Posting Title

Post-Undergraduate/Graduate (Year-Round) Data Engineering Intern

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Location

CO - Golden

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Position Type

Intern (Fixed Term)

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Hours Per Week

40

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Working at NREL

NREL is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.

Join NREL, where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NREL stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.

At NREL, you’ll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.

Job Description

NREL’s Grid Planning and Analysis  team is seeking a curious, sharp, and technically skilled Data Engineering Intern to help build the largest grid datasets in the world. We are looking for a candidate passionate about building trusted, validated databases from the ground up using messy, heterogeneous datasets that fuel real-world energy research and national policy decisions.

You'll be instrumental in transforming unstructured or semi-structured data into high-integrity, queryable databases that support machine learning models, visual analytics, and advanced simulations. 

Responsibilities

• Ingest, clean, and validate diverse datasets from internal and external sources (e.g., CSVs,  spreadsheets, Text Files and SQL dumps)
• Design and assemble structured, high-quality relational databases
• Collaborate with domain scientists and software engineers to understand data contexts and ensure correct integration into the Sienna Platform
• Document data provenance, validation logic, and database design decisions
• Write reusable scripts for data transformation and validation (preferably in Python or similar languages)
• Contribute to internal Git repositories and maintain clean, reproducible codebases

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Basic Qualifications

Must be enrolled as a full-time student in a Bachelor's, Master's or PhD degree program, or graduated in the past 12 months from an accredited institution. Candidates who have earned a degree may work for a period not to exceed 12 months. Must have a minimum of a 3.0 cumulative grade point average.

Please Note:
•You will need to upload official or unofficial school transcripts as part of the application process.
•If selected for position, a letter of recommendation will be required as part of the hiring process.

* Must meet educational requirements prior to employment start date.

Additional Required Qualifications

• Completed a Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, Data Science, Software Engineering, or a related field, or currently enrolled in a masters or PhD program in these fields
• Must provide a GitHub profile or portfolio showcasing relevant projects as part of application, please include a link in your resume/CV
• Strong programming skills in Julia and python with experience with Pandas, NumPy, or similar libraries a plus
• Demonstrated experience working with datasets and databases
• Ability to communicate technical details clearly and collaborate in a team-oriented environment
• Data requests through APIs

Preferred Qualifications

• Proficiency in SQL and experience with relational databases (PostgreSQL, MySQL, SQLite, etc.)

• Machine learning experience

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Job Application Submission Window

The anticipated closing window for application submission is up to 30 days and may be extended as needed.

Annual Salary Range (based on full-time 40 hours per week)

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