Senior Data Integration Engineer

Insight Global; Cincinnati (OH); On-site; USD 110,000 - 150,000; Full time

Job summary

Insight Global in Cincinnati is seeking a Senior Data Integration Engineer to join a financial client in a direct-hire capacity. This role sits within data quality, testing, and validation, 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.

Qualifications

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

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, and support releases with confidence in data integrity.
• Support and validate API- & 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.

Skills

Data validation

SQL

Data quality

Data testing

ETL validation

NoSQL

Relational databases

Tools

Tableau

Cognos

MongoDB

Kafka

Job description

Job Description

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-&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.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [e-mail] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

• 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).