Data Platform Engineer, Autonomy Analytics

Field Ai; Irvine (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

Field AI is seeking a data/backend engineer to design and scale our ingestion platform, handling telemetry from robotics, LiDAR, and edge devices.

You will build reusable SDKs, APIs, and connectors, ensuring end-to-end reliability, data quality, and efficient streaming across on-prem and cloud deployments.

Collaborate with data science, platform, and robotics teams to onboard new data sources with minimal custom code.

Qualifications

• 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
• 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: 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.

Responsibilities

• 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, 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 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.

Skills

Data engineering

Backend engineering

Python

SQL

Kafka

Kinesis

Pub/Sub

Airflow

Dagster

BigQuery

Snowflake

Databricks

Redshift

Fivetran

Airbyte

Debezium

Custom connectors

Data quality

Testing

Monitoring

Lineage

Incident response

Education

Bachelor's or Master's degree

Tools

Kafka

Kinesis

Pub/Sub

Airflow

Dagster

BigQuery

Snowflake

Databricks

Redshift

Fivetran

Airbyte

Debezium

Job description

Responsibilities

• 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.

Qualifications

• 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.

Additional Experience

• 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.