Data Quality Engineering Lead
Dearborn, MI
USD 115K-153K (estimate) Senior-level Contract Full Time
Tasks
- Automate QA processes using Python, PySpark, Java
- Coordinate defect validation with development
- Create and execute API testing scenarios
- Create and execute manual test cases in Jira
- Create test cases in TestRail
- Design and implement automation frameworks with PyTest
- Ensure quality, efficiency, and compliance
- Lead testing of dashboards and metrics
- Log defects in JIRA
- Manage code repositories, pull requests, and code merges
- Participate in daily agile stand-ups
- Stay current with testing tools and practices
- Test AI prompting tools and responses
- Test data ingestion, transformation, and aggregation in Databricks and Azure
- Update task status in JIRA
- Use SonarQube for code quality and fixing code smells
- Validate ETL rules and complex business logic
- Validate data flow, content, and business rules
- Verify data accuracy in UI and backend
- Write complex SQL queries and validate data
Perks/Benefits
- N/A
Skills/Tech-stack
API Testing | Agile methodology | Automation frameworks | Avro | Azure Databricks | Code Repository | Code repository tools | Data Aggregation | Data Pipeline Testing | Data Transformation | Data Validation | Data metrics | Data pipeline | Databricks | Development Lifecycle | ETL | JSON | Jira | Parquet | Pipeline testing | PySpark | Pytest | Python | Repository tools | SQL | Software Development Lifecycle | Software development | SonarQube
Education
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