Data Engineer: AI-Driven Pipelines & Multi-Agent Platform

Amazon; Madrid; On-site; EUR 70,000 - 110,000; Full time

Job summary

Amazon Spain Services, S.L.U. is seeking a Data Engineer to design, build, and operate data pipelines, models, and platform infrastructure powering Ring's analytics, science, and AI initiatives.

You will own the end-to-end data lifecycle—from ingestion to delivery—ensuring analysts, scientists, and AI systems access reliable, well-structured data at scale, and you will use AI development IDEs and generative AI tooling to accelerate tasks and build multi-agent solutions that automate common data

Qualifications

• 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

Responsibilities

• Design, build, and operate data pipelines, models, and platform infrastructure for analytics initiatives
• Own end-to-end data lifecycle from ingestion to delivery for reliable, well-structured data
• Contribute to shared data platform, tooling, and developer experience
• Leverage AI development IDEs and generative AI tooling to accelerate data engineering tasks

Skills

Python

SQL

Data pipelines

Tools

Spark

Airflow

dbt

Job description

Amazon Spain Services, S.L.U. is seeking a Data Engineer to design, build, and operate data pipelines, models, and platform infrastructure powering Ring's analytics, science, and AI initiatives.

You will own the end-to-end data lifecycle—from ingestion to delivery—ensuring analysts, scientists, and AI systems access reliable, well-structured data at scale, and you will use AI development IDEs and generative AI tooling to accelerate tasks and build multi-agent solutions that automate common data