Data Engineering Manager

Lower•Columbus, OH

1d•$125,000 - $185,000•Hybrid

About The Position

The Engineering Manager will lead and grow a high-impact team of data engineers while remaining hands-on in the codebase. This role blends people leadership, technical execution, and architectural stewardship. The Engineering Manager is responsible for guiding a team of three data engineers, setting technical direction, planning and prioritizing work, and contributing directly through individual contributor efforts. This is an ideal opportunity for a senior engineer or manager who enjoys mentoring others, improving systems, and still spending meaningful time building pipelines, reviewing code, and solving complex data problems. The role plays a critical part in shaping the future of our data platform as we scale, modernize, and deepen our impact across the business.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
6+ years of experience in data engineering, analytics engineering, or related roles.
Strong hands-on experience with cloud data warehouses (Snowflake or equivalent).
Expert-level SQL skills, including query optimization and analytical data modeling.
Strong Python experience for data pipeline development, automation, and orchestration.
Experience implementing ELT/ETL workflows using managed tools (e.g., Fivetran) and custom API-based ingestion solutions.
Solid understanding of data modeling, pipeline orchestration, and analytics enablement.
Experience reviewing code, improving architecture, and managing technical debt.
Strong communication skills and ability to balance technical depth with business context.

Nice To Haves

Prior experience leading or mentoring engineers (formal management experience preferred but not required).
Mortgage, real estate, or financial services industry experience is a strong plus.

Responsibilities

Team Leadership & Management
Manage, mentor, and support a team of data engineers, fostering growth, accountability, and strong engineering practices.
Plan, prioritize, and groom team work through sprint planning, backlog refinement, and capacity management.
Conduct regular 1:1s, provide feedback and coaching, and support career development for team members.
Partner with cross-functional leaders to align engineering priorities with business needs.
Hands-On Technical Contribution
Act as a player-coach by owning and delivering individual contributor work alongside the team.
Design, build, and maintain scalable data pipelines using tools such as Snowflake, Fivetran, SQL, Python, and custom API integrations.
Review pull requests, enforce coding standards, and ensure high-quality, maintainable, and well-documented code.
Troubleshoot and resolve data pipeline issues, performance bottlenecks, and reliability concerns.
Architecture & Platform Ownership
Own and evolve the data engineering architecture with a focus on scalability, reliability, and simplicity.
Identify and reduce technical debt, improve repository structure, and introduce best practices across the codebase.
Partner closely with the analyst side of the team to ensure datasets are well-modeled, performant, and analysis-ready.
Contribute to tooling, process improvements, and documentation that increase team velocity and data quality.
Stakeholder Collaboration
Work directly with analyst teammates, product, marketing, and business stakeholders to understand data needs and translate them into technical solutions.
Provide technical guidance and context to non-technical partners.
Proactively identify opportunities where data engineering can unlock new insights or efficiencies.

Benefits

Extended benefit offerings including medical/dental/vision, parental leave, life insurance, short- and long-term disability
Paid holidays and paid time off
401K with company match
Discount on home mortgage refinances or purchase

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