Data Platform Engineer, Autonomy Analytics

FieldAI; Peoria (IL); On-site; USD 110,000 - 160,000; Full time

Job summary

FieldAI in Peoria, IL seeks an experienced data/backend engineer to design and build the data platform, ingestion pipelines, and developer tooling that power our field robotics data initiatives.

You will handle intermittently connected data, large sensor payloads, and edge-to-cloud backfills; develop reusable ingestion SDKs, APIs, and connectors; integrate with BI, ML pipelines, and data stores; requires strong Python and SQL.

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.

Responsibilities

• Design and build the data platform, frameworks, and developer tooling that power ingestion across Field AI.
• Handle intermittently connected field data, large sensor payloads, edge-to-cloud synchronization, and backfill from offline deployments.
• Develop reusable ingestion SDKs, APIs, and services for onboarding new robotics data sources with minimal custom code.
• Build and maintain integrations across heterogeneous sources: robot/edge systems, fleet management and deployment tooling, cloud storage.
• Integrate the platform with downstream consumers: BI tools, ML pipelines, labeling systems, and issue tracking.
• Develop connectors and APIs (REST/gRPC, webhooks, CDC) for reliable data input and curated datasets.
• 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 pipelines

Backend engineering

Python

SQL

Problem solving

Education

Bachelor's or Master's in CS/Engineering

Tools

Kafka

Kinesis

Pub/Sub

Airflow

Dagster

BigQuery

Snowflake

Databricks

Redshift

Fivetran

Airbyte

Debezium

Job description

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.