•Lead end-to-end testing of ETL and data pipelines across multiple processing stages.
•Review requirements, file layouts, data dictionaries, mappings, transformation rules, and data lineage.
•Develop test strategies, test cases, validation queries, and traceable QA evidence.
•Perform source-to-target, file-to-table, table-to-table, and table-to-file reconciliation .
•Validate record counts, field-level values, aggregates, calculations, transformations, filtering, enrichment, standardization, and default values.
•Test null handling, duplicates, data loss, referential integrity, matching, de-duplication, mastering, and survivorship rules.
•Validate inbound and outbound files for structure, headers, delimiters, data types, field lengths, required fields, encoding, and naming conventions.
•Create controlled test files and synthetic data to validate business and transformation rules.
•Design positive, negative, boundary, edge-case, regression, reload, and recovery scenarios.
•Investigate data-quality issues and identify root causes across source data, requirements, mappings, configurations, transformations, and downstream processing.
•Develop reusable SQL queries, scripts, reconciliation checks, and automated data-quality validations.
•Investigate production data issues and support post-deployment validation and regression testing.
•Collaborate with Data Engineering, Business Analysis, Product, Operations, and other technical teams to improve pipeline testability and diagnostics.
•Participate in client and data-vendor onboarding by reviewing specifications, sample files, mappings, and expected outputs.
•Identify requirements gaps, data-quality risks, ambiguities, and testability concerns during onboarding.
•Communicate testing status, defects, risks, blockers, and production-readiness findings to technical and non-technical stakeholders.
•Document and manage defects in Jira with supporting SQL, test evidence, and sample records.
•Lead complex ETL QA initiatives independently, defining testing strategy, scope, approach, and quality standards for critical data pipelines.
•Mentor and provide technical guidance to QA engineers on SQL validation, data reconciliation, test design, defect investigation, and ETL best practices.
•Drive continuous improvement of ETL QA processes by identifying automation opportunities, improving reusable validation frameworks, and strengthening data-quality and production-readiness checks.
