Data Platform Engineer, Autonomy Analytics
Employment type
Full-time
Work setting
On-site
Location
Peoria, IL
Schedule
Day shift
Posted
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Job overview
The Data Platform Engineer, Autonomy Analytics role is listed as onsite in Peoria, IL. Compensation is not specified. The role supports FieldAI’s data platform and ingestion across the company. The engineer builds reusable infrastructure for collecting, integrating, and delivering robotics field data to internal teams and downstream systems. The work addresses intermittent connectivity, large sensor payloads, and edge-to-cloud synchronization while improving reliability, scalability, and cost as fleet deployments grow.
What you'll do
- Design and build data platform frameworks and developer tooling for ingestion
- handle field data challenges including intermittent connectivity, large LiDAR, camera, and IMU payloads, edge-to-cloud synchronization, and offline backfills
- develop reusable ingestion SDKs, APIs, and services
- integrate robot and edge systems, fleet management and deployment tools, simulation outputs, and cloud object storage
- connect data to BI, ML training and evaluation, labeling, and issue-tracking systems
- own schema contracts, versioning, retries, backfills, and monitoring
- optimize pipeline performance, scalability, and cost.
What we're looking for
- Skills & competencies
- data platform engineerautonomy analyticsfull-timepeoria ildata engineeringpythonsqlrobotics datadata ingestionstreaming systemscloud object storageedge-to-cloud synchronization
- Work arrangement
- Weekend coverage required
Why this role
Requires a bachelor’s or master’s degree in a technical field and 3–5+ years of data or backend engineering experience. Python, SQL, streaming systems, orchestration tools, and modern warehouse or lakehouse experience are specified.
About the employer
FieldAI is hiring for this role. Industry: Engineering Services. Sector: 54.
Additional details
- Industry sector
- 54
- Industry
- Engineering Services
- Occupation code
- 15-1243.00
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Listing ID: e5879ea0-80b6-43c6-bd2b-0216540ced71