Database Tester

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.
• Strong 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.