Cloud Platform - Data Engineer

Lab37 Robotics, is a technology company focused on the development and deployment of robots designed specifically for direct-to-customer food production. Our mission is to revolutionize the food industry by creating innovative robotic solutions that enhance efficiency, quality, and customer satisfaction. We are passionate about pushing the boundaries of technology to deliver cutting-edge products that meet the evolving needs of our clients.

Take on ownership of the data and ML platform layer that powers Lab37's robotics fleet. This spans building ETL pipelines that turn raw robot telemetry into actionable analytics, managing the ML training infrastructure that produces the computer vision models running on our robots, and ensuring the observability and reliability of the entire data stack.

Work with a small, high-impact platform team to build the systems that every product team at Lab37 consumes — from data scientists training models, to kitchen operations teams viewing dashboards, to engineers deploying new ML models to robots in the field.

• →Build and maintain ETL pipelines that ingest, validate, transform, and load robot telemetry data into BigQuery for analytics and ML training
• →Manage ML training infrastructure using Argo Workflows on Kubernetes — from data extraction through model training, evaluation, and registration
• →Design and maintain data quality checks and observability dashboards (the "glass panel" for data pipeline health)
• →Own the data warehouse layer — schema design, incremental loading, dbt transforms, and BigQuery/Athena query optimization
• →Build dashboard infrastructure (Superset, Grafana) that kitchen operations and leadership teams rely on for real-time insights
• →Collaborate with data scientists to productionize model training pipelines — from notebook experiments to reproducible, automated workflows
• →Contribute to infrastructure-as-code (Terraform) and CI/CD pipelines for data and ML workloads
• →Participate in on-call rotations for data pipeline reliability

• 2+ years in data engineering, ML infrastructure, or analytics engineering
• Strong experience with workflow orchestration tools (Argo Workflows, Airflow, Prefect, or similar)
• Python data stack proficiency — pandas, SQL, dbt, and comfort writing production-quality data pipelines
• Experience with cloud data services (BigQuery, Athena, S3, or GCP equivalents)
• Experience building or maintaining ML training pipelines (not just using them as a consumer)
• Docker containerization and Kubernetes basics
• SQL fluency — you'll write complex queries daily and care about query performance
• Familiarity with documentation tools and writing design documents

• ETL debugging, data quality frameworks, and anomaly detection
• Experience with hybrid cloud environments (AWS + GCP)
• Robotics or IoT data experience — understanding the challenges of real-world sensor data
• Experience with dbt for data transformations and warehouse modeling

This role is based in our Warrendale office. As a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate. We do our best work together. That’s why all of our office-based teams work onsite, five days a week.

The base salary range for this role is $130,000 - $164,500 per year.

Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.

Base salary is just one part of your total rewards package. You may also be eligible for equity awards and an annual performance-based bonus.

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