Senior Data Quality Engineer

A financial client in the Cincinnati area is seeking a Senior Data Integration Engineer to join their team in a direct-hire capacity. This role sits firmly within the data quality, testing, and validation function, supporting enterprise data platforms rather than building ETL pipelines.

The Senior Data Integration Engineer partners closely with data engineering, analytics, and business teams to ensure data accuracy, completeness, consistency, and reliability across operational and analytical systems.

This role plays a key part in standardizing data validation practices, improving data quality maturity, and mentoring other team members while remaining hands-on.

Key Responsibilities

• Lead and standardize enterprise-level data validation and data quality testing strategies, frameworks, and best practices.

• Serve as a technical leader for data testing and assurance, providing guidance on validation approaches, reconciliation methods, and defect prevention.

• Design, execute, and maintain automated and manual data validation tests for ETL pipelines, data warehouses, and operational data stores.

• Perform deep SQL-based data analysis to identify discrepancies, data quality issues, and transformation errors across systems.

• Validate data across relational and NoSQL platforms, ensuring alignment between source systems and downstream consumers.

• Partner with engineering, analytics, and release teams to:

• Align testing timelines

• Validate data readiness

• Support releases with confidence in data integrity

• Support and validate API- and streaming-based data integrations as part of modern data architectures.

• Contribute to BI and reporting validation, ensuring accurate and trusted datasets for Tableau, Cognos, and similar tools.

• Mentor junior and mid-level data integration engineers, promoting strong data quality, testing, and validation discipline.

• Drive continuous improvement in data testing processes, tooling, automation, and quality metrics.

• Experience in data validation, data quality, or data testing (not primarily ETL development).

• Strong SQL skills for validating data, reconciling sources, and finding data issues.

• Experience validating ETL or data pipeline outputs to ensure data accuracy and consistency.

• Experience working with data warehouses, data marts, and ODS environments.

• Experience validating BI and reporting data in tools such as Tableau or Cognos.

Experience working with relational and NoSQL databases (MongoDB preferred) and understanding core data concepts like aggregation, merging, and transformations within the testing lifecycle. * Experience validating streaming or API-based data (e.g., Kafka).

• Hands-on experience tracking and resolving data defects and data quality issues.

• Familiarity with enterprise data warehouse architectures.

Experience leading or helping standardize data testing practices (no people management required).