Top Data Engineer Jobs in Seville

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.

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.

The Senior Data Engineer will lead the design and evolution of scalable data architectures, mentor team members, build data pipelines, and integrate Generative AI into workflows.

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.

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.

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.

Design, implement, and operate cloud-based data infrastructure focusing on scalability and security, and collaborate with teams on data pipelines and models.

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

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 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.

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.

The Senior Data Engineer will build and optimize data pipelines, collaborate with engineering teams, and ensure data quality and scalability.

As a Senior Data Engineer, you will deliver complex projects, manage customer relationships, and mentor junior engineers while using Databricks and various cloud platforms.

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.

Design and build AI-driven data products and investment platforms, developing ETL pipelines, ensuring data quality, and integrating structured and unstructured data.

Design and maintain data pipelines, collaborate with stakeholders, ensure data quality, deploy infrastructure as code, and optimize reporting solutions.

Design and build reliable, performant data pipelines for large payment datasets. Own end-to-end data flows, drive data quality, implement transformations (dbt), operate on StarRocks and Flink/Spark in cloud (AWS/GCP), collaborate cross-functionally, establish engineering standards, mentor engineers, and shape CI/CD and incident practices.

Design and develop features for the data platform, focusing on data ingestion and processing. Architect scalable data pipelines, mentor team members, and handle operational issues.

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

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