DataEngineerII,AmazonManufacturingServices(AMS)

Do you want to turn manufacturing data into decisions that move physical parts through a factory? Amazon Manufacturing Services (AMS) runs 135+ machines producing custom parts for over 100 Amazon organizations, and nearly every machine, order, and operator action generates data worth analyzing. You will join a small, growing data engineering team that owns the pipelines, warehouse, dashboards, and ML workflows that turn raw signals from our services and enterprise systems into throughput, utilization, and quality insights for shop floor users and AMS leadership. The scope is broad, the stakeholders are in the building, and your models will influence how Amazon makes things. Key job responsibilities - Design and operate data pipelines on AWS Glue (PySpark), Kinesis, S3, and EventBridge to ingest DynamoDB streams and enterprise system data into the AMS data lake - Model and maintain the Redshift warehouse and S3/Athena data lake that power analytics across AMS services - Build ingestion and modeling layers for enterprise data sources including SAP S/4HANA, JobBoss, Siemens Teamcenter, and Dot Compliance - Develop QuickSight dashboards for shop floor operators, planners, and AMS leadership, covering operational metrics and executive KPIs - Build and deploy ML models and pipelines for manufacturing use cases such as demand forecasting, machine health prediction, and scheduling optimization - Own data quality, lineage, and documentation across the AMS analytics stack - Collaborate with senior SDEs on architecture, service event schemas, and integration patterns, while holding significant ownership over your part of the data domain A day in the life Your day starts with a standup alongside SDEs, data engineers, and manufacturing stakeholders. You pick up where you left off on a React component that displays real-time resource status for shop floor planners. After lunch, you shift to a backend service, designing a DynamoDB schema for part versioning. A code review comes in