Data Engineer , 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 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.
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. - Non-internship professional experience in data engineering or a closely related discipline
- Experience building and operating data pipelines (batch and/or streaming) using frameworks such as Spark, Airflow, dbt, or equivalent
- Proficiency in Python and SQL
- Experience with cloud-native data services including data warehouses, object storage, event streaming, and serverless compute
- Familiarity with data modeling and data quality practices
- Experience with software development life cycle practices including code reviews, source control, CI/CD, testing, and operational support
- Demonstrated use of generative AI tools (e.g., agentic coding assistants, AI-powered IDEs) in a professional or project setting
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.
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. - Non-internship professional experience in data engineering or a closely related discipline
- Experience building and operating data pipelines (batch and/or streaming) using frameworks such as Spark, Airflow, dbt, or equivalent
- Proficiency in Python and SQL
- Experience with cloud-native data services including data warehouses, object storage, event streaming, and serverless compute
- Familiarity with data modeling and data quality practices
- Experience with software development life cycle practices including code reviews, source control, CI/CD, testing, and operational support
- Demonstrated use of generative AI tools (e.g., agentic coding assistants, AI-powered IDEs) in a professional or project setting
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