QA Engineer – AWS Data Platform

Job Description

Years of Experience *

Minimum 5 years

Required Skills *

Primary technical and functional skills

• Strong hands-on experience testing enterprise-scale data platforms, data lakes, lake houses or cloud data warehouses.
• Advanced SQL skills for complex reconciliation, profiling, transformation validation and root-cause analysis.
• Strong automation experience using Python and preferably PySpark for high-volume data validation and regression testing.
• Practical experience testing AWS data services including AWS Glue, Amazon S3, Amazon Redshift, Amazon Athena, AWS Lake Formation and Glue Catalog.
• Strong understanding of ETL/ELT, batch and streaming pipelines, data orchestration, metadata, lineage and schema management.
• Experience validating Apache Iceberg or similar open table formats, including schema and partition evolution, snapshots and data consistency.
• Strong knowledge of data-quality dimensions and controls, including completeness, accuracy, validity, consistency, uniqueness, timeliness and referential integrity.
• Experience with automated test frameworks such as pytest, Great Expectations, Deequ or equivalent data-quality tooling.
• Experience testing data integrations across relational databases, APIs, files/object storage and streaming or event-based sources.
• Experience with test planning, requirements traceability, risk-based testing, defect management, regression testing and release assurance.
• Good understanding of IAM, data-access controls, masking, encryption and secure handling of test data within AWS.
• Experience with version control and CI/CD using Git Hub and Jenkins, Git Hub Actions or AWS Code Pipeline.
• Experience working in Agile environments using JIRA and Confluence, with evidence-based quality reporting and clear release recommendations.
• Strong analytical, troubleshooting and stakeholder-communication skills.

Required Experience / Knowledge Areas *

• Strong professional experience in Quality Assurance, Data Testing or Test Automation for Enterprise Data Platforms.
• Demonstrable experience assuring production-grade data pipelines and analytical datasets in complex enterprise environments.
• Experience working within Banking or Financial Services is strongly preferred.
• Understanding of data governance, security, privacy, auditability and regulatory expectations in a controlled enterprise environment.
• Experience across the full testing lifecycle, from requirements and source-data analysis through test design, execution, defect resolution, deployment validation and production support.
• Ability to challenge unclear requirements, identify data risks early and translate technical findings into clear business impact.