Data Engineering Manager, Ring Agent Platforms
Amazon (AI roles) · Madrid, Community of Madrid, ESP
No salary listed. Estimated from 1377 salary-disclosed Engineering roles on this board: $200K–$300K (interquartile range; estimate, not the employer's figure).
Apply at Amazon (AI roles)Find your warm intro on LinkedInAbout the role
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 Agent Platform 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. - Engineering team management experience, including hiring, performance management, and career development
- Working directly within data engineering or closely related teams, with hands-on contribution to data platform and pipeline delivery
- 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
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 Agent Platform 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. - Engineering team management experience, including hiring, performance management, and career development
- Working directly within data engineering or closely related teams, with hands-on contribution to data platform and pipeline delivery
- 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
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