Robotics Data Platform Engineer - ML/ETL (Equity Eligible)

Cssmerge; Warrendale (Allegheny County); On-site; USD 130,000 - 164,500; Full time

Benefits offered by this job

Medical, dental, vision

401(k)

HSA

Paid time off

Equity awards

Job summary

Lab37 Robotics in Warrendale is seeking a data platform engineer to own the data and ML stack powering our robotics fleet. You will build ETL pipelines, manage ML training infrastructure, and ensure observability and reliability across the data stack.

You will collaborate with a small, high-impact platform team to deliver dashboards and models to product teams, operators, and leadership, enabling real-time insights and scalable machine learning deployments onsite.

Qualifications

• 2+ years in data engineering, ML infrastructure, or analytics engineering.
• Proficient Python data stack: Pandas, SQL, dbt, and building production pipelines.
• Experience with cloud data services and containerized workloads.
• Strong SQL skills and interest in data quality.

Responsibilities

• Build and maintain ETL pipelines ingesting robot telemetry into BigQuery.
• Manage ML training infra using Argo Workflows on Kubernetes.
• Design data quality checks and dashboards for pipeline health.
• Own data warehouse design, incremental loading, and queries.
• Develop real-time dashboards for operations and leadership.
• Collaborate with data scientists to productionize training pipelines.
• Contribute to IaC and CI/CD for data and ML workloads.
• Participate in on-call rotations.

Skills

Data engineering

ML infrastructure

Analytics engineering

Python data stack

SQL

Tools

Argo Workflows

Airflow

Prefect

Kubernetes

Docker

dbt

BigQuery

Athena

S3

Job description

Lab37 Robotics in Warrendale is seeking a data platform engineer to own the data and ML stack powering our robotics fleet. You will build ETL pipelines, manage ML training infrastructure, and ensure observability and reliability across the data stack.

You will collaborate with a small, high-impact platform team to deliver dashboards and models to product teams, operators, and leadership, enabling real-time insights and scalable machine learning deployments onsite.