About the RoleWe are looking for a hands-on Senior Data Engineer to help build and evolve Tesla’s next-generation enterprise analytics platform that powers business intelligence, operational intelligence, manufacturing insights, supply chain visibility, service telemetry, energy operations, and more — all while operating under strict SOX compliance and change management controls.You will design, develop, and operate large-scale data infrastructure in a fast-paced, high-impact environment where decisions affect vehicle production, global delivery, battery lifecycle, Supercharger network, Full Self-Driving development, and energy grid optimization.Key ResponsibilitiesArchitect, build, and maintain state-of-the-art Enterprise Data Warehouse / Lakehouse solutions that serve both batch and near-real-time analytics use casesDesign and implement robust ETL / ELT pipelines using Python and Apache Airflow (or modern orchestration equivalents)Develop and operate real-time data streaming and processing platforms using open-source technologies such as Apache Kafka, Apache Spark Streaming / Structured Streaming, Flink, or equivalentHandle sensitive financial, production, and customer data systems while strictly adhering to SOX controls, segregation of duties, change management, and audit requirementsPartner closely with business sponsors, product managers, manufacturing engineers, service operations, finance, and IT/security teams to gather requirements, scope projects, and deliver high-quality solutions quicklyCommunicate complex technical concepts and business impact effectively through written documentation, verbal discussions, architecture diagrams, and executive-level presentations (360-degree communication)Define, enforce, and continuously improve engineering standards, coding best practices, testing methodologies, CI/CD patterns, monitoring & alerting, and quality assurance processesActively participate in design reviews, code walkthroughs, and pull request reviews across the teamStay current with evolving open-source technologies and recommend adoption when they provide meaningful differentiation or operational efficiencyProvide global 24×7 data support on a rotating basis (on-call) and own ETL / streaming pipeline health monitoring, alerting, and incident resolutionRequired Qualifications6+ years of professional experience as a Data Engineer, Backend Engineer, or ETL developer building large-scale data platformsStrong proficiency in Python for data engineering (pandas, PySpark, SQLAlchemy, etc.)Deep hands-on experience designing and operating Airflow DAGs in production at scaleProduction experience with at least one distributed streaming system (Kafka, Kafka Streams, Spark Streaming, Flink, Pulsar, etc.)Solid understanding of data modeling for analytical workloads Experience building and operating systems under SOX compliance or similarly regulated environments (change control, audit trails, separation of duties, etc.)Strong SQL skills and understanding of distributed query enginesExperience with containerization (Docker) and orchestration (Kubernetes / ECS) is highly desirableExcellent communication skills — able to explain technical trade-offs to engineers and business value to non-technical stakeholders