Staff Data Platform Engineer, Applied Intuition

• This is a highly impactful role for someone who thrives at the intersection of technical leadership, system design, and cross-team collaboration, and who wants to elevate the capabilities of our data platform to support cutting-edge ML and autonomy development
• 10+ years of experience in data engineering, distributed systems, or related backend engineering roles
• Proven track record of architecting and building large-scale, data-intensive, distributed systems
• Deep experience with complex ETL pipelines, data ingestion frameworks, and data processing engines (e.g., Spark, Flink, Airflow, Flyte, Kafka, etc)
• Strong understanding of data modeling, partitioning, schema evolution, and metadata management at scale
• Hands-on experience with cloud object stores (e.g., AWS S3), lakehouse architectures, and data warehouse technologies
• Ability to drive technical discussions with both engineers and non-technical stakeholders
• Strong communication and leadership skills, with the ability to influence across teams and functions
• Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway

Desirable

• We are looking for a Staff Data Platform Engineer to shape the strategy, architecture, and execution of our next-generation data ecosystem
• In this role, you will partner closely with our autonomy stack teams—the “customers” of our platform—to deeply understand their workflows, pain points, and evolving needs
• You will lead the design and development of robust, scalable, and data-intensive distributed systems that power the full lifecycle of autonomous driving data: collection, ingestion, curation, machine learning training, and evaluation
• You’ll be a key decision-maker in defining best practices, data contracts, and integration patterns across upstream and downstream systems
• Drive Data Platform Strategy – Define the long-term vision and technical roadmap for the data ecosystem, balancing scalability, reliability, cost efficiency, and developer experience
• Partner with Customers – Engage deeply with autonomy stack teams to gather requirements, uncover pain points, and translate them into platform capabilities
• Lead Complex Workflow Development – Architect and build end-to-end, large-scale ETL and data workflows for data collection, ingestion, transformation, and delivery
• Establish Data Contracts – Define and enforce clear SLAs and contracts with upstream data producers and downstream data consumers
• Set Best Practices – Champion data engineering best practices around governance, schema evolution, lineage, quality, and observability
• Mentor and Influence – Guide other engineers and teams on designing scalable data systems and making strategic technology choices
• Collaborate Across Functions – Work with infrastructure, ML platform, autonomy stack, and labeling teams to ensure smooth data flow and ecosystem integration
• You’ll be at the heart of enabling autonomous driving innovation—building the systems that allow teams to harness massive amounts of real-world and simulated data for developing and validating our stack
• You’ll have the autonomy to set the technical direction, the opportunity to solve some of the hardest data engineering problems, and the ability to make a measurable impact on the success of our platform and products