Staff Data Platform Engineer, Applied Intuition

• 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

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
• 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
• 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