Senior Data Engineer, Agility Robotics

• 5+ years of experience as a Data Engineer or in a similar role building and maintaining production data pipelines
• Strong proficiency in Apache Spark or an 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 such as Iceberg or Delta Lake, and with medallion architecture
• Solid understanding of data modeling, schema evolution, and data quality best practices
• Good intuition for modeling datasets logically and partitioning them physically for optimal query performance, both for analytical query engines and for playback or root-cause analysis (e.g., ReRun or Foxglove)
• Comfort working cross-functionally with software, hardware, and analytics teams in a fast-paced environment

Desirable

• Agility Robotics’ Data Platform team builds the data infrastructure that powers fleet operations, hardware reliability, business analytics, and machine learning. The team enables engineers across robotics, perception, and product teams to derive insight from the telemetry and log data generated by robots in the field
• Join the Data Platform team and help shape the foundation of data-driven operations by working with robotics and operations teams to design, curate, and maintain high-quality datasets for analytics, debugging, and fleet-scale insights
• Bridge raw robot data and actionable information, working on both on-robot data generation and cloud ingestion and processing pipelines. Design transformations, author pipelines, and collaborate across teams to deliver reliable, queryable data products for hardware reliability, system health, workflow metrics, and root-cause analysis
• 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 the internal Data Registry and cataloging systems
• Collaborate with internal consumers—including 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