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Manager Data Engineering Jobs in Ohio (NOW HIRING)

FMHC

AlloBaas - Data Engineering Lead

Lakewood, OH · On-site

The Data Engineering Lead plays a critical and strategic role in advancing FMHC's enterprise data ... Oversee table structures, role and permission management, performance tuning, cost optimization ...

Pwc

Data Engineer - Manager

Columbus, OH · On-site

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary ... In data engineering at PwC, you will focus on designing and building data infrastructure and ...

Pwc

Data Engineer - Manager

Cleveland, OH · On-site

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary ... In data engineering at PwC, you will focus on designing and building data infrastructure and ...

Guidehouse

Data Engineer Lead

Beavercreek, OH

Define technical architecture, engineering standards, and best practices for data integration ... Experience implementing and managing CI/CD pipelines, infrastructure automation, and DevOps ...

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All JobsData Engineering JobsManager Data Engineering JobsManager Data Engineering Jobs in Ohio

Manager Data Engineering information

See Ohio salary details

$29.5K

$92.4K

$163.5K

How much do manager data engineering jobs pay per year?

As of Aug 28, 2026, the average yearly pay for manager data engineering in Ohio is $92,355.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,700.00 and $119,300.00 per year, depending on experience, location, and employer.

What are the roles and responsibilities of a manager data engineering?

A Manager Data Engineering oversees teams that design, build, and maintain data infrastructure and pipelines for organizations. They are responsible for ensuring the efficient flow and storage of data, implementing best practices in data management, and collaborating with stakeholders to meet business data needs. Additionally, they mentor and guide data engineers, manage project timelines, and ensure data security and quality standards are met. Their role often involves strategic planning to enable data-driven decision making across the company.

What are the key skills and qualifications needed to thrive as a manager data engineering?

To thrive as a Manager Data Engineering, you need expertise in data architecture, advanced analytics, and leadership, typically supported by a degree in computer science or a related field. Familiarity with big data tools (like Hadoop, Spark), data warehousing systems, cloud platforms (AWS, Azure), and certifications such as AWS Certified Data Analytics are highly valued. Strong communication, problem-solving, and team management skills help drive project success and foster collaboration. These skills ensure effective data solutions, alignment with business goals, and the ability to lead and grow high-performing engineering teams.

How does a manager data engineering typically collaborate with data scientists and business stakeholders?

A Manager of Data Engineering often serves as a bridge between technical teams and business stakeholders. They work closely with data scientists to ensure that data pipelines and infrastructure meet analytical needs, while also translating business requirements into actionable engineering solutions. Regular coordination meetings, clear documentation, and cross-functional projects are common, enabling seamless collaboration and alignment on goals. This role requires strong communication skills and the ability to balance technical priorities with business objectives.

What is the difference between Manager Data Engineering vs Data Engineer?

AspectManager Data EngineeringData Engineer
Required CredentialsBachelor's or Master's in CS, Data Science, or related; often leadership experienceBachelor's or higher in CS, IT, or related; technical certifications optional
Work EnvironmentTeam leadership, project management, strategic planningData pipeline development, coding, data modeling
Employer & Industry UsageTech companies, finance, healthcare, where data teams are commonData-focused roles across various industries

The main difference is that Manager Data Engineering oversees data teams and projects, focusing on strategy and leadership, while Data Engineers handle the technical implementation of data pipelines and infrastructure. Managers typically have more experience and leadership skills, whereas Data Engineers are more hands-on with coding and data architecture.

What are the most commonly searched types of Data Engineering jobs in Ohio?

The most popular types of Data Engineering jobs in Ohio are:

What are popular job titles related to Manager Data Engineering jobs in Ohio?

For Manager Data Engineering jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Manager Data Engineering jobs in Ohio look for?

The top searched job categories for Manager Data Engineering jobs in Ohio are:

What cities in Ohio are hiring for Manager Data Engineering jobs?

Cities in Ohio with the most Manager Data Engineering job openings:

Manager Data Engineering jobs near you
Infographic showing various Manager Data Engineering job openings in Ohio as of August 2026, with employment types broken down into 90% Full Time, 9% Part Time, and 1% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $92,355 per year, or $44.4 per hour.

AlloBaas - Data Engineering Lead

First Mutual Holding Co.

