Software Data Engineer, Fleet Risk

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Apache Hadoop, Scala Programming, Machine learning techniques, Data science techniques, PyTorch, TensorFlow, MapReduce, R Programming

Job Description

Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract.

As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations.

We are seeking strong Software Data Engineers who will solve complex data problems using data mining and analysis. You will be responsible for architecting and building out infrastructure for data ingestion from various sources – vehicles, internal business systems, and external sources.

This is what you’ll do:

• Design and develop secure, scalable, high-performance and reliable data platform for fleet safety, security, and risk management features.
• Use Data & Analytics to help improve fleet health and to provide insights and predict anomalies using time series data.
• Design, extend and review software architecture – be hands on and involved with every stage of the product development life cycle 
• Build high quality, testable, and scalable backend systems including REST APIs, batch jobs, and system integrations. 
• Work with internal teams to ingest system data to help build fleet insights and analysis. 
• Develop state of the art code – influence/establish the software development culture of the team. Establish standards and best practice for software engineering. 

This is what you’ll need:

• Ability to understand complex business problems and provide software solutions. 
• 4+ years of software experience with 1+ years of experience in building large scale data platforms. 
• Proven track record of anomaly prediction using time series data and deploying models to production. 
• Comprehensive knowledge of modern data science and product analytics tools and techniques such as Python, R, Machine Learning, advanced statistics and data mining techniques.
• Deep knowledge of Amazon’s AWS ecosystem (ex: Lambda architecture, EMR, TimeStream, RedShift) on big data.
• Strong hands-on experience with NOSQL, Timeseries, Graph, Columnar and Relational databases. 
• Bachelors/Masters in computer science, engineering, mathematics, or a related technical discipline preferred