1

Data Analytics Engineer Jobs in Ohio (NOW HIRING)

The position serves as a technical authority and strategic partner across data engineering, analytics, and business teams. This role will be measured by its contribution to business outcomes such as ...

The position serves as a technical authority and strategic partner across data engineering, analytics, and business teams. This role will be measured by its contribution to business outcomes such as ...

The position serves as a technical authority and strategic partner across data engineering, analytics, and business teams. This role will be measured by its contribution to business outcomes such as ...

The position serves as a technical authority and strategic partner across data engineering, analytics, and business teams. This role will be measured by its contribution to business outcomes such as ...

The position serves as a technical authority and strategic partner across data engineering, analytics, and business teams. This role will be measured by its contribution to business outcomes such as ...

Strong analytical, problem-solving, and organizational skills * Strong verbal and written ... Work with analysts, developers, and business stakeholders to understand data requirements and ...

Data Analyst / Engineer Location: Raymond, OH (Hybrid 4 days/week onsite and 1 day/week remote) Contract Duration: 14+ months Rate: $57/hr on W2 What will this person be working on In this role ...

Partner with multiple developers and stakeholders to understand modeling data requirements and help ... Proficiency in using analytical tools (SQL, Python, SAS, Visual Basic) and Microsoft Office Suite ...

next page

Showing results 1-20

Data Analytics Engineer information

See Ohio salary details

$42.3K

$123.3K

$168.7K

How much do data analytics engineer jobs pay per year?

As of Jul 14, 2026, the average yearly pay for data analytics engineer in Ohio is $123,321.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,900.00 and $130,700.00 per year, depending on experience, location, and employer.

How do Data Analytics Engineers typically collaborate with data scientists and business stakeholders on projects?

Data Analytics Engineers play a crucial role in bridging the gap between raw data and actionable insights by building, optimizing, and maintaining data pipelines. They often work closely with data scientists to ensure data is clean, accessible, and structured for advanced analytics or machine learning models. Additionally, they collaborate with business stakeholders to understand reporting requirements and ensure that data solutions align with organizational objectives. Regular communication and cross-functional teamwork are essential aspects of this role, as engineers must translate business needs into technical specifications and deliver reliable data products.

Can a data engineer make 200k?

Data engineers can earn $200,000 or more annually, especially with experience, advanced skills in cloud platforms, big data tools, and certifications. Salaries vary by location, industry, and company size, with senior roles and those in high-demand markets more likely to reach or exceed this level.

What engineers make $500,000?

Senior data analytics engineers with extensive experience, advanced skills in data modeling, machine learning, and proficiency with tools like Python, SQL, and cloud platforms can reach salaries of $500,000 or more, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires a combination of technical expertise, leadership roles, and sometimes equity compensation.

What are the key skills and qualifications needed to thrive as a Data Analytics Engineer, and why are they important?

To thrive as a Data Analytics Engineer, you need strong proficiency in data modeling, SQL, and statistical analysis, typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools such as Python, R, Apache Spark, Tableau, and cloud data platforms like AWS or Google BigQuery is essential, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills help you translate data insights into actionable business solutions. These skills and qualities are crucial for designing robust data pipelines and enabling data-driven decision-making across organizations.

Is 40 too late for data science?

Data Analytics Engineers and data science professionals can successfully transition into the field at age 40 or older, as skills such as programming, statistical analysis, and experience with tools like Python or SQL are valuable regardless of age. Many employers value diverse experience and lifelong learning, and certifications or online courses can help enhance credentials at any age.

What is the difference between Data Analytics Engineer vs Data Scientist?

AspectData Analytics EngineerData Scientist
CredentialsBachelor's or master's in CS, Data Science, or related fields; certifications like Google Data AnalyticsBachelor's or master's in CS, Statistics, or related fields; certifications like Certified Data Scientist
Work EnvironmentFocus on building data pipelines, dashboards, and analytics toolsFocus on statistical modeling, machine learning, and data exploration
Employer & Industry UsageUsed across tech, finance, healthcare for data infrastructure and analyticsCommon in research, product development, and advanced analytics teams

While both roles work with data, Data Analytics Engineers primarily develop data infrastructure and tools for analysis, whereas Data Scientists focus on statistical modeling and machine learning to generate insights. They often collaborate but have distinct technical focuses.

What does a data analytics engineer do?

