AI & Data Analytics Internship, Technical Service (m/f/d) - Gigafactory Berlin-Brandenburg

What To Expect
Location: Gigafactory Berlin-Brandenburg

Duration: 6 months

Start date: September / October 2026

Time Type: Full-time

The Technical Service team is dedicated to maximizing the performance and availability of utilities and infrastructure systems, ensuring uninterrupted operations at Gigafactory Berlin. This dynamic team works tirelessly to maintain, enhance, and restore these systems through a combination of preventative and corrective maintenance strategies.

We are looking for a highly motivated and detail-oriented Data Analyst Intern to join the Maintenance Planning team. This role sits at the intersection of factory operations and data analytics: you will turn maintenance, asset, and operations data into reports, dashboards, and automation that planners, engineers, and leadership use to make decisions.

You will support strategic initiatives by preparing analytical outputs, tracking KPIs, streamlining reporting processes, and building digital tools that reduce manual effort. Working alongside analysts and maintenance professionals, you will translate business needs from Technical Service teams into functional data solutions: combining hands-on factory analytics with Python, SQL, and modern automation workflows.

What You'll Do
Reporting & analytics

Support planning, tracking, and coordination of analytics initiatives across Technical Service and Intralogistics

Prepare dashboards, status reports, presentations, and documentation for stakeholders

Write and maintain SQL queries; perform data analysis and prepare decision-support briefs

Monitor reporting timelines, data quality, and deliverables

Build and refresh Excel exports for spare parts, preventive maintenance (PM) coverage, consumption, forecasts, and assets

Maintenance operations data

Track KPIs for maintenance orders, PM compliance, missed work orders, and spare parts consumption

Support asset analysis, checklist compliance, and inventory planning

Handle ad-hoc data requests from planners and cross-functional partners

Automation & digitalization

Translate business needs into automation solutions and internal analytics tools

Develop Python/SQL workflows, data pipelines, and automated reporting using Git

Support development of AI solutions such as RAG agents, automated workflows, and reusable skills for maintenance teams

Identify automation and AI opportunities; support adoption through documentation and knowledge-sharing

Collaboration & process

Attend meetings, capture action items, and coordinate with internal teams

Improve analytics processes and reporting templates for efficiency and clarity

What You'll Bring

Currently enrolled in a Bachelor's or Master's degree in Data Science, Business Informatics, Industrial Engineering, Technology & Management, Operations, or a related field

Strong experience with SQL for data extraction, joins, aggregations, and reporting

Proficiency in MS Office (especially Excel and PowerPoint) for structured exports, pivot tables, and stakeholder presentations

Strong analytical and problem-solving skills with a business-first mindset

Excellent written and verbal communication in English; German is a plus

Strong organizational skills with exceptional attention to detail

Proactive, reliable, and comfortable managing multiple tasks simultaneously

Self-starter who learns quickly in a fast-paced environment

A team player with a flexible mindset and strong sense of ownership

Preferred

Experience with Python for data analysis, automation, and workflow integration

Familiarity with Git and version control

Understanding of AI/ML concepts, prompt engineering, or workflow design

Familiarity with BI and database tools (e.g., DBeaver, Power BI, or similar)

Demonstrated interest in building digital tools, automations, and data solutions

Familiarity with maintenance, manufacturing, or facilities operations concepts

Hands-on experience with at least one data or automation project used by real users — a portfolio or GitHub repository is highly recommended

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