Data Platform Engineer, Data Pipelines

**Location: Irvine, CA** [Field AI](https://www.fieldai.com/) is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven(1) approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. **About Field AI** Field AI is at the forefront of robotic embodied AI, transforming industries like construction, security, mining, and manufacturing. Our autonomous robots operate globally, often in harsh environments, delivering critical insights to customers. Whether monitoring construction progress, ensuring safety compliance, or conducting predictive maintenance, Field AI is advancing technology to make a meaningful impact. Learn more at [](https://fieldai.com/). **About the Data Platform Team** Every robot we deploy generates a continuous stream of telemetry, sensor logs, and operational data from environments around the world. The Data Platform team builds the systems that capture every autonomy intervention, anomaly, and operational signal from globally deployed robots, classify them, and turn them into the ranked problem list that drives our engineering roadmap. **About the Job** As a **Data Platform Engineer, Data Pipelines**, you will design and build the systems and integrations that help move data reliably from robots in the field to the teams and services that depend on it — analytics, autonomy, ML training, and deployment operations. You will collaborate with cross-functional teams spanning robotics, autonomy, and deployment to build the data backbone of a field robotics company. ### 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**. - Experience with edge computing or intermittently connected data collection. - Experience with **dbt** or similar transformation frameworks.