A data analytics engineer designs, builds, and maintains data pipelines and systems to collect, process, and analyze large datasets. They use tools like SQL, Python, and cloud platforms to enable data-driven decision-making and often collaborate with data scientists and business teams to deliver actionable insights.
What are the most commonly searched types of Data Analytics Engineer jobs in Ohio? The most popular types of Data Analytics Engineer jobs in Ohio are:
What cities in Ohio are hiring for Data Analytics Engineer jobs? Cities in Ohio with the most Data Analytics Engineer job openings:
Infographic showing various Data Analytics Engineer job openings in Ohio as of July 2026, with employment types broken down into 1% Internship, 90% Full Time, 7% Part Time, and 2% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $123,321 per year, or $59.3 per hour.
Lead Data & Analytics Architect

Lead Data & Analytics Architect

Hylant

Toledo, OH • On-site

Full-time

Re-posted 16 days ago


Hylant rating

9.8

Company rating: 9.8 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

2nd of 281 rated insurance


Job description

The Opportunity:

The Lead Data Architect exists to define, own, and evolve Hylant's enterprise application and information architecture. This role ensures that current data initiatives are intentionally designed to meet today's analytical needs while enabling future innovation through scalable, secure, and wellgoverned platforms. The position serves as a technical authority and strategic partner across data engineering, analytics, and business teams. This role will be measured by its contribution to business outcomes such as operational efficiency, adoption of self-service data products, and AI agent effectiveness.

In This Role You Will Execute On:

  • Define and drive the enterprise data architecture vision, ensuring alignment between business objectives, analytics needs, and platform capabilities (long term strategy and innovation roadmaps).

  • Responsible for application architecture (platform, tools, technologies) and information architecture (data models, data flow, data structures and data access)

  • Translate business and analytical requirements into scalable, secure, and feasible data architecture designs that support both nearterm delivery and longterm innovation.

  • Guide requirements gathering, backlog shaping, and solution design to ensure initiatives align with established architectural standards and futurestate roadmaps.

  • Lead the design of analytical data models, including dimensional and star schema designs for curated, businessready data layers.

  • Design and oversee endtoend data pipelines, including sourcetotarget mappings, transformation logic, and serving strategies.

  • Establish architectural patterns and standards for data ingestion, transformation, storage, governance, and analytics consumption.

  • Establish and enforce data governance frameworks, standards, and policies to ensure data quality, security, and compliance.

  • Ensure the integrity, quality, and validation of data across the full lifecycle, from source systems through curated datasets and reporting layers.

  • Partner closely with data engineering and analytics teams to provide architectural guidance throughout delivery while remaining accountable for solution quality. Mentor and guide the lead data engineer.

  • Own the Data Strategy & Lifecycle Management, treating EDW domains as data products with SLAs, ownership, and lifecycle

  • Contribute to AI, Machine Learning, and Analytics Enablement Roadmap.

  • Responsible for overseeing, integrating, and optimizing AI agents in data pipelines and ensuring the right mix of human and AI involvement. Ensure responsible use of AI agents and automation in data integration, transformation, and delivery.

  • Understand and own relationships with external support providers and vendors as needed.

  • Evaluate and recommend platform capabilities and emerging technologies to continuously improve performance, scalability, and usability of the data ecosystem. Ensure solutions and system are scalable, re-usable, and cost optimized.

  • Perform other duties and special projects as requested.

In This Role You'll Need:

  • Prefer bachelor's degree in computer science, data science, engineering, or a related field, or equivalent practical experience.

  • Insurance industry experience, including familiarity with data domains such as policy, claims, billing, underwriting, or risk.

  • Six or more years of experience in data architecture, analytics architecture, or enterprise data platform design.

  • Demonstrated experience designing and delivering data platforms built on Azure based technologies as well as with multi-cloud or hybrid environments.

  • Hands on experience with DevOps (CI/CD process), event streaming and processing, AI/ML toolset, Azure Databricks, Microsoft Data Factory, Delta Lake, Unity Catalog, and Azure cloud services.

  • Strong experience designing analytical data models, including dimensional and star schema approaches.

  • Advanced SQL skills and working proficiency with PySpark for data transformation and modeling.

  • Experience designing and validating end-to-end data pipelines, including data quality and reconciliation processes.

  • Ability to clearly communicate complex technical concepts to both technical and non-technical audiences.

  • Experience managing a team and in establishing an Architecture Review Board, Design Reviews, and Data Standards and Conventions.

  • Must be willing and able to travel for in-person meetings at least on a quarterly basis

  • Ability and willingness to travel by car or airplane for meetings, conferences, or other business-related functions.

  • Must be legally authorized to work in the United States


Why Hylant?

A multi-year recipient of Best Places to Work in Insurance, Hylant is a full-service insurance brokerage with over 20 offices in eight states. And since the founding of our family-owned business over 90 years ago, we made a promise to strengthen and protect the businesses, employees and communities of our client family by embracing them as our own. We're more than an insurance brokerage firm and you're more than a client, employee or neighbor. You're family. And that's just the way we treat you.

Hylant is proud to be an equal opportunity workplace. All qualified applicants will receive consideration for employment without regard to race, marital status, sex, age, color, religion, national origin, Veteran status, disability or any other characteristic protected by law. If you have a disability or special need that requires accommodation, please let us know. Hylant participates in E-Verify.


What Hylant employees say

Hours and flexibility

Workplace

Get the full story on Breakroom