Data Engineering Internship - Summer 2027
Data Engineering intern at Auto-Owners Insurance analyzing data processes, designing data quality and governance solutions, building and maintaining data transformation code, and handling regulatory data requests — fully in person in Lansing, MI.
- Compensation
- Not disclosed
- Location
- Lansing, MI
- Work setup
- Not specified
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Pursuing a bachelor's in statistics, computer science, data mining, machine learning, mathematics, physics, operations research, or related quantitative discipline with at least two years of coursewor
- Master's students considered if graduation is August 2027 or later
- Ability to apply mathematical, statistical, and programming concepts to analyze and solve complex insurance processes
- Strong analytical reasoning, problem solving, technical writing, collaboration, and presentation skills
- High level of initiative, motivation, organization, and leadership
- Ability to lead projects and coordinate among diverse stakeholders
- Ability to mentor by sharing knowledge and challenging others to grow their skill sets
- Ability to work effectively with others at all levels
Nice to have
- Successful completion of actuarial exams or other Company approved courses
Full description
A career at Auto-Owners is challenging and rewarding. Our group of caring associates create financial security by helping individuals and businesses make a new start when a loss occurs.
Job Description
Auto-Owners Insurance, a top-rated insurance carrier, is seeking a motivated Data Engineering intern to join our team this summer. The position analyzes data processes and the needs of users and stakeholders; develops project plans; and designs, implements, and manages data quality, data reporting, and governance solutions. The position requires the person to:
- Work with project stakeholders to identify business requirements and expected outcomes.
- Create, evaluate, and summarize data assets in a form acceptable for use within the Company or outside the Company, including use by government bureaus, agencies, and others.
- Evaluate and complete regulatory data requests in an accurate and timely manner.
- Understand data sources and the data lineage of various computer systems in the Company.
- Build, maintain, and document code used to transform, aggregate, and analyze data.
- Create and enhance data extracts and analyses to support data reporting functions and inquiries.
- Evaluate the reliability of sources of data, investigate data integrity, identify data problems, and provide technical expertise for ensuring data quality, while promoting understanding within the Division.
- Assist with training and mentoring of other associates.
- Contribute to and promote the peer review process, verify work completed by others, and develop other quality control measures
This internship has full-time hours (Monday through Friday) and is in person in Lansing, Michigan. Interns choose their start and end dates. Most join in mid-May and stay until mid-August.
Desired Skills & Experience
- Student must be working toward a bachelor’s in statistics, computer science, data mining, machine learning, mathematics, physics, operations research, or a related quantitative discipline, and have at least two years of coursework completed. Master’s students are also considered if graduation is August 2027 or later.
- Successful completion of actuarial exams or other Company approved courses is preferred. These requirements may be waived or changed, at Company discretion, based on prior education and/or experience.
- Ability to apply mathematical, statistical, and programming concepts and procedures to analyze and solve complex insurance processes.
- Demonstrate strong analytical reasoning, problem solving, technical writing, and collaboration and presentation skills.
- Exhibit high level of initiative, motivation, organization, and leadership.
- Ability to lead projects and coordinate among diverse stakeholders.