Lakewood, OH • On-site

Full-time

Re-posted 5 days ago

Job description

Job Type
Full-time
Description
Position Summary:
This position is under the AlloBaas subsidiary of FMHC.
The Data Engineering Lead plays a critical and strategic role in advancing FMHC's enterprise data transformation and analytics enablement efforts. This role provides technical leadership, direction, and oversight for data engineering activities across data platforms, data pipelines, integrations, data warehouse operations, middleware/Azure Enterprise Service Bus capabilities, reporting modernization, and enterprise data governance practices.
In partnership with business stakeholders and third-party vendors, the Data Engineering Lead is responsible for guiding the design, development, support, and continuous improvement of scalable, secure, reliable, and well-documented data solutions. This role provides day-to-day technical direction to Data Engineer I and Data Engineer II roles, supports their professional development, and serves as a primary escalation point for complex technical, operational, and data quality issues.
Duties and Responsibilities:
  • Leadership & Strategic Oversight.
  • Lead and mentor a team of Data Engineering and Analyst staff, providing coaching, performance feedback, and professional development opportunities.
  • Serve as the primary escalation point for complex data engineering, data platform, integration, data quality, and production support issues.
  • Establish and communicate data engineering standards, best practices, development patterns, documentation expectations, and operational procedures.
  • Lead regular technical planning, solution review, and prioritization discussions to ensure timely execution of data engineering initiatives.
  • Promote a collaborative, high-performing team culture focused on accountability, quality, continuous improvement, operational excellence, and knowledge sharing.
  • Support professional development of data engineering team members through coaching, feedback, technical guidance, and skill development opportunities.
  • Serve as the primary escalation point for team-level challenges, providing guidance,resolution, and technical mentorship as needed.
  • Collaborate with leadership to define key product objectives, KPIs, and performance outcomes.

-Data Platform Ownership
  • Lead and assist in administration, optimization, and operational oversight of Snowflake and related cloud data platforms.
  • Oversee table structures, role and permission management, performance tuning, cost optimization, capacity planning, and platform configuration standards.
  • Provide leadership for Data Warehouse, Middleware/Azure Enterprise Service Bus, Cognos, Limagito, and related analytics platform operations.
  • Ensure data platforms are scalable, secure, reliable, cost-effective, and aligned with business and technology goals. Recommend, implement, and maintain best practices for data platform engineering, platform operations, and environment management.
  • Oversee bug tracking, error resolution, and continuous optimization
  • Design, implement, and oversee intake and delivery processes related to Business Intelligence, Analytics, Reporting, and Data Provider sourcing initiatives.
  • Translate product roadmap features into well-defined requirements, user stories, and acceptance criteria.
  • Support creation of test scripts, QA planning, and post-launch performance tracking Data Pipeline, Integration & Architecture Leadership
  • Lead and assist in implementing the design, development, implementation, and support of scalable data pipelines, data conversions, ingestion processes, integrations, and data vendor feeds.
  • Oversee data extraction, ingestion, transformation, normalization, anonymization, validation, loading, and reconciliation processes.
  • Guide integration activities involving APIs, middleware, Azure ESB, core banking systems, digital banking platforms, and third-party data sources.
  • Maintain and communicate enterprise data architecture documentation, including data flows, system dependencies, integration diagrams, platform architecture, and process maps.
  • Data Quality, Governance, Security & Compliance
  • Lead the implementation and continuous improvement of data validation, reconciliation, monitoring, and quality control processes.
  • Establish standards for data quality checks, issue tracking, data lineage, data mappings, business rules, and data documentation.
  • Enforce data governance policies to ensure data is secure, consistent, accurate, reliable, and compliant with applicable financial regulations and cybersecurity standards.
  • Ensure appropriate controls are considered in data access, data sharing, reporting, integration, and platform administration processes.
  • Operational Support & Continuous Improvement & Non-Technical Ownership
  • Assist with internal stakeholder relationships and partner to ensure alignment and best practice implementation (e.g., marketing, contact center, compliance, fraud, risk).
  • Oversee and track project and program roadmaps, identifying risks, dependencies, and cross-functional impact. Lead backlog prioritization in alignment with business goals and customer feedback.
  • Manage and coordinate with third-party vendors, implementation partners, and technology providers to support data integrations, platform enhancements, issue resolution, and ongoing operations. Ensure high performance of vendors by monitoring work, evaluating contracts and assessing vendor options.
  • Ensure timely delivery of all risk assessments, required reporting, performance metrics, and strategic insights.
  • Lead and assist in operational support practices for data platforms, pipelines, integrations, reporting systems, and related data services including ITSM and documentation best practices.
  • Support continuous improvement and automation of governance practices, operational controls, and data management maturity across the organization.
  • Ensure upstream & downstream data requirements are identified and addressed. Ensure timely resolution of escalated platform, pipeline, data quality, reporting, and integration issues.
  • Develop and improve monitoring, alerting, support documentation, production readiness, release support, and change management practices. Support testing, QA planning, validation, deployment readiness, and post-implementation review for data-related changes.
  • Stay informed of industry trends and emerging technologies to drive innovation and competitive advantage
  • Monitor and manage platform consumption expenses and budgets.
  • Complies with all applicable banking laws and regulations, including, but not limited to the Bank Secrecy Act, USA Patriot Act, and related anti-money laundering statutes, and federal consumer protection legislation and regulations. Builds working knowledge of all applicable laws and regulations.
  • Other duties as required.

