server, and Snowflake.
- Apply data validation tools to verify transformation accuracy.
5) Utilize TestRail for Test Case Management :
- Design, document, and maintain test cases and test plans in TestRail.
- Track execution, report defects, and ensure traceability across automated and manual test cases.
6) Collaborate Across Data Engineering and Modeling Teams :
- Work with data engineers, modelers, and analysts to define test criteria for ingestion jobs, business logic, and reporting outputs.
- Provide early feedback on development tickets and participate in code reviews.
7) Support CI/CD and Test Automation in Production Pipelines :
- Integrate automated tests into CI/CD workflows.
- Validate data integrity during production releases, schema updates, and migrations.
8) Develop Monitoring and Alerting for Data Quality :
- Implement anomaly detection and alerting for critical pipelines (e.g., volume drops, schema drift, business rule violations).
- Create dashboards or automated reports for monitoring data quality trends.
9) Ensure Test Coverage, Reusability, and Documentation :
- Maintain reusable and version-controlled test suites across platforms.
- Document test strategies, data quality standards, and platform-specific practices
(ref:hirist.tech)-
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