Data Engineer II, Ring NA Sales and Marketing

Key job responsibilities

• 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.

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