The ideal candidate will possess a blend of data testing, SQL, Python automation, and cloud data platform experience, with the ability to design both manual and automated validation solutions for enterprise data pipelines.

Key Responsibilities:

- Design, develop, and execute test cases for enterprise data ingestion and transformation pipelines.
- Validate data movement from source systems into Bronze and Silver layers within BigQuery.
- Perform source-to-target data validation and reconciliation testing.
- Verify data transformation logic, mappings, business rules, and data quality controls.
- Create and execute functional, integration, regression, and end-to-end test plans.
- Develop SQL queries to validate data completeness, accuracy, and consistency.
- Develop, maintain, and execute automated test scripts to validate data ingestion, transformation, and data quality processes.
- Leverage Python-based automation frameworks and utilities to improve testing efficiency, data validation, and regression testing coverage.
- Automate source-to-target data reconciliation and validation activities for large datasets within GCP and BigQuery environments.
- Collaborate with engineering teams to integrate automated testing into CI/CD pipelines and release processes.
- Identify, document, and track defects through resolution.
- Collaborate closely with developers, architects, product owners, and business stakeholders.
- Participate in Agile ceremonies including sprint planning, backlog refinement, and daily stand-ups.
- Support production validation activities and post-release verification.
- Provide test evidence and documentation to support data governance and compliance requirements.




- Ensure enterprise data quality standards are maintained across onboarding initiatives.

Required Skills and Expertise:

- 5+ years of QA experience focused on data validation, ETL testing, data warehouse testing, or cloud data platform testing.
- Strong SQL skills with the ability to write complex queries for data validation, reconciliation, and data analysis.
- Hands-on experience with Python programming for test automation, data validation, scripting, or process automation.
- Experience developing and maintaining automated test frameworks, automated validation scripts, or test automation solutions.
- Experience testing cloud-based data platforms and data pipelines.
- Experience validating large datasets and complex data transformations.
- Strong understanding of data warehousing concepts and dimensional modeling.
- Experience with defect tracking and test management tools.
- Familiarity with Agile/Scrum methodologies.
- Solid analytical, troubleshooting, and problem-solving skills.
- Excellent verbal and written communication skills.
- Ability to work effectively with distributed global teams.

Highly Preferred Skills and Expertise:

- Experience with Google Cloud Platform (GCP).
- Experience validating data within BigQuery environments.




- Familiarity with Dataflow and cloud-based data processing frameworks.
- Experience testing data ingestion and transformation pipelines.
- Experience using Python libraries and frameworks such as PyTest, Pandas, NumPy, or custom automation frameworks.
- Experience building automation solutions for data reconciliation, ETL validation, API testing, or regression testing.
- Experience with Java-based data processing applications.
- Understanding of Medallion Architecture (Bronze, Silver, Gold).
- Experience supporting enterprise data modernization initiatives.
- Exposure to CI/CD pipelines and Git-based source control.
- Knowledge of data governance, data quality frameworks, and cloud security best practices.
- Exposure to analytics, AI, or machine learning data platforms.

What Success Looks Like: The successful consultant will help ensure that enterprise data entering our client's Enterprise Data Platform is accurate, reliable, and ready for analytics and AI consumption by:

- Validating data onboarding processes from source systems into BigQuery.
- Ensuring Bronze and Silver layer transformations meet business and technical requirements.
- Developing automated validation solutions using Python to improve testing efficiency and data quality coverage.
- Identifying and resolving data quality issues before production deployment.
- Supporting the delivery of scalable, trusted, and governed enterprise data assets.
- Collaborating effectively with both offshore and U.S.-based engineering teams.
- Contributing to the continuous improvement of QA automation, testing processes, and data quality standards across the platform.

📌 Database Tester (Hyderabad)
🏢 ISPACE SOFTWARE SOLUTIONS INDIA PRIVATE
📍 Hyderabad