Data Engineer II

The Data Engineer II role at Amazon Manufacturing Services focuses on designing and operating data pipelines, maintaining the data warehouse and data lake, and delivering dashboards and ML workflows to illuminate shop floor operations. This onsite position is based in Bellevue, WA, and involves collaboration with senior engineers on architecture and integration patterns while owning a portion of the data domain.

Responsibilities

• Design and operate data pipelines using AWS Glue with PySpark, Kinesis, S3, and EventBridge to ingest DynamoDB streams and enterprise data into the AMS data lake
• Model and maintain the Redshift data warehouse and the S3/Athena data lake that powers analytics across AMS services
• Develop ingestion and modeling layers for enterprise sources such as SAP S/4HANA, JobBoss, Siemens Teamcenter, and Dot Compliance
• Create QuickSight dashboards for shop floor operators, planners, and AMS leadership, covering operational metrics and executive KPIs
• Build and deploy machine learning models and pipelines for manufacturing use cases including 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 maintaining substantial ownership over a segment of the data domain

Requirements

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

Technologies

• AWS Glue, PySpark
• Kinesis, S3, EventBridge
• DynamoDB, Redshift, Athena
• SAP S/4HANA, JobBoss, Siemens Teamcenter, Dot Compliance
• QuickSight
• React, Python, Java, Scala, NodeJS
• EMR, FireHose, Lambda, IAM

Benefits

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

A Day in the Life

Each day begins with a standup alongside software engineers, data engineers, and manufacturing stakeholders. You may continue a React component that shows real-time resource status for shop floor planners, then switch to a backend task such as designing a DynamoDB schema for part versioning. A code review from a senior engineer working on an enterprise integration bridge prompts you to map how AMS connects with external manufacturing platforms.

Some weeks lean toward frontend work, crafting interactive data visualizations or responsive layouts for shop floor devices. Other weeks emphasize backend efforts, including implementing event-sourced entity patterns or integrating with third-party APIs. The exact mix varies by sprint and project needs.