Senior Automation Tester in Python

EPAM is a global leader in AI transformation engineering and integrated consulting with over thirty years of expertise in custom software, product, and platform engineering. The company serves Forbes Global 2000 companies and ambitious startups, empowering clients to become AI-Native enterprises.

• 5+ years of experience in Python automation test framework development
• Strong background in SQL and data testing
• Expertise in automation testing and quality engineering
• Skills to define and implement automation roadmaps for data engineering and document intelligence projects
• Understanding of ETL and streaming validation at scale, including window based and time based correctness checks
• Familiarity with CI/CD and DataOps practices to enforce quality gates before production deployment
• Capability to analyze production data issues, reduce flaky tests, and drive root cause fixes
• English proficiency at B2 level or higher

Nice to have:

• Familiarity with DBT and CI/CD (DevOps) practices

• Own the design and evolution of a scalable, modular, metadata driven test automation framework supporting data pipelines, APIs and backend services, and end to end data product validation
• Enable plug and play components, parallel execution, environment isolation, and deterministic runs
• Build reusable frameworks supporting SQL based assertions, reconciliation, schema validation, and data contracts
• Drive adoption by making frameworks easy to integrate into existing pipelines
• Design tests for schema drift, backward incompatibility, late arriving data, and partial failures
• Implement row, aggregate, and hash based reconciliation, along with incremental and backfill validation
• Integrate and extend tools like Great Expectations and Soda to build custom validations for accuracy, completeness, uniqueness, and timeliness
• Expose quality metrics, SLAs, and test outcomes through dashboards
• Embed testing into pipelines through pre merge gates, release blockers, and integration with GitHub Actions, Jenkins, or similar tools
• Define strategies for synthetic data generation, masking, anonymization, and deterministic datasets for repeatable testing
• Design and execute pipeline and query performance benchmarks, concurrency and stress testing, and data skew analysis
• Automate checks for PII or PHI exposure, encryption, access control, and data retention requirements

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