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About the role
Key Responsibilities Own end-to-end data validation for pipelines and database changes, ensuring data accuracy and reliability for downstream systems. Design and execute database-centric test plans for data feeds, integrations, migrations, and batch processes (incremental and full loads). Define and document data quality rules and validation criteria for priority datasets and flows (e.g., completeness, accuracy, referential integrity, and timeliness) and translate them into repeatable checks. Build and maintain reconciliation and completeness checks across sources and targets, including counts, anti-joins, key-level diffs, field-level mapping checks, and anomaly or outlier checks. Validate and regression-test stored procedures, functions, and triggers, including selection logic that determines which records are picked up for processing or sync, how transformations are applied, and how exceptions are generated and handled. Develop test cases for edge conditions, malformed or invalid data, and exception paths; validate error-handling and correction workflows. Validate restart and re-run safety (idempotency), including delta- or timestamp-based processing, recovery from partial failures, and protected reprocessing patterns. Investigate and help prevent recurring issue patterns tied to locking, deadlocks, or contention; test batching strategies and concurrency impacts and validate secure transaction handling patterns. Support troubleshooting with engineering and SRE using logs, monitoring, and operational signals such as job run histories, exception queues or DLQ patterns (where applicable), correlation IDs, and timestamps. Document test approaches and results and track defects and risk in standard tooling (for example, Jira and Confluence or equivalents). Engage early with cross-functional teams during development to identify data risks, align on validation criteria, and provide testing guidance and reusable validation approaches that protect data integr .
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