Data Engineer: AI Agents Platform & Pipelines

Amazon; Hawthorne (CA); On-site; USD 132,100 - 178,800; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

RSUs or equity

Comprehensive benefits

Job summary

Amazon is seeking a Data Engineer to design, build, and operate data pipelines, models, and platform infrastructure powering Ring's analytics, science, and AI initiatives. You will own the end-to-end data lifecycle—ingestion, transformation, modeling, quality enforcement, and delivery—ensuring analysts and AI systems access to reliable data at scale.

You will use AI development IDEs and generative AI tooling daily to accelerate your work and build multi-agent solutions that automate common data

Qualifications

• 3+ years of data engineering experience.
• Experience with data modeling, warehousing and building ETL pipelines.
• Experience with SQL.
• Experience with software development life cycle practices including code reviews, source control, CI/CD, testing, and operational support.
• Demonstrated use of generative AI tools (e.g., agentic coding assistants, AI-powered IDEs).

Responsibilities

• Design, build, and operate data pipelines, models, and platform infrastructure powering analytics, science, and AI initiatives.
• Own end-to-end data lifecycle: ingestion, transformation, modeling, quality enforcement, and delivery.
• Contribute to the shared data platform by improving tooling, infrastructure, and self-service data services.
• Use AI development IDEs and multi-agent patterns to automate data engineering tasks and raise velocity.

Skills

Data engineering

Data modeling

SQL

SDLC practices

Generative AI tools

Tools

AWS Redshift

AWS S3

AWS Glue

AWS EMR

AWS Kinesis

AWS Firehose

AWS Lambda

IAM roles

Job description

Amazon is seeking a Data Engineer to design, build, and operate data pipelines, models, and platform infrastructure powering Ring's analytics, science, and AI initiatives. You will own the end-to-end data lifecycle—ingestion, transformation, modeling, quality enforcement, and delivery—ensuring analysts and AI systems access to reliable data at scale.

You will use AI development IDEs and generative AI tooling daily to accelerate your work and build multi-agent solutions that automate common data