Senior Data & Analytics Engineer

Amazon Leo is Amazon's low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will serve people and organizations operating in locations without reliable connectivity.

The Amazon LEO Battery organization develops and validates advanced battery systems that power Amazon's Low Earth Orbit satellite constellation. Our team works across battery engineering, test and validation, manufacturing, automation, systems engineering, quality, reliability, software, and production operations.

As a Senior Data & Analytics Engineer, you will own the data infrastructure, analytics, and engineering tools that connect battery development, test, manufacturing, and production data. You will enable end-to-end traceability, automate data processing and reporting, and develop scalable tools that help engineers understand product performance, identify failures and trends, improve processes, and make faster technical decisions.

Key job responsibilities:

• Architect and maintain scalable data pipelines and platforms for battery test, manufacturing, laboratory, and production systems.
• Build unified data models connecting cells, PCBAs, battery packs, test results, manufacturing processes, equipment, configurations, and quality records.
• Establish end-to-end product and test traceability across serial numbers, cell lots, hardware and software revisions, test procedures, equipment, calibration status, and nonconformance data.
• Develop automated data ingestion, transformation, validation, and processing from test systems, manufacturing equipment, databases, APIs, and engineering files.
• Build engineering analytics, dashboards, and tools for test performance, lifecycle data, FPY, SPC, production trends, equipment performance, and failure investigations.
• Develop automated data-analysis and reporting workflows that convert raw test data into engineering metrics, pass/fail results, trends, and actionable insights.
• Establish data quality, lineage, metadata, and schema standards to ensure engineering data is accurate, reproducible, and trustworthy.
• Develop reusable APIs, libraries, datasets, and applications that allow engineering teams to efficiently access and analyze battery data.
• Partner with Battery Test, Systems, Manufacturing Automation, ATE Software, Quality, Reliability, Manufacturing, and Production teams to translate engineering needs into scalable data and analytics solutions.
• Drive technical architecture, analytics standards, tool development, documentation, and long-term battery data strategy.