Data Engineer

We are looking for an experienced Data Engineer to support enterprise-scale data initiatives for a financial services environment in Cincinnati, Ohio. This Long-term Contract position will focus on building reliable data foundations, shaping data structures that support investment and portfolio reporting, and partnering with business and technology leaders to solve complex information challenges. The ideal candidate brings deep expertise in modern data engineering, strong knowledge of financial data domains, and the ability to turn strategic needs into scalable technical solutions.

Responsibilities:

• Design and maintain scalable data pipelines that collect, transform, and deliver high-value business and financial data across enterprise platforms.
• Develop conceptual, logical, and physical data models that improve consistency, usability, and long-term management of critical information assets.
• Collaborate with executive stakeholders, business teams, and technical partners to define data solutions aligned with operational and strategic objectives.
• Establish and strengthen practices for data governance, stewardship, metadata management, lineage tracking, and data quality oversight.
• Translate complex business questions related to funds, investments, portfolio companies, and investors into effective engineering and data architecture solutions.
• Build and optimize ETL workflows using technologies such as Python, Apache Spark, Hadoop, and Kafka to support large-scale data processing.
• Partner across business and technology functions to ensure data platforms meet regulatory, privacy, risk, and compliance expectations within a financial services setting.
• Analyze enterprise data issues, identify improvement opportunities, and implement solutions that enhance reliability, accessibility, and reporting accuracy.
• 10+ years of experience in data engineering, data architecture, data strategy, data modeling, data governance, or enterprise data management.
• Background working within private equity, asset management, investment management, alternative investments, or broader financial services environments.
• Strong understanding of financial data domains, including fund structures, investment activity, portfolio company information, investor records, and related reporting data.
• Proven ability to convert business challenges into practical enterprise data solutions that support decision-making and operational performance.
• Hands-on experience designing conceptual, logical, and physical data models for complex data ecosystems.
• Solid knowledge of data governance disciplines, including stewardship, metadata, lineage, and data quality management.
• Proficiency with Python, Apache Spark, Apache Hadoop, Apache Kafka, and ETL development.
• Bachelor’s degree required in Business, Finance, Computer Science, Data Science, Engineering, or a related field; advanced degree preferred.