Data Engineer, Manufacturing Quality, Amazon Leo

As a Data Engineer on the Manufacturing Quality team, you will architect, build, and operate scalable data infrastructure that enables data-driven quality and manufacturing decisions across production programs. You will independently own end-to-end data solutions, from ingestion of complex manufacturing and test data through processing, analytics, and visualization, serving quality engineers, production teams, and cross-functional stakeholders. You work with ambiguous manufacturing and quality challenges, translating them into reliable, extensible data systems that scale with the program.

Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Key job responsibilities
● Design and build end-to-end data pipelines that ingest, transform, and serve manufacturing and test data at scale
● Own the operation and reliability of production data systems, including monitoring, data quality validation, and incident response
● Build services and APIs that enable downstream teams and automated processes to consume and act on data programmatically
● Implement and maintain cloud infrastructure (AWS) supporting data ingestion, processing, and serving layers
● Apply quality engineering and statistical methods to manufacturing data for process monitoring, capability analysis, and yield characterization
● Evaluate and operationalize AI/ML models within manufacturing data pipelines to improve production quality for customers and accelerate engineering productivity
● Design and build dashboards and reporting solutions that surface actionable insights to production and quality stakeholders
● Partner with quality engineers, manufacturing engineers, and program teams to translate domain problems into technical data solutions
● Contribute to the team's technical direction through design documents, code reviews, and adherence to software engineering best practices
● Identify opportunities to improve data reliability, pipeline performance, and analytical coverage proactively
● Support the expansion of data solutions to new production lines and manufacturing programs as the team scales


A day in the life
Support data-driven root cause analysis by leveraging dashboards and analytical tools to identify trends, recurring defect topics, and severity patterns.

Conduct regular health checks on operational dashboards to ensure data accuracy, refresh reliability, and visual integrity.

Conduct regular health checks on operational dashboards to ensure data accuracy, refresh reliability, and visual integrity.

Proactively identify inefficiencies within the Quality and Production teams through data analysis and process mapping.

Leverage analytical tools (SPC dashboards, trend analysis, defect tracking) to pinpoint bottlenecks, reduce false failure rates, and improve first-pass yield.

Design and implement automated alerts for key operational triggers (e.g., SLA breaches, quality drift, escalation aging thresholds).

Build recurring reports that consolidate critical metrics—defect trends, productivity KPIs, and resolution timelines—reducing manual effort and ensuring consistent stakeholder visibility.