Top Data Engineer Jobs in Spain

Design, develop, and maintain batch and real-time data pipelines and highly available distributed data systems. Process large datasets, apply data modeling and testing practices, monitor production systems, troubleshoot issues, and optimize performance and cost. Lead code reviews, make technical architecture decisions, collaborate across teams, and mentor junior engineers while improving engineering processes and operational practices.

Build and maintain reliable data pipelines, models, and semantic layers for revenue analytics or product insights. Establish trusted KPI sources, reconcile data across systems, improve data quality, investigate anomalies, and support executive reporting and forecasting. Collaborate with finance, sales operations, product, customer success, analysts, and architects to translate business needs into scalable technical solutions. Participate in occasional planned month-end pipeline monitoring.

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.

Design, develop, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows that automate validation, troubleshooting, issue identification, and operational processes. Develop reusable data engineering frameworks, implement quality controls and monitoring, optimize workloads, support productionization, and troubleshoot complex data issues while collaborating with business and platform teams.

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

Execute large-scale data platform migrations to Snowflake, adapting pipelines and migrating approximately 1,000 datasets. Develop, test, schedule, and monitor Airflow workflows while supporting migrations from Airflow 2 to 3, Confluent regions, and existing storage formats to Iceberg. Ensure data quality, reliability, performance, and cost optimization throughout modernization efforts using Python, SQL, Spark, Kafka, AWS S3, and related data engineering technologies.

Execute large-scale data platform migrations to Snowflake, adapting pipelines and ensuring data quality and reliability. Develop, test, monitor, and debug Airflow workflows using Python, while supporting migrations involving Airflow, Kafka, and Iceberg. Work with the data platform team to migrate approximately 1,000 datasets and modernize data infrastructure through March 2027.

Design, develop, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows and reusable data engineering frameworks, implement data quality and monitoring controls, optimize performance, support productionization, and troubleshoot complex data issues.

Leads the design and delivery of knowledge graph, semantic, and AI solutions for enterprise clients. The role includes ontology and schema design, graph-based machine learning, AI data pipelines, data architecture, client consulting, business-case development, thought leadership, and team or workstream leadership. Candidates will work across technical, research, product, and client teams to develop scalable solutions using graph, vector, cloud, NLP, and LLM technologies.

Design, build, and maintain scalable data infrastructure and ETL pipelines. Develop reliable data systems using relational, NoSQL, vector, and graph databases; optimize data retrieval and ensure data quality, integrity, governance, and accessibility. Collaborate with data scientists and analysts to translate requirements into effective architectures, leveraging Python, SQL, big data technologies, and cloud services, preferably AWS. Support emerging use cases including similarity search, machine learning, and complex relationship modeling.

Develop and maintain business intelligence, data warehouse, ETL/ELT, OLAP, and reporting solutions. Design and optimize SQL Server databases using T-SQL, stored procedures, views, and indexes. Integrate data from multiple sources, resolve data quality and performance issues, create SSRS and Tableau dashboards, and manage deployments across development, QA, integration, and production environments.

Lead the strategy, architecture, and engineering of AWS-based data and model platforms supporting credit risk and capital provisioning. Build scalable data pipelines, model deployment and orchestration frameworks, monitoring, governance, lineage, and auditability. Drive legacy platform migration, productionize ML workloads, ensure regulatory controls and data integrity, influence cross-team technical decisions, improve engineering standards, and mentor engineers.

Build and maintain scalable BigQuery data models and ETL/ELT pipelines within Google Cloud. Clean and migrate legacy business-unit data, establish governance and lineage, and optimize datasets for AI and machine learning consumption. Collaborate with business-unit leaders and the AI Office to translate business logic into technical requirements and ensure reliable, high-quality data foundations.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Lead hands-on engineering for AstraZeneca’s APD Platform, designing and operating scalable data pipelines, data products, AI applications, and machine learning capabilities. Responsibilities include data integration, governance, quality, lineage, MLOps, generative AI, DevOps, cloud engineering, architecture, technical standards, mentoring, and stakeholder collaboration. The role supports secure, reliable, and reusable capabilities for pharmaceutical R&D while providing technical leadership across multidisciplinary teams.

Lead the design, development, deployment, and operation of scalable data and AI capabilities for AstraZeneca’s APD Platform. Build data pipelines, data products, analytical services, ML solutions, and generative AI applications while ensuring quality, security, governance, and regulatory compliance. Provide technical leadership, establish engineering standards, mentor engineers, guide architecture, and collaborate with scientists, product managers, architects, vendors, and business stakeholders.

Designs, builds, and optimizes scalable data systems, ETL pipelines, data warehouses, and data quality solutions. Integrates data from internal and external sources, develops internal tools, supports real-time processing, and participates throughout the software development lifecycle. Collaborates with Product, Operations, and Marketing teams while contributing to technology evolution and actionable insights.

Design, implement, deploy, and maintain enterprise data integration pipelines across business, engineering, machine, drone, and external data sources. Responsibilities include data quality checks, API and microservices migration, platform performance analysis, UAT, deployment planning, data modeling, documentation, and support for AI interoperability initiatives. The role also provides technical architecture guidance and ensures service-level performance across integration platforms.

Twelve-month hybrid internship supporting data engineering and generative AI initiatives. Responsibilities include preparing structured and unstructured data, developing RAG and embedding pipelines, data exploration, database modeling, integration, vector processing, governance, privacy, cataloging, and data quality. The intern will collaborate with data scientists, AI/ML engineers, and business stakeholders while gaining experience with enterprise analytics platforms and Agile or Waterfall delivery.

Build governed, reusable AI-ready data products and pipelines across cloud and on-premises environments. Establish data quality, lineage, metadata, access controls, and semantic definitions. Design retrieval foundations using structured and unstructured data, embeddings, vector search, graph approaches, and semantic enrichment. Apply AI-assisted metadata generation, entity resolution, and content classification while partnering with data owners, business stakeholders, and technical teams.

Build and optimize large-scale data pipelines, cross-game data marts, analytics tools, and AI-powered workflows. The role involves advanced SQL and Python development, production ETL maintenance, collaboration with game and marketing analysts, and educating stakeholders on software engineering best practices.

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and operate production-grade data pipelines, curated datasets, data marts, and BI-ready data models. Develop Python ETL/ELT and near-real-time AWS DMS ingestion, manage lakehouse schemas and incremental processing, and implement data quality and observability controls. Orchestrate workflows with Airflow and AWS services while contributing to CI/CD, infrastructure as code, architecture, and engineering best practices.

Build and maintain data infrastructure and pipelines for a globally deployed Customer Data Platform. The role covers CDP development and operations, data quality, incident management, test automation, stakeholder collaboration, requirements negotiation, and platform stability in a DevOps environment.