Athletic BI Operations & Insights Analyst

University of Minnesota; Saint Paul (MN); On-site; USD 70,000 - 85,000; Full time

Job summary

The University of Minnesota Athletics Department is seeking a Business Intelligence Operations Analyst for roster management. You will manage data, build models, develop dashboards, and support forecasting and valuation to inform roster construction and financial planning.

You will collaborate with BI colleagues, translate complex data into actionable insights, and ensure transparent, bias-aware outputs while supporting revenue-generation initiatives and other data priorities across the

Qualifications

• Bachelor's degree in a quantitative field plus at least two years of experience, or a master's degree.
• Experience with databases, data management, reporting tools, or analytical platforms.
• Proficiency with SQL, Excel, Python, R, Tableau, Power BI, or similar BI tools.

Responsibilities

• Build and maintain authoritative data sources and BI infrastructure for roster analytics.
• Create and improve dashboards and recurring reports for coaches, administrators, finance, and leadership.
• Translate datasets into clear reports and visualizations to support decision-making.
• Develop and validate models for roster needs, performance, and financial planning.
• Collaborate with BI colleagues to support cross-functional priorities and governance.

Skills

Data governance awareness

Data storytelling

Ability to manage multiple projects

Strong written and verbal comms

Discretion and professionalism

Education

Bachelor's degree in quantitative field or related

Master's degree in related field

Tools

SQL

Excel

Python

R

Tableau

Power BI

Job description

The University of Minnesota Athletics Department is seeking a Business Intelligence Operations Analyst for roster management. You will manage data, build models, develop dashboards, and support forecasting and valuation to inform roster construction and financial planning.

You will collaborate with BI colleagues, translate complex data into actionable insights, and ensure transparent, bias-aware outputs while supporting revenue-generation initiatives and other data priorities across the