Data Engineering Intern (Python, SQL, ETL)

Remote · Part-time (15-20 hrs/week) · San Francisco Bay Area, CA · Unpaid / Academic CreditWhat You're Walking Into Kelvolta is an energy technology company focused on uncovering and capturing real, measurable cost savings for energy-intensive operations at large commercial and industrial sites. Our work spans cold storage, AI data centers, industrial manufacturing, EV charging, and other markets in which demand is growing fast and the economics of storage turn on behavior nobody has measured. The data behind all of it arrives messy, inconsistent, and rarely documented. Turning it into something analysts can rely on is the job. As a lean team, we operate close to the work, giving interns the opportunity to contribute to real, production-adjacent challenges."Learning to build things that actually matter in a startup where I'm constantly learning and adapting."— L. Sanchez, UC Santa Barbara Skills You'll Put to Use and DevelopETL pipelines that ingest, clean, and structure real-world energy and operational datasets Python-based analysis (pandas, numpy, matplotlib) on messy, real-world datasetsSQL queries against production databases to power analytics workflows Judgment about data quality: deciding what a record is worth, what is safe to infer, and what has to be marked unknown Who We're Looking ForCurrently-enrolled (not graduated) students. Upper-division undergraduate or graduate students in Computer Science, Data Science, Statistics, or Engineering with hands-on experience in at least two of the following: Python, SQL, ETL/data pipelines. Past interns have come in with strong fundamentals and left with experience building production-level work. We care more about what you can do than what year you're in. What You GetHands-on experience working on real projects with meaningful impact Opportunities to take ownership and contribute to production-level work Direct mentorship from a CEO with 25 years of energy project development experience Exposure to real datasets and practical problem-solving A fast-paced environment that helps you grow quickly A strong portfolio of work you can speak to in interviews Eligibility for academic credit (8–10 units, subject to university approval) Priority consideration for extended or paid opportunities based on performance This is an unpaid internship. All participants are eligible for academic credit, and strong performers are considered for paid roles. Logistics Commitment: 15-20 hours per week

DataAnalysis