Data Engineer II, Amazon Manufacturing Services (AMS)

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 from a senior engineer working on an enterprise integration bridge, and you spend time understanding how AMS connects to external manufacturing platforms.

Benefits

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

Qualifications

• 3+ years of data engineering experience
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
• Experience with data modeling, warehousing and building ETL pipelines
• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS