Hybrid Data Engineer with DevOps and Cloud experience is sought to design and build data pipelines, manage databases across relational, columnar, document, graph, and vector models, and deploy machine learning solutions. The role mainly uses Google Cloud, with valued experience in AWS, Azure, and Databricks. Responsibilities include SQL-based data processing and analysis, using dbt for transformation and modeling, and ensuring data quality. You will develop data pipelines primarily in Python, with additional experience in Scala, R, and Java. Familiarity with functional and object-oriented programming and API-first architectures is required. Experience with ETL/ELT tools for real-time and batch processing, and deploying ML models for production, AI-powered solutions, orchestration frameworks like LangGraph, and observability platforms like Langfuse. Knowledge of DevOps practices and AI-assisted development using Terraform and GitHub (Actions and Copilot) is needed, as well as understanding digital product development principles and validated learning for continuous improvement. Experience with Agile methodologies and modern software engineering practices (e.g., Agile and Shape Up) in multidisciplinary, self-managing, multicultural teams is preferred. Minimum five years of experience as a Data Engineer or Backend Software Engineer. Madrid, Spain; hybrid work arrangement, with one day per week in Madrid.

Hybrid Data Engineer with DevOps and Cloud experience is sought to design and build data pipelines, manage databases across relational, columnar, document, graph, and vector models, and deploy machine learning solutions. The role mainly uses Google Cloud, with valued experience in AWS, Azure, and Databricks. Responsibilities include SQL-based data processing and analysis, using dbt for transformation and modeling, and ensuring data quality. You will develop data pipelines primarily in Python, with additional experience in Scala, R, and Java. Familiarity with functional and object-oriented programming and API-first architectures is required. Experience with ETL/ELT tools for real-time and batch processing, and deploying ML models for production, AI-powered solutions, orchestration frameworks like LangGraph, and observability platforms like Langfuse. Knowledge of DevOps practices and AI-assisted development using Terraform and GitHub (Actions and Copilot) is needed, as well as understanding digital product development principles and validated learning for continuous improvement. Experience with Agile methodologies and modern software engineering practices (e.g., Agile and Shape Up) in multidisciplinary, self-managing, multicultural teams is preferred. Minimum five years of experience as a Data Engineer or Backend Software Engineer. Madrid, Spain; hybrid work arrangement, with one day per week in Madrid.