Ring is seeking a Data Engineer with strong analytical, communication, and project management skills to join our team. This role will work closely with business intelligence engineers, data scientists, and business stakeholders across various verticals. You will design, evangelize, and implement state-of-the-art solutions that help us provide a great customer experience. You will work with a complicated data environment, employ the right architecture to handle data, and support various analytics use cases including business reporting, production data pipelines, machine learning, and optimization models. This role will be expected to utilize GenAI to build reliable, scalable data pipelines, which produce data optimized for our customers, who will also be utilizing AI Agent pipelines.

Key job responsibilities

In this role, you will:

- Design, build, and maintain reliable, scalable ETL/ELT pipelines that deliver data to RBKS sales and marketing teams, owning data-readiness SLAs and CloudWatch-based monitoring and observability.

- Apply the right architecture for a complex, multi-channel data environment which supports the sell-through pipeline that serves as the single source of truth for Ring & Blink unit sales across 450+ datasets.

- Leverage GenAI to accelerate development and produce data optimized for both analysts and downstream AI Agent pipelines.

- Build direct API integrations for new retailers (Costco, Best Buy, Home Depot) using reusable frameworks that enable rapid onboarding without sacrificing reliability.

- Contribute to large-scale migrations from the Ring Data Warehouse to native AWS and from Tableau to QuickSight and AI-driven reporting.

- Identify and resolve data quality issues and performance bottlenecks in core pipelines without disrupting daily operations.

- Enable diverse analytics use cases - business reporting, demand planning, machine learning, and optimization models - by producing clean, query-ready data.

- Partner with business intelligence engineers, data scientists, and stakeholders across verticals, evangelizing platform standards and best practices.

A day in the life

A typical day blends building, collaborating, and maintaining. You monitor pipeline health and data-readiness SLAs across our GTM platforms, triaging issues before stakeholders are impacted. You partner with BIEs and data scientists to refine sell-through data models, scope new retailer API integrations, and advance migration efforts like Clara and Horizon. You leverage GenAI to accelerate pipeline development and harden the data feeding AtlasKB and our Bedrock agents. You participate in code reviews by authoring changes and reviewing teammates' work to uphold quality and platform standards. No two days look the same, but each one moves a strategic platform forward.

About the team

The RBKS Sales & Marketing Analytics team is the single source of truth for Ring and Blink go-to-market data, including sell-through, forecast, attainment, and inventory. We are a data engineering organization that owns the entire data lifecycle, from production ETL and Redshift cluster and AWS infrastructure to full-stack applications and CI/CD. We operate an extensive data platform along with AtlasKB, our company-wide AI-ready knowledge base. We also lead the GTM organization's AI strategy, translating enterprise ambition into scalable, production-grade execution. Our work directly powers planning and executive decision-making across the Ring and Blink business.

BASIC QUALIFICATIONS

- 5+ years of data engineering experience

- Experience with data modeling, warehousing and building ETL pipelines

- Experience with SQL

PREFERRED QUALIFICATIONS

- 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 in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS

- Experience working in or building GenAI enhanced data pipelines