Top Data Engineer Jobs in Madrid

The Senior Data Engineer will redesign and simplify the data warehouse infrastructure, improve system stability, optimize data pipelines, and oversee infrastructure performance. Responsibilities include conducting audits, making architectural proposals, and supporting ML/AI workflows.

As a Senior Data Engineer, you'll design scalable data pipelines, collaborate with engineers and product teams, and evolve the architecture to support growth in users and data.

The role involves developing and supporting data processing pipelines, collaborating with ML Engineers, and improving infrastructure while mentoring team members.

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.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

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

As a Senior Backend Engineer, you'll develop backend services and APIs, collaborate with cross-functional teams, and ensure code quality and performance in a high-growth environment.

As a Backend Engineer (Data Engineering), design and build scalable data pipelines and infrastructure for processing security data, ensuring reliability and performance.

Support the migration of legacy SQL Server data warehouse to Snowflake, integrating data sources, refactoring ETL processes, and ensuring reliable data movement for analytics.

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

As a Senior Data Engineer, you will architect scalable infrastructure, ensure data governance, mentor engineers, and resolve complex data issues.

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