Data Engineer II – AI-Driven Cloud Pipelines

Amazon.com Services LLC; Bellevue (WA); On-site; USD 132,000 - 179,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon.com Services LLC is seeking a Data Engineer II to design, build, and operate production SQL data pipelines within a cloud data warehouse, enabling AI-driven analytics at enterprise scale. This onsite role is based in Bellevue, WA.

You will own end-to-end data lineage, model schemas, and deliver features across backend services and tools, while ensuring reliability, observability, and security. The team collaborates with stakeholders to translate domain knowledge into machine-readable

Qualifications

• Minimum of 3 years of data engineering experience.
• Bachelor’s degree or higher in a related field or equivalent professional experience.
• Proficiency with SQL and data warehousing concepts.

Responsibilities

• Design, implement, and manage production SQL data pipelines in a cloud data warehouse with reliability and observability.
• Model entity schemas, define metrics, and own end-to-end data lineage from raw sources to consumption.
• Deliver features across backend services and frontend interfaces for team tools.
• Collaborate with stakeholders to translate business context into machine-readable definitions.

Skills

Data engineering

SQL

Python

Java

ETL/ELT

Data modeling

Cloud data warehouse

Education

Bachelor's degree or higher

Tools

Redshift

Oracle

NoSQL

S3

AWS Glue

EMR

Kinesis

Firehose

Lambda

IAM

Job description

Amazon.com Services LLC is seeking a Data Engineer II to design, build, and operate production SQL data pipelines within a cloud data warehouse, enabling AI-driven analytics at enterprise scale. This onsite role is based in Bellevue, WA.

You will own end-to-end data lineage, model schemas, and deliver features across backend services and tools, while ensuring reliability, observability, and security. The team collaborates with stakeholders to translate domain knowledge into machine-readable