Staff Data Engineer

<p>Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is based in San Jose, CA and require 5 days/week in-office collaboration.</p> <p>We’re looking for a Staff Data Engineer to own the <strong>design</strong>, <strong>scalability</strong>, and <strong>reliability</strong> of our data platform powering fleet analytics, regression detection, and release validation across Figure’s humanoid robot fleet. This role blends elements of Data Engineering, Data Architecture, and Data Science. This role is ideal for someone who has built <strong>large-scale telemetry or platform data systems</strong> for domains like <strong>EVs, autonomous driving, or robotics fleets.</strong></p> <p>You’ll serve as the <strong>technical</strong> <strong>anchor</strong> for the robot data platform, ensuring robot platform data is <strong>accurate</strong>, <strong>accessible</strong>, and <strong>actionable</strong> across the entire engineering organization. This is a hands-on individual-contributor role with direct influence over the software and hardware decisions shaping the next generation of humanoid robots.</p> <p>If you’re a <strong>hardcore data engineer</strong> who thrives on solving complex, cross-disciplinary problems — join us and help define the data backbone for Humanoid robots.</p> <p><strong>Key Responsibilities:</strong></p> <ul> <li><strong>Architect and evolve Figure’s end-to-end platform data pipeline</strong> — from robot telemetry ingestion to warehouse transformation and visualization.</li> <li><strong>Improve and maintain existing ETL/ELT pipelines</strong> for scalability, reliability, and observability.</li> <li><strong>Detect and mitigate data regressions, schema drift, and missing data</strong> via validation and anomaly-detection frameworks.</li> <li><strong>Identify and close gaps in data coverage, ensuring high-fidelity metrics coverage across releases and subsystems.</strong></li> <li><strong>Define the tech stack and architecture</strong> for the next generation of our data warehouse, transformation framework, and monitoring layer.</li> <li><strong>Collaborate with robotics domain experts</strong> (controls, perception, Guardian, fall-prevention) to turn raw telemetry into structured metrics that drive engineering/business decisions</li> <li><strong>Partner with fleet management, operators, and leadership</strong> to design and communicate fleet-level KPIs, trends, and regressions in clear, actionable ways.</li> <li><strong>Enable self-service access </strong>to clean, documented datasets for engineers</li> <li><strong>Develop tools and interfaces</strong> that make fleet data accessible and explorable for engineers without deep data backgrounds</li> </ul> <p><strong>Requirements:</strong></p> <ul> <li><strong>Experience owning or architecting large-scale data platforms</strong> — ideally in <strong>EV, autonomous driving, or robotics fleet environments</strong>, where telemetry, sensor data, and system metrics are core to product decisions.</li> <li>Deep expertise in <strong>data engineering and architecture</strong> (data modeling, ETL orchestration, schema design, transformation frameworks).</li> <li>Strong foundation in <strong>Python, SQL, and modern data stacks</strong> (dbt, Airflow, Kafka, Spark, BigQuery, ClickHouse, or Snowflake).</li> <li>Experience building <strong>data quality, validation, and observability</strong> systems to detect regressions, schema drift, and missing data.</li> <li><strong>Excellent communication skills</strong> — able to <strong>understand technical needs from domain experts</strong> (controls, perception, operations) and <strong>translate complex data patterns into clear, actionable insights</strong> for engineers and leadership.</li> <li><strong>First-principles under ... (truncated, view full listing at source)