Data Engineer, Manufacturing Quality, Amazon Leo

Amazon • Redmond, WA

1d • Onsite

About The Position

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.

Requirements

• 3+ years of data engineering experience
• Proficiency in SQL at scale (Trino/Presto, Spark SQL, Redshift, or equivalent)
• Proficiency in at least one high-level programming language (Python, Java, or Scala)
• Familiarity with statistical methods applied to manufacturing data
• Experience in manufacturing, hardware, or production environments
• 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.

Nice To Haves

• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
• Experience designing and building production data pipelines end-to-end
• Experience with AWS services (S3, Lambda, DynamoDB, Kinesis, Glue, or similar)
• Proficiency with data visualization tools and building solutions that are both technically sound and user-friendly (QuickSight, Grafana, Tableau)

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
• 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.
• 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.

Benefits

• sign-on payments
• restricted stock units (RSUs)
• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed