Supply Chain Systems & Data Engineer (m/w/d) - Gigafactory Berlin-Brandenburg

The Operations and Systems Engineering team in Supply Chain develops processes and systems for reliable, efficient flow of direct materials to Tesla's factories, distribution centers, service locations, and installation sites. As a Supply Chain Systems & Data Engineer, you will partner with planning, logistics, warehousing, and other stakeholders worldwide to deliver operational improvements, systems capabilities, and data insights that support growing production volume and fleet size.

This is the team's first dedicated data-heavy role. You will own the data — what matters, why, how to source it, and how to keep it true over time. The interactive data reports, data apps, AI agents, and pipelines you deliver are the visible outputs; the underlying work is metric definition, lineage, snapshot history, and partnering with Tesla's IT teams to extend the data lake where required attributes don't yet exist.

You will be a thought leader in designing solutions and own execution end-to-end. The role requires deep subject matter knowledge across supply chain processes, systems, and KPIs; the ability to think strategically while staying comfortable in the details; and the capacity to manage multiple highly visible projects at global scale. It suits a self-motivated, driven individual who thrives in a fast-paced environment.

Based at Giga Berlin and anchored on EMEA Service Material Planning day-to-day, with reach across the team's broader EMEA and global scope alongside North America counterparts.

• Work directly with planning, purchasing NPI, and product teams to identify the metrics, reports, and operational questions that are missing or broken
• Establish trusted metric definitions in partnership with Operations and own their long-term reproducibility — methodology, lineage documentation, and historical logging that preserves trend integrity. This includes deciding when to capture point-in-time snapshots versus recompute metrics from raw history, how to handle data that arrives late, and how to treat retroactive corrections to past records.
• Develop the interactive data report, data app, AI agent, or API that delivers the data and insights to end users.
• Continuously improve existing operational reports across shortage management, master data, inventory health, and similar workflows, and monitor the infrastructure that hosts your tools for performance and reliability.
• Define and track KPIs that measure adoption, performance, and operational value of what you build.
• Drive solutions across short- and long-term horizons, partnering with IT software engineering to productionize proven prototypes.
• Own end-to-end data availability for your work — partner with Tesla's IT teams to extend the data lake, source missing attributes, and stand up new feeds where required data does not yet exist in a usable form.
• Build and maintain the SQL, Python, and Apache Airflow data pipelines that feed your tools, including ETLs deployed on VMs or via CI/CD, along with schema design, indexing, and performance work.
• Own the team's databases and serve as the first-line resource for schema, access, query, and performance questions. Escalate platform-level issues to central infrastructure teams.
• Triage the inbound pipeline of tool, report, and data requests — quantify the expected operational impact of each, and prioritize what gets built next.
• Deliver regular updates on roadmap progress, prototype portfolio, and data and infrastructure health to leadership and stakeholders.

• Bachelor's or master's degree in engineering, computer science, data science, or a related field, or equivalent experience or evidence of exceptional ability.
• Minimum 3-5 years of experience as a data engineer, analytics engineer, AI engineer, product/systems engineer, or in a similar hands-on building role; data-heavy supply chain or operations backgrounds are equally valued.
• Fluency with SQL, including complex joins, window functions, query optimization, and performance tuning.
• Daily fluency with Python, with the ability to build data tools and interactive data apps end-to-end using frameworks such as Streamlit or Dash.
• Experience with data modeling for measurement reproducibility, including dimensional modeling, slowly-changing dimensions, snapshot patterns, and audit and lineage columns.
• Working knowledge of NoSQL and Graph (GQL) query paradigms, with hands-on experience in MongoDB and a graph database.
• Familiarity with data lake and lakehouse environments, with the ability to partner with platform teams to extend coverage, source new attributes, and consume from lake-stored data.
• Experience with pipeline orchestration tools such as Apache Airflow, and comfort standing up ETLs on VMs or via CI/CD.
• Experience building operational dashboards and KPI surfaces in Grafana, Tableau, or similar tools.
• Familiarity with DevOps tooling, including Git and GitHub, CI/CD pipelines, Kubernetes basics, and API tooling such as Postman and Swagger.
• Fluency with AI-augmented development tools such as Cursor, Claude Code, or similar coding agents.
• Hands-on experience building AI agents, agentic workflows, or AI-integrated data apps.
• Strong stakeholder collaboration and communication, with the ability to work directly with end users and translate business questions into trustworthy data and working tools.
• Structured, pragmatic problem-solving and a track record of working effectively across cross-functional teams.
• Demonstrated ability to manage multiple concurrent projects at organizational scale, balancing strategic perspective with attention to detail.
• Strong prioritization judgment and ability to quantify the impact of competing requests before committing engineering effort.
• Experience in a supply chain, logistics, manufacturing, or service operations environment
• Exposure to SAP, MES, WMS, or other enterprise supply chain systems
• Experience integrating external APIs at scale (Google Maps, carrier APIs, ERP APIs)
• Prior role bridging Operations and Engineering (analytics engineer, solutions engineer, internal tools engineer)
• Candidates are expected to uphold and actively promote sustainability principles in their daily work, operating in line with Tesla Global Environmental, Health, Safety & Security (EHS&S) Policy and EMAS requirements, fostering a culture of continuous environmental improvement.