Senior Data Engineer, Data Analytics

About the role

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer, Data Analytics based in the United States.

We are seeking an experienced Senior Data Engineer to build and optimize the data infrastructure powering next-generation humanoid robotics and automation. In this role, you will transform large volumes of raw robot telemetry, logs, and operational data into reliable, high-quality datasets that support analytics, debugging, and fleet-wide insights. You will work at the intersection of robotics, hardware engineering, software development, and cloud-based data infrastructure. Partnering with multidisciplinary teams, you will design scalable ETL pipelines, develop data models, and establish robust data quality and governance practices. Your work will directly support hardware reliability, system performance, machine learning, and operational decision-making. This is an opportunity to join a small, high-impact team shaping the future of robotics through innovative, data-driven solutions.

Accountabilities - Collaborate with robotics software and hardware engineering teams to define, collect, structure, and curate the data required for analytics, system monitoring, and debugging.

- Design, develop, maintain, and optimize scalable ETL pipelines that transform raw robot logs and telemetry into structured, reliable datasets.

- Leverage Apache Spark, Airflow or equivalent orchestration technologies, and AWS data services to build and manage production-grade data processing workflows.

- Own and maintain gold-layer datasets supporting fleet dashboards, hardware reliability models, operational reporting, and business performance metrics.

- Establish and maintain data freshness service-level agreements (SLAs), documentation standards, monitoring systems, and quality controls to ensure dependable data products.

- Implement data validation checks, schema evolution strategies, and metadata management practices using internal data registries and cataloging systems.

- Collaborate with analytics, reliability, and machine learning teams to design efficient data models that meet their analytical and operational requirements.

- Optimize logical data models and physical data partitioning to improve query performance across analytical engines and playback or root-cause analysis tools.

- Ensure efficient storage, processing, and accessibility of structured, numerical, and potentially multimodal robotics datasets.

- Contribute to shared software libraries and APIs using Python, Java, or C++ to improve data capture, serialization, and consumption when needed.

- Promote best practices in data architecture, pipeline reliability, dataset versioning, and data governance across the broader engineering organization.

- Work closely with cross-functional stakeholders to translate technical and operational requirements into scalable data platform solutions.