Data Engineer, AWS Data Platform & ETL Pipelines

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

Job summary

Amazon Web Services (AWS) is seeking a Data Engineer for the Specialist Technology Team (STT) in Seattle to design, build, and operate scalable data pipelines that power agentic analytics across the STT product portfolio.

You will be among the first data engineers on a greenfield team, shaping the data backbone, collaborating with engineers and scientists, and delivering self-service analytics for thousands of AWS field members.

Qualifications

• 3+ years of data engineering experience.
• 3+ years building and operating large-scale BI analytics data structures with ETL/ELT.
• 3+ years SQL experience for data analytics and reporting.
• 3+ years data modeling experience for analytics platforms.
• 3+ years in the job offered or a related occupation.

Responsibilities

• Design, build, and operate scalable ETL/ELT pipelines ingesting product telemetry and usage data.
• Architect a centralized data platform using AWS-native services as the single source of truth.
• Develop data models linking product usage signals to business outcomes.
• Build data infrastructure supporting AI/ML pipelines and agentic systems.
• Implement data quality frameworks with monitoring and validation at scale.
• Establish data contracts, lineage tracking, and catalog metadata across the organization.
• Operate with on-call responsibility and ensure data freshness.
• Contribute to evolution from static dashboards toward agentic data systems.

Skills

Data engineering

ETL/ELT pipelines

SQL

Data modeling

Experience in related occupation

Tools

Redshift

S3

AWS Glue

EMR

Kinesis

FireHose

Lambda

IAM roles

Job description

Amazon Web Services (AWS) is seeking a Data Engineer for the Specialist Technology Team (STT) in Seattle to design, build, and operate scalable data pipelines that power agentic analytics across the STT product portfolio.

You will be among the first data engineers on a greenfield team, shaping the data backbone, collaborating with engineers and scientists, and delivering self-service analytics for thousands of AWS field members.