Data Engineer, Ring Agent Platforms

Amazon Amazon · Big Tech · Hawthorne, CA · Data Science

Data Engineer role focused on building and operating data pipelines, models, and platform infrastructure for Ring's analytics, science, and AI initiatives. The role involves designing end-to-end data lifecycles and building multi-agent solutions to automate data engineering tasks. It also includes contributing to the shared data platform and AI/ML models.

What you'd actually do

  1. design, build, and operate the data pipelines, models, and platform infrastructure that power Ring's analytics, science, and AI initiatives
  2. own the end-to-end data lifecycle — ingestion, transformation, modeling, quality enforcement, and delivery
  3. use AI development IDEs and generative AI tooling daily to accelerate your work
  4. build multi-agent solutions that automate common data engineering tasks — pipeline generation, data quality enforcement, testing, and operational response
  5. contribute to the shared data platform when needed — improving developer tooling, maintaining infrastructure, and supporting the services that the broader data org depends on

Skills

Required

  • 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) in a professional or project setting

Nice to have

  • Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
  • Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
  • Experience designing or building AI agents or multi-agent solutions that automate engineering workflows
  • Familiarity with agentic AI patterns including tool use, function calling, and multi-agent orchestration
  • Familiarity with at least one agentic AI development IDE
  • Experience building or maintaining shared data models, semantic layers, or data contracts
  • Familiarity with data governance, cataloging, or lineage tracking
  • Experience contributing to shared platform infrastructure, developer tooling, or self-service data services
  • Familiarity with observability tooling for data pipelines (logging, metrics, alerting)

What the JD emphasized

  • Experience designing or building AI agents or multi-agent solutions that automate engineering workflows
  • Familiarity with agentic AI patterns including tool use, function calling, and multi-agent orchestration
  • Demonstrated use of generative AI tools (e.g., agentic coding assistants, AI-powered IDEs) in a professional or project setting

Other signals

  • building multi-agent solutions
  • automating data engineering tasks
  • generative AI tooling