Data Platform Engineer Jobs in Ohio
Data Platform Engineer jobs in Ohio are in active demand, concentrated in financial services, healthcare systems, manufacturing technology, and logistics, with openings at every level from junior engineers to principal architects. Columbus, Cleveland, and Cincinnati are the primary hiring centers, where employers like Nationwide, Progressive, and Kroger Technology maintain established engineering teams that rely on data platform expertise. The most sought-after specialties in Ohio listings include cloud data infrastructure, real-time pipeline development, and data lakehouse architecture. Find a role that fits below and apply directly.
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Your Opportunity as the Data Architect, Enterprise Data Platform
The Data Architect is responsible for designing and maintaining enterprise data models that support analytics, reporting, and data-driven decision making. This role ensures that data structures are scalable, consistent, and aligned to business needs, while supporting efficient data consumption across the organization. The Data Architect contributes to enterprise analytics data governance and modeling standards by defining data structures, validating implementations, improving model quality and consistency, and working across data domains to ensure alignment between source data, engineered data layers, and published analytics assets.
This role applies and enforces dimensional modeling best practices using star schema design, including fact and dimension tables, conformed dimensions, and standardized metrics. Data models are designed to support a unified semantic layer and enable accurate, consistent reporting across tools such as Tableau.
Work Arrangements: Hybrid - onsite a minimum of 9 days a month primarily during core weeks as determined by the Company; maybe more as business need requires
In this role you will:
Data Modeling & Design
Design, develop, and maintain dimensional data models using star schema methodology, including defining fact tables, dimension tables, grain, and relationships that support enterprise analytics and reporting.
Ensure models are optimized for performance, scalability, and usability.
Create and maintain conceptual, logical, and physical data models using appropriate modeling tools.
Analyze and profile new data sources to understand structure, quality, relationships, and business context, informing appropriate modeling and architecture decisions.
Model Quality & Standards
Apply enterprise data modeling standards and best practices across all solutions.
Validate data models for consistency, accuracy, and alignment with business requirements.
Identify and resolve issues related to duplication, inconsistency, poor model design, or data quality concerns that impact analytics and reporting.
Improve data model usability and clarity for downstream analytics and reporting.
Represent the Enterprise Data Platform team in architecture review boards and design reviews, providing guidance on data modeling, semantic consistency, and analytics architecture considerations.
Semantic Alignment & Analytics Support
Establish and maintain a consistent semantic layer, including standardized metrics, dimensions, and business logic that support trusted analytics and reporting.
Align data models with reporting requirements, certified data sources, and enterprise analytics standards.
Support analytics teams by providing clear, well-structured, and consumable data models.
Data Architecture & Solution Design
Develop and guide data architecture decisions related to data structures and design patterns.
Collaborate with Data Engineers to ensure data pipeline implementations align with the intent of approved data models, enterprise standards, and architectural best practices while meeting performance and scalability requirements.
Partner with Data Owners, domain experts, engineers, and analytics teams to align data models with business processes, priorities, and enterprise standards.
Recommend improvements to data design, storage, and structure.
Ensure alignment across source, transformed, and published data layers.
Governance & Documentation
Support metadata, lineage, and documentation standards.
Define and document data models, including structure, definitions, and usage guidance.
Ensure models align with governance standards for ownership, classification, and compliance.
Contribute to improving discoverability and trust in enterprise data.
Collaborate with platform, governance, and security teams to ensure data models and architecture designs align with enterprise security, privacy, data classification, and access control standards.
What we are looking for
Minimum Requirements:
Bachelor’s degree, equivalent experience or specialized training in Information Technology
8+ years of experience in data modeling, data architecture, analytics, or senior data engineering environments
Demonstrated ability to collaborate effectively across technical teams, business stakeholders, and data domain partners to drive alignment and adoption
Advanced SQL skills and experience working with large datasets
Experience designing data models, metadata structures, and semantic foundations that support trusted analytics, reporting, and emerging AI use cases
Experience with Databricks, lakehouse architectures, or similar modern cloud data platforms
Strong understanding of data structures, relationships, and performance optimization
Ability to think critically and conceptually, communicate complex data architecture topics clearly, and adapt recommendations for both technical and nontechnical audiences
Additional skills and experience that we think would make someone successful in this role (not required):
Experience creating and maintaining conceptual, logical, and physical data models using enterprise modeling tools such as ER/Studio, Erwin, or equivalent platforms
Experience leveraging metadata management, data catalog, lineage, and governance capabilities to improve data discoverability, traceability, and trust across enterprise analytics environments, including platforms such as Atlan or similar solutions
Experience working across multiple areas of the analytics lifecycle, including data engineering, data modeling, and business intelligence/reporting solutions
Familiarity with Python and modern data engineering workflows
Familiarity with source control and collaborative development practices (e.g., Git, GitHub, Azure DevOps)
Understanding of modern data platform concepts and workflows
Understanding of how data architecture, metadata, and governance enable trusted analytics and AI solutions
The Right Place for You
We are bold, kind, strive to do the right thing, we play to win, and we believe in a strong community that thrives together. Our culture is rooted in our Basic Beliefs , and we believe in supporting every employee by meeting their physical, emotional, and financial needs.
