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Associate Data Engineering Jobs in Ohio (NOW HIRING)

In data engineering at PwC, you will focus on designing and building data infrastructure and ... Associate] is a plus - Designing and implementing thorough data architecture strategies ...

Data Engineer - Senior Manager

Toledo, OH · On-site

$124K - $280K/yr

In data engineering at PwC, you will focus on designing and building data infrastructure and ... Associate] is a plus - Designing and implementing thorough data architecture strategies ...

In data engineering at PwC, you will focus on designing and building data infrastructure and ... Associate] is a plus - Designing and implementing thorough data architecture strategies ...

In data engineering at PwC, you will focus on designing and building data infrastructure and ... Associate] is a plus - Designing and implementing thorough data architecture strategies ...

Oversee the full model lifecycle: data exploration, feature engineering, model development ... With headquarters in Reading, PA, Penske and its associates are driven by a dedication to ...

Oversee the full model lifecycle: data exploration, feature engineering, model development ... With headquarters in Reading, PA, Penske and its associates are driven by a dedication to ...

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Associate Data Engineering information

What is an associate data engineer?

An Associate Data Engineer is an entry-level professional who assists in designing, building, and maintaining data pipelines and infrastructure. They typically work with senior data engineers to ensure data is collected, stored, and processed efficiently for analytics and business use. Responsibilities often include data cleaning, integration, and supporting the development of scalable data solutions. Associate Data Engineers usually have foundational knowledge of programming, databases, and cloud technologies.

What are some typical projects an associate data engineer might work on in their first year?

In their first year, an Associate Data Engineer often works on building and maintaining data pipelines, cleaning and transforming raw data, and supporting the integration of new data sources. They may also assist in optimizing existing data workflows for better performance and reliability, as well as collaborating closely with data analysts and senior engineers to ensure data quality and accessibility. These projects help new team members develop a strong understanding of the organization's data infrastructure and best practices in data engineering.

What are the key skills and qualifications needed to thrive as an associate data engineer, and why are they important?

To thrive as an Associate Data Engineer, a solid understanding of database systems, SQL, data modeling, and a relevant bachelor's degree in computer science or a related field is essential. Familiarity with ETL tools, cloud platforms like AWS or Azure, and programming languages such as Python or Java is typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help set candidates apart in collaborative, data-driven environments. These skills and qualities are crucial for building reliable data pipelines, ensuring data quality, and enabling actionable business insights.

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

AspectAssociate Data EngineeringData Engineer
Required CredentialsBachelor's degree in CS, IT, or related field; some certificationsBachelor's or master's degree; extensive experience preferred
Work EnvironmentEntry-level, team-focused, supporting data pipelinesDesigning, building, and maintaining large-scale data systems
Employer & Industry UsageCommon in tech companies, finance, healthcareUsed across industries for advanced data infrastructure roles
Search & Comparison IntentEntry-level, learning, support rolesAdvanced, specialized data infrastructure roles

The main difference between Associate Data Engineering and Data Engineer lies in experience and responsibilities. Associate Data Engineers are typically entry-level, focusing on supporting data pipelines and gaining hands-on experience. Data Engineers have more experience, handling complex data architecture, optimization, and system design. Both roles require similar educational backgrounds, but Data Engineers usually have more technical expertise and responsibility.

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

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

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

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

Infographic showing various Associate Data Engineering job openings in Ohio as of September 2026, with employment types broken down into 88% Full Time, and 12% Contract. Highlights an 95% In-person, and 5% Hybrid job distribution.

Associate Data/ Metadata Analyst

Dublin, OH • Hybrid

Full-time

Medical, Retirement

Re-posted just now


Key responsibilities

  • Manage the lifecycle of bibliographic and holdings data, including ingestion, normalization, enrichment, and matching.

  • Profile incoming data to identify and resolve structural issues, encoding errors, and tagging discrepancies, and troubleshoot data quality issues.

  • Apply existing scripts and automated workflows to process data, monitor data quality trends using dashboards, and utilize AI-assisted tools for record matching and field mapping.


Job description

Together we make breakthroughs possible.

