Software Engineer, Data Engineering, Toolbox Software


The Role 
Data is deeply embedded in the product and engineering culture at Tesla. We collect data from our vehicle fleet and use it to support service and engineering in identifying issues at the fleet or vehicle level, enabling diagnostic automation and technician workflow to identify issues and take corrective actions. We’re looking for a talented engineer to join us in analyzing and reporting on data, creating ETL processes, and implementing improvements to our data platform. Your work will affect thousands of technicians and Tesla engineers daily, enabling them to keep our vehicle fleet on the road and improve customer experience. 



  • Develop analysis and reporting focused on operational, technical metrics and KPIs 
  • Deliver quick turnarounds on dashboards, identify trends and/or issues within data sets, and make recommendations to influence business decisions and investments 
  • Improve our data platform by improving performance, stability and maintainability of our relational databases, ETL pipelines, queue (Kafka, RabbitMQ) environments 
  • Evaluate the effectiveness of engineering initiatives and deliver actionable insights to improve service productivity 
  • Present analytical results and insights to business partners to answer strategic questions and influence business decisions  


  • Minimum of 2-3 years of experience in a data analytics related capacity 
  • Understanding of relational database theory and proficiency in writing, understanding, and optimizing complex SQL code 
  • Software engineering fundamentals 
  • Experience with Business Intelligence tools (eg Tableau or Superset) and real time dashboard development 
  • Experience with Jupyter, Pandas workflow 
  • Understanding of queue-based environments including Kafka or AMQP 
  • Robust DevOps abilities and a strong predilection for automation 
  • Strong communication, organizational, and analytical and problem solving skills 
  • Strong business acumen 

Nice to Have 

  • Knowledge of statistical models, predictive model development (Python, R, SAS, etc), machine learning, proficiency in Spark