Top Data Engineer Jobs in Madrid

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

The Staff Data Engineer will design and maintain data models, develop ELT pipelines, collaborate with teams, and enable data accessibility for informed decision-making across the business.

The Senior Software Engineer will enhance database infrastructure tools, ensure system reliability, support developer experiences, and engage in on-call shifts.

Design, build, and maintain a trusted semantic layer and reusable data models (dimensions, facts, metrics). Translate business logic into clear data specs, improve model reliability with testing and lineage, drive adoption across teams, standardize modelling conventions, and enable self-serve analytics for product, GTM, and finance stakeholders.

As a Data Analytics Engineer, you'll design and maintain ETL pipelines, ensure data quality, and collaborate with various teams to meet data needs.

The Agricultural Engineer will develop ETL processes, automate quality control, validate databases, and coordinate technical needs between teams.

As a Senior Data Engineer, you'll design ETL workflows, maintain cloud data platforms, ensure data quality, and leverage AI in data operations. You'll collaborate with various teams to support data needs and optimize data architecture.

Design, build, and maintain data pipelines and warehouses; deploy Data Science models to production; automate data collection, monitoring, and model retraining; collaborate with analysts and engineers to ensure data quality and availability.

The Senior Data Engineer will evolve data infrastructure, manage data governance, and ensure reliability while collaborating with ML and analytics teams to provide well-governed datasets.

Seeking a skilled Data Engineer to design, develop, and deploy ML solutions with expertise in Python, Databricks, Snowflake, and AWS, focusing on supply chain management.

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

As a Senior Data Platform Engineer, you will lead complex data engineering projects, design core data platforms, mentor engineers, and develop automation capabilities for data workflows.

You will design and develop data solutions, create ETLs using Spark, work on cloud projects, and build scalable data pipelines.

The Senior Data Engineer will design and maintain scalable ETL/ELT pipelines, model analytics-ready data, and collaborate with teams to enhance data reliability and observability.

Design, build, and maintain backend services and automation for data pipelines and internal tooling. Integrate AI/ML features, own CI/CD and observability, lead code reviews, and collaborate with cross-functional teams to ensure scalable, reliable production systems.

The Data Engineer will focus on infrastructure automation, reliability, and cost optimization. Responsibilities include designing Infrastructure-as-Code, managing CI/CD pipelines, establishing observability standards, and collaborating with teams to improve platform resilience.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

The Senior Data Engineer will design, build, and maintain data pipelines, ensuring data quality, governance, and performance optimization while collaborating with cross-functional teams.

As a Senior Data Engineer at Fever, you'll design and deliver data products, build ETL workflows, and collaborate with cross-functional teams to ensure data accuracy and meet business needs.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.