Software Engineer, Data Engineering

Field AI is looking for a Data Engineer to build reliable data pipelines and infrastructure for robotics data at scale. You will work on systems that move telemetry, logs, sensor data, and operational signals from deployed robots into the platforms used by autonomy, analytics, ML, and deployment teams. This role is ideal for someone with a strong software engineering background who enjoys building production systems, working with cloud infrastructure, and owning data pipelines end to end.

• Design, build, and maintain production data pipelines for robot telemetry, logs, sensor data, and operational events.

• Build reliable ingestion, transformation, and orchestration workflows using tools such as Airflow.

• Develop backend services, APIs, and internal tools that make data accessible to engineering, ML, analytics, and operations teams.

• Work with cloud infrastructure on AWS, including object storage, compute, databases, queues, and data processing services.

• Improve pipeline reliability through monitoring, retries, backfills, schema validation, and data quality checks.

• Collaborate with robotics, autonomy, infrastructure, and deployment teams to support real-world robot operations.

• Help scale data systems for growing fleets and increasingly large datasets.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

• 3+ years of experience in data engineering, backend engineering, infrastructure, or platform engineering.

• Strong software engineering skills, preferably in Python.

• Experience building and operating data pipelines in production.

• Familiarity with Airflow or similar workflow orchestration tools.

• Experience with AWS services such as S3, Lambda, ECS, EC2, RDS, Redshift, Glue, Athena, SQS, or related systems.

• Good understanding of testing, monitoring, debugging, and incident response for production systems.

• Ability to work across teams and translate messy real-world data problems into reliable systems.

• Experience with AWS CDK, Terraform, CloudFormation, or other infrastructure-as-code tools.

• Experience with Kubernetes, including deploying, operating, or debugging workloads in production clusters.

• Experience with robotics, autonomy, IoT, fleet telemetry, time-series data, or sensor-heavy systems.

• Experience with data lake or warehouse systems such as Redshift, Snowflake, BigQuery, Databricks, or Athena.

• Familiarity with CI/CD and modern DevOps practices.