Data Engineer, Quality Data Analytics & Systems

The Quality Engineering team plays a key role in ensuring safety of our customers by providing world class Quality of our products. Quality Systems and Analytics team is looking for a key player on the team who can help drive Quality data analytics and help cross-functional engineering organizations to provide opportunities in product quality improvements. Candidate should have experience working with large data sets, finding best ways to engineer the data to help create critical KPI metrics, building innovative visualizations and dashboards all the while keeping in mind what improvements can be driven in underlying data systems.

Tesla's mission is to accelerate the world's transition to sustainable energy. We are committed to hiring the world's best and brightest people to help make this future a reality. Every Tesla is designed to be the safest, quickest car in its class-with industry-leading safety, range, and performance.

• Collaborate with cross-functional problem-solving teams to drive process systems, analytics, data engineering improvements; use of effective QA methodologies to manage and facilitate issue resolution including root cause investigations and the development, implementation and monitoring of effective corrective and preventive actions
  
• Establish and maintain guidelines for analytics/data engineering, QA, change management and metric/project documentation best practices and ensure team adheres to those standards
• Design and implement metrics, applications and tools that will enable engineers by allowing them to self-serve their data insights
  
• Analyze manufacturing, equipment, and vehicle data to extract useful statistics and insights about failures in order to drive meaningful improvements to production quality and customer experience
• Interpret trends in manufacturing, equipment, and vehicle data, analyzing results using statistical techniques and depicting the story via dashboards and reports
• Automate analyses and author pipelines using SQL, Python, Airflow, and Kubernetes based ETL frameworks
• Monitor key product metrics, understanding root causes of changes in metrics
• Drive underlying data systems improvement by working with key cross-functional stakeholders
• Work effectively with engineers and conduct end-to-end analyses, from data requirement gathering to data processing and modeling
• Contribute to all stages of the factory and service quality data modeling including but not limited to problem formulation, data pre-processing, feature engineering, sample design, algorithm selection and evaluation, hyper-parameter tuning, deployment, implementation, and monitoring

• Bachelor's Degree in Management Information Systems, Computer Science, Math, Physics, Engineering, Statistics or another technical field, or equivalent
• Knowledge of SQL and experience with multiple data architecture paradigms (MySQL, MicrosoftSQL, Vertica, Oracle, kafka, Spark) with proficiency in Python, text processing, and python data analysis packages (eg. pandas, numpy)
• Knowledge of data visualization techniques and tools using Tableau, Power BI, Superset, Matplotlib, Plotly etc
  
• Understanding of various statistical techniques to effectively summarize data findings with strong knowledge of data warehousing concepts as well as data mining tools and techniques
• Proficiency in using open-source ML toolkits (eg. Pytorch, Tensorflow) and building NLP applications, with Strong ML and NLP fundamentals
• Knowledge of manipulating big data with Apache Spark (Python or Scale APIs)
• Able to work under pressure while collaborating with cross-functional teams and managing competing demands with tight deadlines
• Excellent communication skills and ability to work with different business stakeholders to understand, identify, and translate business challenges into data projects
• A passion and curiosity for data and data-driven decision making