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# Sr. Staff Data Engineer, IT Applications

- **Company:** Tesla Motors
- **Location:** Fremont, CA, United States
- **Experience:** Expert
- **Salary:** $180,000.0 - $342,000.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Adobe InDesign, Application Programming Interfaces (APIs), Airflow, Software Applications, Audit Trail, Big Data, Databases, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Database Queries, Microsoft SQL Server, MySQL, NoSQL, Open Source Technology, SQLAlchemy, Data Streaming, Tableau (Software), Strategies of Testing, Apache Spark, Backend, Pandas, Containerization, Pyspark, Kubernetes, Apache Flink, Apache Kafka, Spark Streaming, Vertica, Docker
- **Published:** August 2, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/17784878?backUrl=%2Fcareer%2F17784878%2FSr-Staff-Data-Engineer-It-Applications-California-Fremont

## About the Role

* 8+ years of professional experience as a Data Engineer, Backend Engineer, or ETL developer building large-scale data platforms
 * Proficient withSQL, Pythonfor data engineering (pandas, PySpark, SQLAlchemy, API Scrapping, etc.)
 * Strong Proficiency with database systems like Vertica, MySQL, SQL Server, NoSQL, OpenSearch, etc. is required
 * Deep hands-on experience designing and operatingAirflowDAGs in production at scale
 * 3+ years of production experience with at least one distributed streaming system (Kafka,Kafka Streams,Spark Streaming,Flink,Pulsar, etc.)
 * Solid understanding ofdata modelingfor analytical workloads
 * Experience building and operating systems underSOX complianceor similarly regulated environments (change control, audit trails, separation of duties, etc.)
 * Strong SQL skills and understanding of distributed query engines
 * 3+ years of experience with containerization (Docker) and orchestration (Kubernetes / ECS) is required
 * Excellent communication skills. Be able to explain technical trade-offs to engineers and business value to non-technical stakeholders as well as mentor junior engineers

## Description

We are looking for a hands-on Sr. Staff Data Engineer to help build and evolve Tesla's next-generation enterprise analytics platform that powers business intelligence and operational intelligence for Vehicle and Optimus Service 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 Service operations and customer experience.
What You'll Do

 * Architect, build, and maintainstate-of-the-art Enterprise Data Warehouse / Lakehousesolutions that serve both batch and near-real-time analytics use cases
 * Design robustETL / ELT pipelinesusingPythonandApache Airflow(or modern orchestration equivalents)
 * Develop and operatereal-time data streamingand processing platforms using open-source technologies such asApache Kafka,Apache Spark Streaming / Structured Streaming,Flink, or equivalent
 * Maintain platforms- Vertica, SQL Server, Airflow, Tableau etc
 * Handlesensitive financial, production, and customer datasystems while strictly adhering toSOX controls, segregation of duties, change management, and audit requirements
 * Partner 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 quickly
 * Communicate 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 improveengineering standards, coding best practices, testing methodologies, CI/CD patterns, monitoring & alerting, and quality assurance processes
 * Actively participate indesign reviews,code walkthroughs, and pull request reviews across the team
 * Stay current with evolving open-source technologies and recommend adoption when they provide meaningful differentiation or operational efficiency


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