Data Consultant Co-Op (2027 Spring Semester) / University of Cincinnati

Coretek Services · posted Oct 23, 2026

Open to candidates in United States

Temporary · Intern

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What this job asks for

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Must have

  • •Pursuing degree in CS, Data Science, IS, or related
  • •Foundational SQL skills
  • •Basic Python programming experience
  • •Familiarity with data movement and pipelines
  • •Attention to data accuracy and investigation
  • •Clear written and verbal communication

Nice to have

  • •Exposure to Git, APIs, Azure, Fabric, or Power BI
  • •Exposure to Elastic or Tines
  • •Experience using AI coding tools

Tools and technologies

SQL, Python, Azure, Microsoft Fabric, Power BI, Elastic, Tines, Git, APIs

Read the full posting

Coretek provides AI, cloud, security, and managed services. We are seeking a Data Consultant Co-Op to help build the pipelines and datasets that support reporting, business operations, and AI solutions.

Coretek’s AI & Data Co-Op Program places you on a team supporting internal initiatives and client projects. With guidance from experienced Consultants, you will connect data sources, prepare datasets, and test your work. You will own defined tasks and incorporate feedback before changes go through review and approval.

You will gain practical experience with SQL, Python, and Microsoft data platforms such as Azure and Microsoft Fabric. Depending on project needs, you may also work with Power BI, Elastic, and Tines.

Responsibilities

  • Work with Consultants, project managers, and business users to understand data sources, reporting needs, and acceptance criteria for assigned tasks.
  • Build assigned data pipelines to extract, transform, and load data from databases, files, and APIs, following established designs and standards.
  • Write SQL queries and Python scripts to clean and combine data. Prepare tables and datasets for reporting, analytics, and AI applications.
  • Check for missing values, duplicates, and invalid formats. Reconcile outputs to source records and investigate data discrepancies with the team.
  • Monitor scheduled data jobs, help troubleshoot failures, and communicate delivery impacts early. Test corrections before requesting review or release.
  • Document source-to-target mappings, transformation rules, and operating steps. Use version control and participate in code reviews so others can support the work.
  • Follow approved data access and handling practices. Protect client information and obtain review before changing shared datasets or production pipelines.
  • Keep project tasks and time entries current, raise blockers promptly, and help measure improvements in data accuracy, refresh times, or manual effort.

Requirements

Requirements

  • Currently pursuing a degree in Computer Science, Data Science, Information Systems, Computer Consultanting, or a related field, with eligibility to participate in a university co-op program.
  • Foundational SQL skills, including filtering, joins, grouping, and aggregations, with an understanding of relational tables, keys, and basic database concepts.
  • Basic Python programming experience through coursework, personal projects, or previous work, including reading files, manipulating data, and troubleshooting code.
  • Familiarity with how data moves between systems and an interest in data pipelines, transformation, and storage. Experience working with CSV or JSON data is helpful.
  • Careful attention to data accuracy and the ability to investigate unexpected results, explain findings, and ask for help when requirements or source data are unclear.
  • Clear written and verbal communication, a willingness to act on feedback, and the ability to complete agreed tasks while keeping teammates informed of progress and blockers.
  • Exposure to Git, APIs, Microsoft Azure, Microsoft Fabric, or Power BI is a plus. Coursework and personal projects count as relevant experience.
  • Exposure to Elastic for search and data analysis or Tines for workflow automation is a plus. Project assignments will determine which tools you use.
  • Experience using AI coding tools is helpful, along with a willingness to test generated code, understand its behavior, and follow company rules for data and tool use.

Successful co-ops produce data that others can trust and use. They check results against the source, follow through on commitments, and leave clear documentation. Progress is reflected in reliable data jobs, fewer recurring errors, and work the team can maintain after the co-op ends.

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