Sr Data Integration Engineer

Job Description

A financial client in the Cincinnati area is looking for a Sr Data Integration Engineer to join their team for a direct hire opportunity. The Senior Data Integration Engineer plays a critical leadership role in designing, testing, and maintaining high‑quality data integration solutions across enterprise data platforms. This role partners closely with engineering, analytics, and business stakeholders to ensure data reliability, scalability, and consistency across ETL pipelines, operational data stores, data warehouses, and downstream BI solutions.

This individual leads data integration testing strategy, drives continuous improvement in data quality practices, and mentors junior engineers while remaining hands‑on in complex technical environments.

• Lead and standardize enterprise‑level data integration and data quality testing strategies, frameworks, and best practices.
• Serve as a technical leader on data integration initiatives, providing guidance on ETL validation, data reconciliation, and quality assurance.
• Design, execute, and maintain automated testing for ETL pipelines, data warehouses, and operational data stores using Informatica and related tools.
• Develop and maintain scalable, reusable data testing frameworks to improve efficiency, accuracy, and long‑term sustainability.
• Partner with engineering, analytics, and release teams to manage data requests, align testing timelines, and ensure readiness across integrated environments.
• Validate data across relational and No
  
  SQL data stores, ensuring accuracy, completeness, and consistency.
• Perform complex SQL‑based data analysis and validation across ODS, data marts, and enterprise data warehouses.
• Support and validate API‑ and streaming‑based integrations (e.g., Kafka) as part of modern data architectures.
• Contribute to BI and reporting validation, ensuring trusted data for tools such as Tableau or Cognos.
• Mentor and coach junior and mid‑level data integration engineers, fostering technical growth and adherence to enterprise standards.
• Drive continuous improvement in testing processes, tooling, and automation practices.

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.

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• 7+ years of experience in data integration, data quality, or data engineering roles with a strong testing and validation focus.
• Strong hands‑on experience with ETL processes and tools (Informatica preferred).
• Advanced proficiency in SQL, including writing and optimizing complex queries for data validation and reconciliation.
• Hands‑on experience with No
  
  SQL databases (Mongo
  
  DB strongly preferred; other No
  
  SQL technologies acceptable).
• Proven experience validating data across relational databases, No
  
  SQL stores, ODS, data marts, and data warehouses.
• Solid understanding of data modeling and data warehousing concepts, including OLTP vs. OLAP, star schemas, fact and dimension tables.
• Experience working within the Software Testing Life Cycle (STLC) in complex enterprise environments.
• Strong leadership skills with experience guiding technical direction and mentoring peers.
• Experience with Kafka or other event‑streaming platforms, including validation of API‑ or stream‑based data flows.
• Exposure to BI testing and reporting validation using tools such as Tableau or Cognos.
• Experience validating data transformations involving cleansing, aggregation, merging, and scrubbing.
• Familiarity with enterprise‑scale, federated, or centralized data warehouse architectures.