Quality Assurance Engineer
Tasks
- Apply AI assisted testing and intelligent test generation
- Benchmark materialized view performance and validate data freshness and latency
- Build automated data validation checks
- Collaborate with data and AI teams to test RAG analytics and machine learning workflows
- Design automated testing frameworks for data pipelines
- Develop test strategies for Apache Iceberg schema evolution time travel and partitioning
- Develop unit integration and end to end test cases for data workflows
- Document test results defects and quality metrics
- Implement reconciliation and audit mechanisms source to target mappings
- Integrate automated tests into CI/CD pipelines
- Operate within Agile teams to improve data quality processes and tooling
- Perform defect prediction and automated root cause analysis
- Validate AI generated metadata lineage and transformations
- Validate ETL and ELT pipeline ingestion transformation and delivery
- Validate data quality rules lineage and metadata accuracy
- Validate data readiness for AI ML and generative AI use cases
Perks/Benefits
Skills/Tech-stack
AI-assisted testing | AWS | AWS Glue | AWS Lambda | Amazon Redshift | Amazon S3 | Anomaly Detection | Apache Iceberg | Apache Spark | Audit | CI/CD | Data Governance | Data Integrity | Data Lakehouse | Data Modeling | Data Quality | Data Validation | ELT | ETL | End to End | End-to-End Testing | Generative AI | Integration Testing | Lineage | Machine Learning | Materialized views | Metadata | Python | RAG | Reconciliation | SQL | Test automation | Unit Testing | XBRL
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