Data Engineer, Paintshop

Data Engineer, Paintshop

Tesla

Grünheide (Mark), Germany

What to Expect

Tesla is revolutionizing the automotive industry with sustainable energy and advanced manufacturing. As a Data Engineer in the Paint Shop, you will help harness production data to optimize paint processes, support quality assurance, and improve operational efficiency. Working alongside senior engineers, you will build and maintain the data pipelines that integrate real-time data from paint lines, inspection systems, and automation tools, and turn it into datasets and dashboards the team relies on. Collaborating with cross-functional teams (Production Engineering, Controls, and Quality), you will help enable data-driven decisions that support Tesla's mission of accelerating the world's transition to sustainable energy through high-volume, defect-free vehicle production. This role suits an engineer with a solid technical foundation who wants to grow their impact in a fast-paced manufacturing environment.

What You’ll Do

• Data Pipeline Development: Build, maintain, and optimize ETL (Extract, Transform, Load) pipelines that process data from Paint Shop systems, including sensor data from coating robots, inspection cameras, and cycle time trackers. Contribute to pipeline design and keep them robust and integrated with Tesla's data platforms (e.g., cloud systems like AWS, Azure, or internal tools).
• Data Integration and Management: Integrate data sources from manufacturing equipment (e.g., automated lines, EHS sensors, ERP systems) into unified datasets for analysis. Help maintain data lakes, warehouses, and streaming pipelines that support monitoring of paint quality, defect rates, and throughput. Deliver dashboard requests (e.g., for production metrics or defect tracking) and perform data validation to ensure accuracy, completeness, and reliability.
• Performance and Quality: Monitor and improve existing data workflows to reduce latency and improve accuracy. Apply data governance and validation practices under the guidance of senior engineers to keep data compliant with EHS standards and manufacturing requirements.
• Collaboration and Support: Work with Paint Shop teams (supervisors, engineers, and analysts) to understand their needs and translate them into data solutions. Support data-related projects such as dashboards for cycle time tracking or automated reporting for production metrics.
• Problem-Solving and Growth: Contribute to initiatives that apply advanced technologies, such as machine learning for anomaly detection in paint defects or IoT data feeds. Help maintain and extend the iSVS (Internal Surface Vision System) for defect classification. Surface opportunities for process improvements, such as reducing paint material waste through data insights, and grow your technical depth across the stack.
• Documentation: Keep clear documentation of the pipelines and processes you own. Follow Tesla's safety protocols, data privacy standards (e.g., GDPR), and sustainability goals.

What You’ll Bring

• Education: Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field (or equivalent practical experience).
• Experience: 2+ years of experience in data engineering or a closely related role. Exposure to manufacturing, automotive, or industrial data is a plus.
• Proficiency in Python (Java or Scala a plus).
• Working experience with data pipelines and at least one big data or streaming tool (e.g., Apache Spark, Kafka) or a willingness to learn.
• Solid SQL skills (e.g., PostgreSQL, MySQL); exposure to NoSQL and orchestration tools (e.g., Airflow) is a plus.
• Familiarity with API integrations and version control (Git).
• Soft Skills: Good problem-solving ability, clear communication with technical and non-technical colleagues, and the ability to work well in a dynamic, high-pressure environment.
• Other: Willingness to work in a manufacturing setting with occasional shift flexibility. Must pass Tesla's background check and safety training.

Preferred Qualifications

• Exposure to MES systems, IoT platforms, or data visualization tools (Tableau/Power BI).
• Basic understanding of manufacturing processes, such as paint application, quality inspection, or EHS compliance.
• Interest in machine learning or AI for predictive analytics in production settings, including systems like iSVS.