Software Engineer, Data, Autonomy

Position: Staff Software Engineer, Data, Autonomy
About Rivian

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.

Role Summary

The Autonomy organization at Rivian is seeking a highly skilled and hands-on Staff Data Engineer to join our Cloud and Data team. This role is ideal for an experienced engineer who thrives at the intersection of data architecture, distributed systems, and scalable engineering.

You will be responsible for designing and evolving critical data infrastructure to support the next generation of autonomous vehicle development. You will lead the technical direction for scalable, reliable, and secure data pipelines, while staying deeply involved in hands-on development. The ideal candidate is a self-starter who can work independently, lead complex projects end-to-end, and proactively drive cross-functional initiatives. You will also collaborate closely with engineering and product teams, mentor fellow engineers, and influence architectural decisions that support long-term platform growth.

Responsibilities

• Architect and build scalable, high-throughput data pipelines to process vehicle telemetry, sensor logs, and simulation outputs using Spark, Databricks, and AWS services.
• Design and evolve distributed data architecture across batch, streaming, and hybrid processing frameworks to support real-time and offline autonomy use cases.
• Independently lead the design, development, and delivery of complex data engineering projects, from requirements gathering to deployment.
• Partner with cross-functional teams including Perception, Simulation, QA, and Safety to deliver reliable and performant data infrastructure aligned with autonomy goals.
• Implement engineering best practices for CI/CD, monitoring, testing, and observability across all data workflows.
• Mentor and guide team members, providing technical leadership and growing expertise across the team in areas such as system design, software development, and distributed computing.
• Establish frameworks for privacy-aware data collection, geospatial enrichment, and high-volume data ingest and transformation.
• Evaluate and introduce new technologies and frameworks to optimize pipeline performance, reduce costs, improve reliability and increase reliability.
• Take ownership of ambiguous problems, drive them to clarity, and implement practical, scalable solutions.
• Stay current with the evolving data engineering ecosystem and apply the latest tools and practices where appropriate.

Qualifications

• Architect and build scalable, high-throughput data pipelines to process vehicle telemetry, sensor logs, and simulation outputs using Spark, Databricks, and AWS services.
• Design and evolve distributed data architecture across batch, streaming, and hybrid processing frameworks to support real-time and offline autonomy use cases.
• Independently lead the design, development, and delivery of complex data engineering projects, from requirements gathering to deployment.
• Partner with cross-functional teams including Perception, Simulation, QA, and Safety to deliver reliable and performant data infrastructure aligned with autonomy goals.
• Implement engineering best practices for CI/CD, monitoring, testing, and observability across all data workflows.
• Mentor and guide team members, providing technical leadership and growing expertise across the team in areas such as system design, software development, and distributed computing.
• Establish frameworks for privacy-aware data collection, geospatial enrichment, and high-volume data ingest and transformation.
• Evaluate and introduce new technologies and frameworks to optimize pipeline performance, reduce costs, improve reliability and increase reliability.
• Take ownership of ambiguous problems, drive them to clarity, and implement practical, scalable solutions.
• Stay current with the evolving data engineering ecosystem and apply the latest tools and practices where appropriate.

Pay Disclosure

The salary range for this role is $206,500-$258,100 for San Francisco Bay Area based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs.

We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a…