Data Engineering Manager, Ring Agent Platforms
Role details
Job location
Tech stack
Job description
We are looking for a Manager of Data Engineering to lead a team of data engineers building and operating the data pipelines, models, and platform infrastructure that power Ring's analytics, science, and AI initiatives. You will own the delivery and operational health of the data platform, build and mentor a high-performing team, and drive the adoption of AI-assisted engineering practices across the group.
Your team will use AI development IDEs and generative AI tooling daily, and will build multi-agent solutions that automate common data engineering tasks - pipeline generation, data quality enforcement, testing, and operational response. You will guide this evolution, helping your engineers develop fluency with agentic tooling while maintaining the data engineering fundamentals that everything depends on.
You will also partner with business intelligence, applied science, and product teams to translate data needs into technical roadmaps, and contribute to shared platform infrastructure when the work calls for it.
About the team
The Data and Agents Organization spans data engineering, business intelligence, applied science, and agentic AI. The org is structured into three primary groups: one focused on core data platforms, tooling, and pipeline infrastructure; another focused on AI/ML models, business analytics, shared data models, product analytics, and strategic science initiatives; and a third focused on building a multi-agent AI platform that enables teams to compose, deploy, and orchestrate autonomous AI agents at scale. Capacity is balanced across direct business support, strategic new development, and operational health.
Requirements
- Experience in engineering team management
- Experience working directly within data engineering or closely related teams, with hands-on contribution to data platform and pipeline delivery
- Experience designing or architecting data systems, including data modeling, pipeline patterns, reliability, and scaling strategies
- Experience building or leading development of data pipelines and cloud-native data infrastructure (e.g., data warehouses, data lakes, event-driven architectures, orchestration platforms)
- Knowledge of engineering practices across the full software development life cycle, including coding standards, code reviews, source control, CI/CD, testing, and operational excellence
- Experience partnering with product management, applied science, or cross-functional stakeholders to translate business needs into technical roadmaps, * Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with AWS Tools and Technologies (Redshift, S3, EC2)
- Experience leading teams that use generative AI tools and AI development IDEs to accelerate engineering work
- Familiarity with multi-agent solutions that automate data engineering workflows (pipeline generation, data quality, testing, operational response)
- Familiarity with at least one agentic AI development IDE
- Experience building or overseeing shared data models, semantic layers, or data contracts
- Familiarity with data governance, cataloging, or lineage tracking practices
- Experience contributing to or overseeing shared platform infrastructure, developer tooling, or self-service data services
- Familiarity with observability tooling for data pipelines and data platform operations
