QA Engineer – AWS Data Platform
Key Skills for This Role
Full Job Posting
Experience and qualifications
- Minimum 5 years of experience.
- Professional experience in QA, data testing, or test automation for enterprise data platforms.
- Demonstrable experience assuring production-grade pipelines and analytical datasets in complex enterprise environments.
- Banking or financial services experience is strongly preferred.
- Experience across the full testing lifecycle, from requirements and source-data analysis through production support.
Technical skills
- Advanced SQL for reconciliation, profiling, transformation validation, and root-cause analysis.
- Strong Python automation experience; PySpark is preferred.
- Experience testing AWS Glue, S3, Redshift, Athena, Lake Formation, and Glue Catalog.
- Understanding of ETL/ELT, batch and streaming pipelines, orchestration, metadata, lineage, and schema management.
- Experience validating Apache Iceberg or similar formats, including schema and partition evolution, snapshots, and data consistency.
- Knowledge of data-quality controls, including completeness, accuracy, validity, consistency, uniqueness, timeliness, and referential integrity.
- Experience with pytest, Great Expectations, Deequ, or equivalent data-quality testing tools.
- Experience with database, API, file/object-storage, and streaming/event-based data integrations.
- Knowledge of IAM, access controls, masking, encryption, and secure test-data handling.
- Experience with GitHub and CI/CD using Jenkins, GitHub Actions, or AWS CodePipeline; Agile work with JIRA and Confluence.
Testing and quality responsibilities
- Plan and perform risk-based data-platform testing, including regression testing, defect management, release assurance, and requirements traceability.
- Validate production-grade data pipelines and datasets across source analysis, test design, execution, defect resolution, deployment, and production support.
- Report evidence-based quality findings and clear release recommendations.
- Identify data risks early, challenge unclear requirements, and translate technical findings into business impact.
Quality and domain knowledge
- Understand data governance, security, privacy, auditability, and regulatory expectations in controlled enterprise environments.
- Strong analytical, troubleshooting, and stakeholder-communication skills.
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