Data Engineer

Job description

What to Expect Tesla’s Electronics Supplier Industrialization team needs a hands-on Engineer who can build at scale and maintain the supplier intelligence infrastructure that keeps our suppliers, factories, and field fleet running at optimal efficiency and quality. You will work closely with suppliers, contract manufacturers, and project leadership to design, deploy, and maintain supplier intelligence architecture that enables real-time production performance monitoring—including Quality, Capacity, and Equipment performance. You will collaborate to define specs and scopes for expansions into Agentic AI and Computer Vision, while proactively discovering and provisioning internal resources like AI tools, elastic cloud servers, and platform teams to supercharge system improvements and feature development. Your work will deliver high-quality reporting, real-time operational insights, and advanced analytics that directly boost manufacturing efficiency and product quality. Expect a fast-paced, highly collaborative environment where your contributions accelerate Tesla’s mission to transition the world to sustainable energy. What You’ll Do Design and maintain SIE database infrastructure to support large-scale manufacturing data storage and analytics workloads, handling both numerical data and images Set up and scale manufacturing data pipelines between Tesla and suppliers by pulling supplier data into the SIE database Review and approve data schemas from suppliers to ensure standardization and quality Develop hardware and software for edge computing installed in assembly equipment from automation suppliers Build and maintain workflow orchestration pipelines using Airflow or similar tools Implement data quality validation, monitoring, and alerting frameworks to ensure analytics reliability Collaborate closely with project leadership to define and refine scope for Agentic AI (e.g., autonomous agents) and Computer Vision (e.g., real-time detection) expansions, translating vision into actionable milestones, requirements, and integration paths Proactively identify, secure, and integrate internal resources—such as AI tools, cloud servers, and their maintaining platform teams—to support defined scopes and drive system evolution What You’ll Bring Degree in Computer Science, Data Engineering, Industrial Engineering (Data specialization), related quantitative discipline, or equivalent experience 1-5 years of industry experience Proficiency in SQL and Python stack (e.g., FastAPI, pandas, Jupyter, matplotlib, NumPy, SciPy) Experience building data pipelines, REST/gRPC APIs, database fundamentals, CI/CD, and Kafka (preferred) Experience with Git or other source control Strong communication skills to translate engineering needs into data/software requirements Experience with manufacturing and quality tools is a plus Familiarity with Agentic AI, Computer Vision, or cloud infrastructure (e.g., AWS/GCP/Azure) is a strong plus Extracted Data Skills: Python, AWS, Azure, GCP, SQL, Kafka, CI/CD, Git, Computer Vision Experience: 5+ years