Data Quality Engineer

Cognizant Technology Solutions•Dulles, VA

40d•$115,000 - $135,000

About The Position

We are seeking a highly experienced Data Validation Lead with 12+ years of expertise in Data Warehousing, Data Quality, and Data Engineering. The ideal candidate will have deep technical proficiency in SQL, Oracle, Big Query, and hands-on experience with Data Validation Tool. This role focuses on ensuring data integrity during system cutovers and migrations, troubleshooting discrepancies, and driving continuous improvement in data quality frameworks.

Requirements

Minimum of 12+ years of experience in Data Warehousing, Data Quality, or Data Engineering with emphasis on data validation.
Expert-level proficiency in SQL, Oracle, and Big Query for data comparison and reconciliation.
Deep knowledge of the full data migration lifecycle, including ETL/ELT processes.
Proven ability to troubleshoot data quality issues and perform RCA in high-pressure environments.

Responsibilities

Design and implement robust data validation processes and scripts for source-to-target comparison during system cutovers and separations.
Write, optimize, and execute complex Oracle, SQL, and Big Query queries and stored procedures across heterogeneous database environments.
Perform schema and count validation, including record counts, schema fidelity, data types, and key relationships post-migration.
Develop validation checks to confirm business transformation logic and custom mappings are correctly applied.
Investigate and resolve data discrepancies, performing root cause analysis (RCA) for mismatches between source and target systems.
Collaborate with Data Engineering, Application Development, and Business teams to communicate and resolve data quality issues.
Maintain detailed logs of identified data issues, including reproduction steps and resolution progress.
Leverage Data Validation Tool (Ignition) for validation and reporting.
Contribute to automation of data validation test cases and reporting metrics.
Establish KPIs for measuring data quality and migration success.