WHAT YOU'LL GET TO DO Design and build the data platform, frameworks, and developer tooling that power ingestion across Field AI. Handle the realities of field data: intermittent connectivity, large sensor payloads (LiDAR, camera, IMU), edge-to-cloud synchronization, and backfill from offline deployments. Develop reusable ingestion SDKs, APIs, and services that enable teams to onboard new robotics data sources with minimal custom code. Build and maintain integrations across heterogeneous sources: robot/edge systems, fleet management and deployment tooling, simulation outputs, and cloud object storage. Integrate the platform with downstream consumers: BI tools, ML training and evaluation pipelines, labeling systems, and issue tracking. Develop connectors and APIs (REST/gRPC, webhooks, CDC) so internal teams can feed data in and consume curated datasets reliably. Own integration reliability end to end: schema contracts, versioning, retries, backfills, and monitoring. Optimize pipeline performance, scalability, and cost across growing fleet deployments. WHAT YOU HAVE Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. 3–5+ years of experience in data engineering or backend engineering focused on pipelines and infrastructure. Strong programming skills in Python and SQL (C++, Scala, or Java a plus). Production experience with streaming systems (Kafka, Kinesis, Pub/Sub) and orchestration tools such as Airflow or Dagster. Experience with a modern warehouse or lakehouse (BigQuery, Snowflake, Databricks, Redshift) and cloud object storage at scale. Experience building integrations across systems: third-party APIs, internal services, and CDC/ELT tooling (Fivetran, Airbyte, Debezium, or custom connectors). Experience building for data quality: testing, monitoring, lineage, and incident response. Strong problem-solving skills and ability to work in interdisciplinary teams. THE EXTRAS THAT SET YOU APART Experience with robotics, autonomy, automotive, or other telemetry-heavy operational data (bag files, fleet logs, time-series sensor data). Familiarity with robotics middleware and log formats such as ROS/ROS2, MCAP, or rosbag.