As a Data Engineer II with Amazon Manufacturing Services, you will shape the data landscape that powers shop floor insights from Bellevue, onsite. You’ll design and run data pipelines, maintain both the data warehouse and data lake, and build dashboards and ML workflows that illuminate operational performance. Collaboration with senior SDEs on architecture and integration patterns will be a core part of owning a portion of the data domain.
Compensation and Location
Location: Bellevue, WA (onsite). Salary: USD 132,100 - 178,800 per year.
Responsibilities
- Design and manage data pipelines using AWS Glue (PySpark), Kinesis, S3, and EventBridge to ingest DynamoDB streams and other enterprise data into the AMS data lake
- Model and sustain the Redshift warehouse and the S3/Athena data lake that power 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 ML 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 significant ownership over your data domain
Requirements
- At least three years of data engineering experience
- A minimum of one year developing and operating large-scale BI data structures using ETL/ELT processes
- At least one year of data modeling experience for BI data structures
- At least one year of SQL experience in BI data structures
- Experience with data modeling, data warehousing, and building ETL pipelines
- Experience with AWS technologies including Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles/permissions
- Proficiency in at least one modern scripting or programming language such as 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
Your day begins with a standup alongside SDEs, fellow data engineers, and manufacturing stakeholders. You might resume work on a React component that displays real-time resource status for shop floor planners, then pivot 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 arrives, and you review how AMS connects to external manufacturing platforms.
Some weeks skew toward frontend work—building interactive visualizations or responsive layouts for shop floor devices. Others favor backend work—implementing event-sourced entity patterns or integrating with third-party APIs. The mix shifts with each sprint and aligns with your strengths.