The duties outlined above are a summary and may not be an exhaustive or comprehensive list of all possible responsibilities, tasks, and duties. All job descriptions may be amended at any time at the sole discretion of FMHC.
Requirements
Qualifications and Skills:
  1. 5+ years of progressive experience in data engineering, data platform engineering, data warehouse operations, ETL/ELT development, data integration, or related data technology roles.
  2. 3+ years of experience providing technical leadership, functional work direction, mentorship, solution review, or escalation support for data engineering or technology teams.
  3. 5+ years of hands-on experience designing, developing, supporting, and optimizing data pipelines, ingestion processes, data conversions, data quality processes, and data loading patterns.
  4. 5+ years of experience with SQL, relational databases, data modeling, data mapping, data validation, reconciliation, and data analysis.
  5. 3+ years of hands-on experience administering, supporting, or optimizing Snowflake or comparable cloud data platforms.
  6. Experience with cloud and/or on-premises database environments, including platform configuration, access management, performance considerations, and operational support.
  7. Experience with data integration technologies, APIs, middleware, Azure Enterprise Service Bus, or similar integration platforms.
  8. Experience with development lifecycle practices and tools such as C#, GitHub or similar source control, Azure DevOps, CI/CD pipelines, and deployment management.
  9. Experience with reporting, analytics, and business intelligence environments such as Cognos, Tableau, Power BI, or similar tools.
  10. Experience leading or supporting data migration, reporting modernization, platform implementation, and data validation initiatives.
  11. Demonstrated ability to document and communicate enterprise data architecture, system dependencies, data flows, business rules, technical requirements, process maps, and support procedures.
  12. Demonstrated ability to translate business needs into scalable, secure, reliable, and well-documented technical data solutions.
  13. Strong understanding of data governance, data security, data quality, regulatory compliance, and cybersecurity considerations in a financial services environment.
  14. Bachelor's degree in Information Technology, Computer Science, Data Analytics, Business Information Systems, Engineering, or a related field, or equivalent combination of education and experience.

Preferred Experience Includes:
  1. Prior work within banking, fintech, mortgage, insurance, or other regulated financial services environments.
  2. Experience supporting data engineering capabilities for digital banking, core banking, customer-facing financial platforms, or enterprise analytics.
  3. Experience with Snowflake cost optimization, performance tuning, role-based access controls, data sharing, and cloud data platform governance.
  4. Experience with Azure data services, Azure DevOps, Azure Enterprise Service Bus, APIs, middleware, and enterprise integration patterns.
  5. Experience with Lean/Six Sigma, process analysis and design, systems architecture, or operational process improvement.
  6. Experience with Agile and Waterfall delivery methodologies.
  7. Experience with collaboration and work management tools such as Smartsheet, Jira, Confluence, Miro, or similar.
  8. Experience developing standards, reusable patterns, technical documentation, production support procedures, and team operating practices.
  9. Experience coordinating vendor relationships, technical escalations, implementation partners, or third-party data providers.
  10. Experience supporting audit, regulatory, risk, compliance, cybersecurity, or data governance activities within a financial institution.

Necessary Competencies:
  1. Critical thinking
  2. Leads Courageously
  3. Initiative
  4. Creativity
  5. Communication
  6. Organizational Skills
  7. Interpersonal Awareness
  8. Decisiveness

Physical Environment:
  • This position is performed in a corporate office (Lakewood, OH), hybrid, or remote setting:
  1. If fully remote: must be willing to travel
  • This position will requires the ability to work flexible days/times including occasionally working beyond normal business hours on an "as needed" basis.
  • While performing the duties of this job, the employee is regularly required to lift, walk, stand, sit, bend, reach with hands and arms, climb, push/pull, use hands, and see, hear and speak.
  • The employee must occasionally lift and/or move up to 25 pounds.
  • The noise level in the work environment is usually quiet to moderate.

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