We're an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, genetic information, age, national origin, disability status or protected veteran status.
See All 6 Data Platform Engineer Jobs in Ohio
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Find Data Platform Engineer JobsData Platform Engineer Jobs by City in Ohio
Where Ohio roles are concentrated, by current openings.
Data Platform Engineer Job Market in Ohio
A snapshot from current Ohio openings, updated as new roles post.
Who's Hiring



What Ohio Employers Look For
The qualifications that appear most often in data platform engineer jobs across Ohio.
- Bachelor's degree in computer science, data engineering, or a closely related technical field
- Hands-on experience building and maintaining data pipelines using tools such as Apache Spark or Kafka
- Proficiency with cloud data platforms including AWS, Azure, or Google Cloud in production environments
- Strong command of SQL and at least one scripting language such as Python or Scala
- Experience with data warehousing solutions like Snowflake, Databricks, or BigQuery in enterprise settings
- Familiarity with orchestration frameworks such as Apache Airflow or dbt for workflow automation
Data Platform Engineer Jobs in Ohio: Frequently Asked Questions
How do you become a data platform engineer in Ohio?
Ohio does not require a state-issued license or registration to work as a data platform engineer. The typical path starts with a bachelor's degree in computer science, information systems, or data engineering, followed by hands-on experience with cloud platforms and pipeline tools. Ohio employers, particularly in Columbus's tech corridor and Cleveland's healthcare-adjacent IT sector, value demonstrated project work, vendor certifications from AWS or Microsoft Azure, and familiarity with enterprise data governance practices.
How much do data platform engineers make in Ohio?
Data platform engineers in Ohio earn a median of about $132,910 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $94,530 for the lowest 10% to over $174,260 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire data platform engineers in Ohio?
Employers hiring data platform engineers in Ohio right now include Wasserstrom, Avery Dennison, and RevSpring, based on current listings on Migrate Mate as of September 2026. Ohio's concentration of large insurance carriers, regional banks, and integrated health systems means demand for data platform engineers remains consistent across both established enterprises and their technology subsidiaries.
Which Ohio cities have the most data platform engineer jobs?
Columbus, Mentor, and Orrville have the most data platform engineer openings in Ohio. Columbus leads due to its density of insurance, financial services, and retail technology headquarters, while Cleveland's large healthcare networks and Cleveland Clinic's analytics division drive significant demand, and Cincinnati's consumer goods and logistics companies anchor hiring in the southwest corner of the state.
Are there remote data platform engineer jobs in Ohio?
Yes, and more than most fields. About 67% of data platform engineer openings tied to Ohio are remote or hybrid as of September 2026, reflecting how fully desk-based and tool-driven this work is. Pipeline development, cloud infrastructure management, and data modeling are the parts of the role most consistently offered as fully remote, particularly at Ohio-headquartered companies with distributed engineering teams.
How can I get hired as a data platform engineer in Ohio with little or no experience?
The most realistic entry path is a junior or associate data engineer role, which Ohio employers in Columbus and Cleveland post regularly for candidates who can demonstrate a portfolio of pipeline or ETL projects built with open-source tools. Nationwide, Huntington Bancshares, and large regional health systems run structured new-grad hiring programs that place candidates with relevant coursework into data or analytics engineering rotations. Transitioning from a database administrator, business intelligence analyst, or analytics engineer position is a common lateral move, and an AWS Cloud Practitioner or Databricks Fundamentals credential gives early-career candidates a measurable edge.
Where can I find and apply to data platform engineer jobs in Ohio?
You can find and apply to data platform engineer jobs in Ohio on Migrate Mate, which lists current Ohio openings from employers across Columbus, Cleveland, Cincinnati, and beyond. Find roles that fit your experience and apply directly to each one.
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