ETL Process Validation:
- Validate and verify ETL processes implemented in Google Cloud Platform, ensuring data integrity during extraction, transformation, and loading.
- Develop and execute comprehensive test cases to confirm that data transformations meet business requirements.
Data Quality Assurance:
- Conduct data profiling and perform quality checks to identify and resolve discrepancies in datasets.
- Monitor data quality metrics and report on data integrity and quality issues.
Test Case Development:
- Create and maintain detailed test plans and test cases based on ETL specifications and business needs.
- Ensure full coverage of all ETL processes, including data extraction, transformation, and loading.
Collaboration:
- Work closely with data engineers, data scientists, and other stakeholders to understand ETL workflows and data flows.
- Participate in design reviews to provide input on testing strategies and best practices.
Automation:
- Use Python to develop automated testing scripts for ETL validation and data quality checks.
- Leverage Databricks notebooks for testing and validating ETL processes efficiently.
Workflow Management:
- Utilize Apache Airflow for scheduling, monitoring, and managing ETL workflows.
- Collaborate with teams to troubleshoot and optimize Airflow DAGs related to ETL processes.
Issue Tracking and Resolution:
- Identify, document, and track defects and data quality issues throughout the ETL process.
- Work with engineering teams to diagnose and resolve data-related problems quickly.
Documentation:
- Maintain clear and comprehensive documentation of testing processes, test cases, and results.
- Document data mappings, transformation rules, and data flow diagrams for reference.
Continuous Improvement:
- Contribute to the enhancement of ETL testing methodologies and data management practices.
- Stay updated on Google Cloud Platform, Databricks, and industry trends to continuously improve testing strategies.