Data Platform Engineer, Infrastructure

FieldAI•Irvine, CA

11d•Onsite

About The Position

As a Data Platform Engineer, Infrastructure, you will build and operate the cloud foundation the entire data platform runs on — the systems every robot log, dashboard, and model-training run depends on. You will collaborate with pipeline and analytics engineers, as well as edge and robotics teams, to keep the platform reliable, secure, and cost-efficient as our fleet grows.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
3–5+ years of experience in infrastructure, platform, DevOps, or SRE roles, ideally supporting data-intensive systems.
Strong experience with a major cloud (AWS or GCP), Kubernetes, and infrastructure-as-code (Terraform or similar).
Strong programming skills in Python, Go, or similar for automation and internal tooling.
Experience operating production systems: monitoring, alerting, incident response, and capacity planning.
Working knowledge of data infrastructure components (warehouses, object storage, streaming systems, orchestrators) and what they demand from underlying infrastructure.
Experience with security fundamentals: IAM, network security, secrets management.
Strong problem-solving skills and ability to work in interdisciplinary teams.

Nice To Haves

Experience supporting data or ML platforms specifically (Spark/Databricks clusters, GPU scheduling, feature stores).
Experience with high-volume telemetry or IoT/edge fleets.
Track record of significant cloud cost optimization.
Prior experience as an early platform/infrastructure hire, building from zero.

Responsibilities

Design, build, and operate the cloud infrastructure for the data platform: compute (Kubernetes, serverless, batch clusters), storage (object stores, warehouse/lakehouse), and networking.
Manage everything as code: Terraform/IaC, GitOps workflows, and CI/CD for infrastructure and data services.
Provision and tune infrastructure for heavy workloads: large-scale sensor data processing, distributed pipeline execution, and ML training and evaluation jobs.
Own platform reliability: SLAs/SLOs, on-call practices, incident response, and capacity planning as fleet data volume grows.
Implement security and governance: IAM and access controls, secrets management, encryption, audit logging, and environment isolation.
Own cloud cost management: monitoring, budgeting, and optimization across storage and compute.
Build the observability stack (metrics, logging, tracing) for all data platform services.
Build internal tooling and paved paths so pipeline and analytics engineers can ship without becoming infrastructure experts.