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

• →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.

• 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.