About the job
We are looking for a Data Engineer to design, build, and operate the data pipelines, models, and platform infrastructure that power Ring's analytics, science, and AI initiatives. You will own the end-to-end data lifecycle — ingestion, transformation, modeling, quality enforcement, and delivery — ensuring that analysts, scientists, and AI systems have access to reliable, well-structured data at scale.
You will use AI development IDEs and generative AI tooling daily to accelerate your work, and you will build multi-agent solutions that automate common data engineering tasks — pipeline generation, data quality enforcement, testing, and operational response. The goal is to turn repeatable patterns into agent-driven workflows that raise velocity and consistency across the team.
You will also contribute to the shared data platform when needed — improving developer tooling, maintaining infrastructure, and supporting the services that the broader data org depends on.
Responsibilities
- Design, build, and operate data pipelines, models, and platform infrastructure powering analytics, science, and AI initiatives
- Own the end-to-end data lifecycle including ingestion, transformation, modeling, quality enforcement, and delivery
- Use AI development IDEs and generative AI tooling daily to accelerate work
- Build multi-agent solutions that automate common data engineering tasks such as pipeline generation, data quality enforcement, testing, and operational response
- Contribute to the shared data platform by improving developer tooling, maintaining infrastructure, and supporting services
Qualifications
Minimum
- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
- Demonstrated use of generative AI tools (e.g., agentic coding assistants, AI-powered IDEs) in a professional or project setting
Preferred
- 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)