At OCLC, we build technology with a purpose: to connect libraries and make knowledge accessible worldwide, because we believe that what is known must be shared. Our teams work with complex global datasets, AI and machine learning, hybrid cloud solutions, and other technologies that connect people and organizations to the information they need. We value the power of unique perspectives and experiences to unlock innovation. At OCLC, your ideas matter, whether you have two years of experience or 20. You'll learn, create, and problem-solve with technologists, product developers, librarians, researchers, marketing pros, and support teams around the world.

Why join OCLC?

OCLC is consistently recognized as a best place to work by several independent programs. Werecognize and reward people and results with a comprehensive Total Rewards package. This means competitive compensation that reflects your unique contributions-performance, experience, and skills-along with exceptional benefits, including best-in-class health coverage, retirement plans with generous company contributions, and a commitment to your overall well-being.

  • We know the best ideas don't always happen at a desk. Take a walking meeting around our 100-acre campus or enjoy lunch on the patio. We're committed to your success-both personally and professionally. Hybrid work environment: For many roles, three days a week on-site, with occasional additional days based on business needs.

  • Free use of our on-site tness center, gym sports, group exercise classes, and game room

  • Onsite catering and cafeteria subsidized by OCLC

  • Health and wellness events

  • Work environments with individual and team spaces and the latest technology tools

  • Paid parental leave and adoption assistance

  • Tuition reimbursement and Public Service Loan Forgiveness eligibility

  • Company-subsidized pricing on local tickets and memberships

Join us in transforming how people everywhere access information and be part of a mission-driven team that makes a global impact.

The job details are as follows:The Associate Data Analyst/Metadata Analyst manages the lifecycle of bibliographic and holdings data, ensuring accuracy and integrity through evaluation, transformation, and maintenance. This entry-level role combines metadata expertise with developing technical skills in scripting, automation, and AI-assisted workflows. Working under regular supervision, the Associate will transition from executing well-defined tasks to taking ownership of complex data projects. This is an opportunity to develop expertise at the intersection of library data, modern data engineering, and emerging AI - in a team that invests in the growth of its people.Responsibilities:Data Operations & Quality
  • Data Lifecycle Management:Perform data ingest, normalization, enrichment, and matching according to established standards (e.g., MARC, KBART).
  • Quality Assessment:Profile incoming data to identify and resolve structural issues, encoding errors, and tagging discrepancies.
  • Troubleshooting:Investigate and resolve data quality issues, escalating complex problems to senior staff as needed.
  • Vendor Communication:Coordinate with external data providers and partner institutions regarding routine data specifications and quality issues.
Technical Solutions & Automation
  • Pipeline Support:Apply existing scripts and automated workflows to process data; identify inefficiencies and suggest process improvements.
  • Data Visualization:Use and interpret dashboards (e.g., Power BI) to monitor data quality trends and communicate findings.
  • AI Integration:Utilize AI-assisted tools for record matching and field mapping; contribute structured feedback that helps improve AI model accuracy over time.
Collaboration & Documentation
  • Knowledge Management:Document data source profiles, processing decisions, and technical workflows to ensure team-wide knowledge sharing.
  • Team Support:Execute foundational tasks to support senior analysts and participate in platform improvement projects.
Minimum Required Knowledge, Skills and Experience:
  • Bachelor's degree in computer science, math, statistics, or related field and internship experience with data analysis or creation of library data.
  • Entry-level analytical and communication skills, with attention to data quality while working under regular supervision.
Preferred Knowledge, Skills and Experience:
  • Metadata Standards:Foundational knowledge of MARC, Dublin Core, KBART, or BIBFRAME.
  • Technical Tools:Exposure to scripting languages (Python, SQL, or XSLT) and data transfer protocols (SFTP, APIs, or AWS S3).
  • Data Platforms:Conceptual familiarity with cloud data stores (e.g., Snowflake) and visualization tools (e.g., Power BI, Streamlit).
  • Emerging Tech:Interest in applying AI and linked data concepts (e.g.,Schema.org) to library data challenges.
Working Conditions: Normal office environment.ADA/EAA: The above statements cover what are generally believed to be principal and essential functions of this job. Specific circumstances may allow or require some people assigned to the job to perform a somewhat different combination of duties.