Fleet Analytics Intern – Data Engineering

Job Description

Internship, Data Engineer, Fleet Analytics | Tesla

The Tone:
This is an internship at Tesla, located in Palo Alto, CA. Tesla relies on large-scale data from vehicles, Superchargers, and energy products to improve hardware designs, proactively detect issues, optimize operations, and make products better and customers safer. This role is crucial for transforming complex data into actionable decisions, directly impacting Tesla products and enabling hundreds of engineers across various teams. It’s an opportunity for a student excited to work at the intersection of data engineering, software engineering, analytics, and AI-native tooling.

The TL;DR
• Role: Internship
• Type: Full-time (40 hours/week), minimum 12 weeks
• Location: In-person, Palo Alto, CA
• Pay: $40.00 – $50.00/hour
• Team: The Fleet Analytics team supports engineering teams by turning data into decisions.
• Mission: This person will help engineering teams convert ambiguous problem statements into clear analytical or engineering scopes, then build reliable tools or analyses to accelerate informed decision-making.
• Tech Stack: Python, Java, Scala, modern open-source technologies

What You’ll Actually Do
• Problem Scoping: Collaborate with stakeholders to transform vague problem statements into clear analytical or engineering scopes, using the results to drive informed decisions.
• Data Analysis: Conduct reproducible data analyses over petabytes of fleet-scale data using modern open-source technologies, clearly communicating assumptions, methodology, limitations, and results to technical audiences.
• Pipeline Development: Build data pipelines that promote high-value ad-hoc analyses into production datasets, dashboards, or applications that engineers can rely on.
• Tool & Metric Design: Design and implement metrics, applications, and tools that help engineers and AI agents self-serve data insights.
• Workflow Improvement: Improve internal workflows for data discovery, analysis, debugging, onboarding, and operational support, and partner with engineers to drive adoption of the applications, datasets, and tools you build.

The Must-Haves
• Background: Pursuing a degree in Computer Science, Data Science, Electrical Engineering, Mechanical Engineering, Statistics, or a related technical field, with an expected graduation between December 2026 and December 2027.
• Experience: Experience working with data through coursework, research, internships, or personal projects.
• Skills: Strong programming ability in Python, Java, Scala, or another general-purpose language; ability to reason about technical systems and learn domain concepts quickly, including vehicle, electrical, or energy systems; strong communication skills and the ability to explain technical work clearly.
• Bonus: Curiosity, ownership, and comfort working through ambiguity.