Top Data Engineer Jobs in Spain

Build and maintain cloud-based media data pipelines and user-facing data products. Design, deploy and govern BigQuery/DBT pipelines, APIs and dashboards, ensure data quality/security, manage vendors, and drive product-oriented delivery and adoption.

The Data Engineer II will develop and optimize data platforms, build scalable pipelines, enhance existing workflows, and collaborate with teams for data solutions.

Design, build, and operate AWS-based, event-driven, S3-backed data pipelines and reusable components. Ensure data quality, metadata, lineage, governance, secure handling, and enable analytics/BI. Use Prefect orchestration, Python and SQL, containerization, CI/CD and monitoring; collaborate with architects and product teams to translate requirements into scalable, production-grade data solutions.

Design, build, and maintain high-scale distributed backend services and data pipelines for asset discovery, recognition, and enrichment. Integrate AI/ML into workflows, solve concurrency and fault-tolerance problems, participate in architecture and code reviews, mentor engineers, and use AI-assisted development tools in daily engineering practice.

The Senior Data Engineer designs and implements data infrastructure, oversees data pipelines, and ensures data reliability while mentoring junior staff. Responsibilities include automating governance, optimizing data performance, and collaborating with various stakeholders.

The Data Engineer will design, build, and maintain data pipelines while collaborating with teams to provide data-driven solutions and enhance data quality.

Build and optimize data ingestion and transformation pipelines using S3 (parquet, Iceberg), Airbyte, Airflow, and cloud platforms. Create and maintain data models in Snowflake/Looker/Cube/dbt, write efficient SQL, apply software engineering practices (version control, CI/CD, testing), and collaborate with product, visualization, and data science teams to deliver analysis-ready data.

Entry-level Data Engineer internship supporting database management, data integration, quality, and maintenance. Provide reliable, structured data to analysts, resolve incidents, and support database structuring and operations.

As a Senior Software Engineer, you will lead backend development for the Merchant Data Platform, mentor junior engineers, ensure system reliability, and collaborate in product development. Your role involves project management, technical planning, and maintaining high availability of services.

Lead technical direction and hands-on implementation of semantic infrastructure for telemetry interoperability. Build scalable systems bridging OpenTelemetry, cloud and customer data models; define standards, mentor engineers, run on-call rotations, and drive cross-team platform initiatives for schema evolution and AI-enabled observability features.

Lead design and delivery of cloud-native data platforms and pipelines for regulatory reporting. Build reusable data assets, integrate diverse sources, enforce coding standards, mentor engineers, and ensure scalable, high-performance, reliable data solutions on AWS/Snowflake.

Design, build, and maintain ETL/ELT pipelines, model and optimize NoSQL data (MongoDB), create Metabase dashboards, prepare datasets for AI/LLM projects, support web back-end (Node.js), and collaborate with product teams to integrate AI components and ensure data quality and freshness.

Design, build, automate, and operate the company's cloud-native data infrastructure (ingest, processing, storage, serving). Provision scalable, resilient platforms (self-service), introduce new data stores, own DR and cross-region replication, monitor availability and performance (Datadog), and handle incidents and on-call duties while collaborating with product, architecture, and security teams.

Build, maintain, and optimize ETL pipelines and analytical data warehouse environments. Deploy, monitor, and scale machine learning models in production; automate model deployment and recurring data processes. Collaborate with Data Science, Engineering, BI, Risk, and IT teams; troubleshoot performance issues and maintain technical documentation.

Design, operate, and ensure reliability of data and AI infrastructure: define SLIs/SLOs, build self-healing systems, implement IaC with Terraform, automate CI/CD, enable streaming real-time feeds, and provide secure feature-store and API integrations for AI agents.

As a Data Engineer, you will model data using dbt, manage data infrastructures, design data pipelines, and deliver training and presentations to clients.

Embed with customers to design, prototype, deploy, and optimize production AI solutions. Own delivery workstreams, build RAG and agentic systems, ensure scalability, reliability, and knowledge capture, and contribute to platform improvements.

The Data Engineer will design data pipelines and architecture, optimize machine learning models, and collaborate on data projects for international brands.

The Data Engineer will design and optimize data pipelines for AML solutions, support data scientists, and manage technical relationships with customers.

Design, develop, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources with SQL Server/SSIS, optimize large-scale processing for performance and scalability, and collaborate with BI teams to support reporting and analytics.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory; integrate and optimize data from multiple sources, support SQL Server/SSIS environments, and collaborate with BI teams for reporting and analytics.

Design, develop, and maintain Azure Databricks-based data pipelines and Lakehouse architectures using PySpark, Spark SQL, Delta Lake, and Azure Data Factory. Integrate diverse sources into SQL Server-based warehouses, ensure performance and data quality, and support BI/reporting (Power BI) and cloud migration efforts.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize large-scale Spark jobs, support SQL Server/SSIS platforms, and collaborate with BI teams to enable analytics and reporting.

Design, develop, and maintain Azure Databricks data pipelines and Lakehouse architectures using PySpark, Spark SQL, Delta Lake, and Azure Data Factory. Integrate data from multiple sources into SQL Server-based warehouses, ensure scalability, performance, and data quality, support BI reporting, and evolve cloud-based data platform architectures.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate and transform large datasets, optimize performance, support SQL Server/SSIS environments, and collaborate with BI teams to enable analytics and reporting.