About The Team

Agility Robotics is building the future of work through humanoid robots that operate in human environments. The Data Platform team builds the data infrastructure that powers everything from fleet operations and hardware reliability to business analytics and machine learning. We enable engineers across robotics, perception, and product teams to derive insight from the vast quantities of telemetry and log data generated by our robots in the field.

About The Role

We are looking for a Senior Data Engineer to join our Data Platform team and help shape the foundation of data-driven operations at Agility. In this role, you’ll work closely with robotics & and operations teams to design, curate, and maintain high-quality datasets that enable analytics, debugging, and fleet-scale insights.

You’ll bridge the gap between raw robot data and actionable information — working both on-robot data generation and in the cloud ingestion and processing pipelines. You’ll design transformations, author pipelines, and collaborate across teams to deliver reliable and queryable data products for hardware reliability, system health, workflow metrics, and root cause analysis.

What You’ll Do

• Collaborate with robot software and hardware teams to define, collect, and curate data needed for analytics and debugging.
• Develop and maintain ETL pipelines that transform raw robot logs and telemetry into structured datasets using Spark, Airflow (or equivalent orchestration tools), and AWS data services.
• Own the gold-layer datasets that power fleet dashboards, reliability models, and business metrics, with defined freshness SLAs, documentation, and monitoring so analytics and data science teams can rely on them.
• Implement data quality checks, schema evolution, and metadata management practices using our internal Data Registry and cataloging systems.
• Collaborate with internal consumers — reliability, analytics, and ML teams — to design efficient data models for their workflows.
• Occasionally contribute to shared libraries or APIs in Python, Java, or C++ to support data capture and consumption.
  
  

What We’re Looking For

Required:

• 5+ years of experience as a Data Engineer or similar role building and maintaining production data pipelines.
• Strong proficiency in Apache Spark or equivalent distributed data processing framework with complementary programming skills in Python, Java and/or Scala.
• Experience with Airflow, Dagster, Prefect, or other data orchestration systems.
• Proficiency with data formats such as Avro, Parquet, and structured/numeric datasets.
• Familiarity with table formats like Icerbeg, Delta Lake etc and medallion architecture. 
• Solid understanding of data modeling, schema evolution, and data quality best practices.
• Good intuitions of how to model datasets logically and partition them physically for  optimal query performance, both for analytical query engines and for playback or root-cause-analysis(e.g. ReRun, Foxglove etc)
• Comfort working cross-functionally with software, hardware, and analytics teams in a fast-paced environment

Nice to Have:

• Familiarity with data versioning or immutable dataset design (e.g., Apache Iceberg, Delta Lake)
• Experience with vision data (RGB, depth, point clouds, or perception outputs) pipelines and how to process, store, and query multimodal efficiently.
• Familiarity with C++ and willingness to contribute to lightweight logging or data serialization libraries.
• Experience with data catalog systems and metadata management.

Why You’ll Love Working Here

• Join a small, high-impact team building the data foundation for humanoid robotics.
• Work at the intersection of physical systems and large-scale data infrastructure.
• Collaborate with talented roboticists, software engineers, and data scientists shaping the future of automation

Location

• This is a fully remote role with the option to work hybrid if a commutable distance from our Salem, OR, Pittsburgh, PA, or Fremont, CA offices.

The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.