Process Engineering Intern - Data-Driven Improvements

Heidelberg Materials; Harleyville (SC); On-site; USD 32,000 - 37,000; Part time

Benefits offered by this job

Summer PTO Holidays

Intern In-Person Events

Employee Resource Group Activities

Job summary

Heidelberg Materials North America is offering an in-plant internship at the Giant Resource Recovery (GRR) facility in South Carolina. You will work hands-on with engineering and operations teams to analyze data and drive process improvements that increase efficiency and reliability.

The role emphasizes data interpretation, collaboration across disciplines, and practical problem solving in a fast-paced industrial environment. Safety adherence and active learning are required for success.

Qualifications

• Pursuing a bachelor’s degree in engineering.
• Ability to analyze data and apply engineering principles.
• Strong communication and collaboration across teams.
• Proficiency with Microsoft Office; experience with data analysis tools is a plus.
• Safety-focused and eager to learn in an industrial setting.

Responsibilities

• Support process improvement initiatives to boost plant efficiency, reliability, and performance.
• Collect, analyze, and interpret process data to identify trends.
• Collaborate with operations, maintenance, and engineering teams on production optimization.
• Assist with troubleshooting process issues and developing data-driven solutions.
• Prepare reports, presentations, and recommendations to communicate findings.

Skills

Data analysis

Process improvement

Team collaboration

Communication

Microsoft Office

Education

Bachelor’s degree in Chemical Engineering, Mechanical Engineering, Industrial Engineering

Tools

Microsoft Office

Data analysis tools

Job description

Heidelberg Materials North America is offering an in-plant internship at the Giant Resource Recovery (GRR) facility in South Carolina. You will work hands-on with engineering and operations teams to analyze data and drive process improvements that increase efficiency and reliability.

The role emphasizes data interpretation, collaboration across disciplines, and practical problem solving in a fast-paced industrial environment. Safety adherence and active learning are required for success.