Quality Data Co-op: Automotive Analytics & Dashboards

BMW Group; Spartanburg (SC); On-site; USD 28,000 - 34,000; Full time

Job summary

BMW Manufacturing Company is inviting a Quality Data Co-op for Spring 2027 to work on data analytics, dashboards, and quality processes in Plant 10. The role emphasizes problem solving, KPI impact analysis, and development of tools to visualize and improve vehicle flow and launch readiness.

You will explore AI applications, collaborate with management on process enhancements, and contribute to the ongoing modernization of data tools while gaining hands-on experience in a high‑stakes

Qualifications

• Engineering degree in a related field with strong analytical skills.
• Proficient in Microsoft Excel (VLOOKUP, Macros, PivotTables preferred).
• Experience with Python/SQL for data manipulation and analysis.
• Familiar with data visualization tools such as Tableau or Power BI.
• Enrolled student status and ability to work on-site 40 hours/week.
• Must attach unofficial transcript and be enrolled for all rotations.

Responsibilities

• Support KPI analytics and problem solving to improve quality.
• Develop dashboards and tools to visualize plant data and processes.
• Create and test tools to improve vehicle flow and launch readiness.
• Develop AI applications for test, finish, and vehicle launch areas.
• Investigate innovations in vehicle information flow and routing.
• Update the co-op manual and onboarding information.

Skills

Analytical thinking

Problem solving

Excel proficiency

Python/SQL basics

Education

Engineering degree

Tools

Excel

Python

SQL

Tableau/Power BI

Job description

BMW Manufacturing Company is inviting a Quality Data Co-op for Spring 2027 to work on data analytics, dashboards, and quality processes in Plant 10. The role emphasizes problem solving, KPI impact analysis, and development of tools to visualize and improve vehicle flow and launch readiness.

You will explore AI applications, collaborate with management on process enhancements, and contribute to the ongoing modernization of data tools while gaining hands-on experience in a high‑stakes