SupercellData Engineer, Analytics

The Role： We are looking for a Data Engineer to be part of our Data Analytics team. This person will design, develop, maintain and support our Enterprise Data Warehouse & Manufacturing and Supply Chain Intelligent Solution within Tesla using various data & AI/BI tools, this position offers unique opportunity to make significant impact to the entire organization in developing data tools and applying AI into the process of manufacturing and supply chain lifecycle. Responsibilities: - Work in a time constrained environment to analyze, design, develop and deliver Enterprise Data Warehouse solutions for Supply Chain/Enterprise Teams. - Initiate or generalize BI solution across the system used by factories in different region globally - Factorize and translate business pain point into executable IT solutions - Setting up, maintaining and optimizing bigdata platform for production usage in reporting, analysis applications. - Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation. - Create ETL pipelines using Spark/Flink. - Create real time data streaming and processing using technologies like Kafka , Spark etc. - Develop collaborative relationships with key business sponsors and IT resources for the efficient resolution of work requests. - Provide timely and accurate estimates for newly proposed functionality enhancements, especially in critical situations. - Develop, enforce, and recommend enhancements to Applications in the area of standards, methodologies, compliance, and quality assurance practices; participate in design and code walkthroughs. Minimum

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Description Data Engineer, Cell Quality & Field Reliability The Team The Cell Quality team is a small team within the R&D organization responsible for incoming, production and field reliability.  The group projects focus on mass production of Li-ion cells for all programs, including Model S, X, 3, Y and Energy Products. The Role The position is to support daily data analytics activities and process improvements suggestions based on fleet data and known field returns for all cell programs with more focus on Kato cell production build issues. Candidate is expected to lead or support quality focused multidisciplinary cell-related projects, typically involving R&D, Design or Production organization. The engineer must be extremely organized, detail orientated, with strong ability to prioritize and multitask, successfully collaborate on projects with a range of business objectives. This person must exhibit the knowledge, leadership, and drive needed to not only challenge the status quo, but also define and execute the optimal path forward. Responsibilities Perform extensive data study on all field failures and correlation studies with upstream cell manufacturing process. Create test models to ensure proper detection and outlier rejection criteria for field rejects, especially from Kato production. Identify trends from field return data and quantify reliability risks for diagnostics of key critical signals/metrics. Create data visualizations to communicate analysis results with cross-functional teams and drive decision making of key failure modes on field. Analyze Field Reliability/Quality data for cell related failures and failure modes to predict expected failure rates, affected populations, verify effectiveness of the corrective actions at Tesla and at suppliers. Provide data integration and setup quality systems for new in-house manufacturing lines and mass production cell models. Produce cogent and intelligible data visualizations, author technical presentations and summarize high-impact technical findings with strong data analysis package.

• Design and implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, and storage, supporting both real-time and offline analytics needs. • Develop automated data monitoring tools and interactive dashboards to enhance business teams’ insights into core metrics (e.g., user behavior, AI model performance). • Collaborate with cross-functional teams (e.g., Product, Operations, Tech) to align data logic, integrate multi-source data (e.g., user behavior, transaction logs, AI outputs), and build a unified data layer. • Establish data standardization and governance policies to ensure consistency, accuracy, and compliance. • Provide structured data inputs for AI model training and inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows. • Explore innovative AI-data integration use cases (e.g., embedding AI-generated insights into BI tools). • Provide technical guidance and best practice on data architecture and BI solution