Data Quality Assurance Engineer
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RESPONSIBILITIES:
We are seeking a Data QA Engineer to design and execute test strategies across our AWS-based cloud data platforms and other platforms. You will validate complex ETL/ELT pipelines, ensure data integrity across data lakes and warehouses, and build automated testing frameworks for large-scale datasets.
• Pipeline Validation: Design and execute end-to-end data testing strategies for batch and streaming pipelines using AWS Glue, EMR
• Data Quality Assurance: Verify data accuracy, completeness, schema drift, and transformation logic between raw sources (S3) and destination analytical stores (Redshift, DynamoDB).
• Querying & Analytics: Write complex SQL scripts and PySpark jobs to perform data reconciliation, checksum validations, and boundary testing.
• CI/CD Integration: Integrate automated data test suites into AWS CodePipeline or GitHub Actions for continuous testing in deployment pipelines.
• Defect Tracking: Identify, log, and monitor data anomalies using tools like Jira, collaborating directly with Data Engineers and Analytics teams.
• Automation: Build and maintain automated data testing suites using Python, PyTest, and frameworks like Great Expectations or AWS Deequ. (Optional, nice to have) JOB
QUALIFICATIONS:
- Experience 1
- 3 years in Data Quality Engineering, Data Testing, or Data Engineering
- AWS Services Deep experience with S3, Amazon Redshift, Athena, and EMR
- Technical Skills
- Advanced SQL (window functions, aggregations) and Python (Pandas, PySpark)
- Testing Tools
- Experience with Great Expectations, pytest, dbt test, or custom SQL/Python test
- frameworks
- Databases
- Hands-on experience with Relational (PostgreSQL, MySQL) and NoSQL
- (DynamoDB) databases
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