Data Engineer II: Build Context-Driven Pipelines

Amazon Inc.; Seattle (WA); On-site; USD 132,000 - 179,000; Full time

Job summary

Amazon.com Services LLC in Seattle seeks a Data Engineer to build and operate scalable, context-driven data pipelines that track the infrastructure delivery lifecycle from intake through deployment to decommission, fueling analytics and ML use cases.

You will partner with Data Engineering, Product, and Software teams to design robust data models, ensure data quality and governance, and deliver self-service datasets and tools for business teams to answer their own questions.

Qualifications

• 4+ years of data engineering experience.
• Bachelor's degree or foreign equivalent in CS/Engineering/IS/Math or a related field.
• Experience with data modeling, warehousing and building ETL pipelines.
• Experience in Python, Java, Scala, or NodeJS.
• Experience with SQL.
• Experience delivering end-to-end projects independently.

Responsibilities

• Design, build, and operate scalable, production-grade data pipelines and their supporting infrastructure.
• Create context-rich source-of-truth datasets and dimensional data models for the delivery lifecycle.
• Collaborate with Data Engineering, Product, and Software teams to enable self-serve analytics.
• Define data quality, governance, and monitoring standards across pipelines.
• Troubleshoot data quality issues and optimize downstream data consumers.

Skills

Data modeling

Data warehousing

ETL pipelines

Python

Java

Scala

Node.js

SQL

End-to-end delivery

Education

Bachelor's degree in Computer Science / Engineering / Information Systems / Mathematics or related field

Tools

Redshift

S3

AWS Glue

EMR

Kinesis

Firehose

Lambda

IAM

Kafka

Flink

Storm

Beam

Job description

Amazon.com Services LLC in Seattle seeks a Data Engineer to build and operate scalable, context-driven data pipelines that track the infrastructure delivery lifecycle from intake through deployment to decommission, fueling analytics and ML use cases.

You will partner with Data Engineering, Product, and Software teams to design robust data models, ensure data quality and governance, and deliver self-service datasets and tools for business teams to answer their own questions.