Data Quality Engineer

This role requires advanced SQL expertise to analyze complex datasets, build validation queries, identify discrepancies, and troubleshoot data quality issues across enterprise data platforms. The Data Quality Engineer will work closely with data engineers, data modelers, architects, and business stakeholders to establish data quality standards, validate data transformations, and support reporting, analytics, and data modernization initiatives.

• 5+ years of experience in data quality engineering, SQL development, data analysis, data engineering, or a related field. \n

• Advanced SQL development experience within enterprise database environments. \n

• Hands-on experience with data validation, reconciliation, profiling, and troubleshooting. \n

• Experience working with relational databases, data warehouses, and ETL/ELT processes. \n

• Demonstrated ability to identify and resolve complex data quality issues across multiple systems. \n

• Strong analytical, problem-solving, and technical documentation skills. \n

• Ability to collaborate with technical and business stakeholders and translate requirements into executable data validation rules. \n

• Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, Mathematics, or a related field preferred. Equivalent professional experience may be considered.

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• Develop and optimize complex SQL queries for data profiling, validation, reconciliation, and quality assessments across enterprise databases and data warehouses. \n

• Design and maintain SQL-based data quality checks to identify missing, duplicate, inaccurate, inconsistent, or improperly formatted data. \n

• Validate data accuracy and completeness throughout ETL/ELT pipelines, ensuring data integrity across source systems, staging environments, and target platforms. \n

• Perform source-to-target data reconciliation to verify data transformations, business rules, and mapping requirements. \n

• Develop automated data quality tests to proactively identify and report data anomalies and integrity issues. \n

• Investigate data quality issues, conduct root cause analysis, and collaborate with engineering teams to implement corrective solutions. \n

• Support enterprise data platforms, including Snowflake, Azure SQL Managed Instance (SQL MI), Azure Data Factory (ADF), and Denodo. \n

• Analyze large datasets to identify data quality trends, inconsistencies, and opportunities for improvement. \n

• Partner with technical and business stakeholders to define data quality requirements, validation rules, and acceptance criteria. \n

• Maintain data quality documentation, test cases, validation scripts, and issue-tracking records. \n

• Establish and monitor data quality metrics related to accuracy, completeness, consistency, uniqueness, validity, and timeliness. \n

• Participate in data integration testing, system migrations, release validation, and enterprise data modernization initiatives. \n

• Support data governance and metadata management initiatives by identifying quality risks and recommending improvements to enterprise data standards. \n

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