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.

Develop and maintain cloud-based data platforms, scalable data pipelines, ETL workflows, anomaly detection, and optimized aggregations. Apply Data Mesh, Data Governance, and Data Lineage principles while supporting data needs across Product and Engineering. Collaborate with analysts, scientists, software engineers, and DevOps teams to build self-service data tools and modern data architecture.

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.

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

Lead design and delivery of shared observability data platform capabilities to reduce developer friction, improve operational visibility, enable cost intelligence, build self-service operational tooling, drive reusable services, mentor engineers, influence cross-team architecture, and participate in on-call to improve platform reliability.

Develop and maintain high-volume data processing pipelines using cloud and distributed-data technologies. Build jobs and libraries supporting data scientists and analysts, while implementing monitoring, alerting, quality checks, testing, and documentation. Contribute to data platform architecture and collaborate with cross-functional teams. The role requires expertise in SQL, data modeling, workflow orchestration, Python, ETL/ELT design, Spark, and AWS.

Develop high-volume distributed data processing jobs using Redshift, Athena, Iceberg, Spark, and Airflow. Collaborate with data scientists, analysts, and product managers to turn methodologies into products, improve system performance, and support data workflows. Mentor junior engineers on architecture and best practices. The role requires strong Python programming, SQL, database, data modeling, and modern lakehouse expertise, with AWS and streaming experience preferred.

Build and maintain high-volume data processing jobs (Redshift, Spark, Athena, Iceberg, Airflow). Collaborate with data scientists and analysts to productionize models, identify performance bottlenecks, and mentor junior engineers. Support data modeling, warehouse management, and troubleshooting.

Build and operate production ETL/ELT pipelines processing millions of daily data points. Design schemas, optimize SQL and databases, implement testing and observability, and support migration from Azure Databricks to an open-source AWS-based stack. Evaluate new tools through proofs of concept, use AI coding assistants responsibly, and collaborate with distributed engineering teams on reliable end-to-end data delivery.

Design, build, and operate customer-facing and internal data products, pipelines, and analytics solutions using Databricks, Spark, Python, and SQL. Develop frameworks for acquiring, processing, monitoring, and extracting value from large datasets. Contribute to data-platform architecture, data quality, observability, automation, testing, security, and CI/CD while collaborating with product owners and cross-functional stakeholders.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Integrate multiple data sources with SQL Server data warehouses, maintain analytical data models, optimize large-scale processing, ensure data quality and performance, and collaborate with BI teams supporting Power BI and reporting platforms. Participate in cloud data architecture implementations under senior guidance.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Integrate data from multiple sources with SQL Server data warehouses, maintain analytical data models, optimize large-scale processing, ensure data quality and performance, and support Power BI reporting. Collaborate with BI teams and contribute to cloud data architecture implementations under senior guidance.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, Azure Data Factory, SQL, and SQL Server. Build data transformations, integrate multiple data sources, maintain warehouse structures, and optimize data quality, scalability, and performance. Collaborate with BI teams supporting Power BI and reporting platforms while contributing to cloud-based data architecture implementations under senior guidance.

Build and maintain data infrastructure for machine learning workflows, including ingestion pipelines, data versioning, orchestration, CI/CD, dependency management, model deployment, data quality checks, and optimization of distributed cloud processing jobs. The role requires Python, ETL, GCP data services, orchestration tools, SQL, Pandas, Docker, and experience with distributed systems such as Dask or Spark.

Own the full lifecycle of pricing data products, including pipelines, data warehouse models, and ML infrastructure. Collaborate with data scientists to productionize real-time pricing algorithms, architect scalable systems, manage performance, and lead cross-team infrastructure initiatives. Build production-quality code, orchestrate workflows, support deployments, develop enterprise dashboards, and enforce GDPR-compliant data governance, security, masking, retention, and auditing.

Own and scale secure cloud infrastructure for data and AI platforms across AWS and GCP. Lead Terraform-based infrastructure as code, reliability and observability programs, GitLab CI/CD automation, Docker and Fargate deployments, and platform enablement for Airflow, dbt, streaming, and AI workloads. Establish security, access-control, cost-governance, monitoring, alerting, and SLO practices while partnering with data engineering teams.

Create, verify, validate, and deliver Integrated Product Data for the NH90 helicopter program. Responsibilities include authoring technical documentation, managing work packages and spare-parts configuration, analyzing change triggers and service bulletins, producing material evolution tables, and creating or modifying technical illustrations. The role requires coordination across international teams and expertise in aerospace documentation standards, configuration control, technical drawings, and tools including Windchill, SAP, AMOSS, and IsoDraw.

Design, build, and operate Nexthink's cloud data infrastructure across AWS regions. Automate provisioning and self-service (Crossplane), run and onboard data stores, ensure resiliency, DR and observability (Datadog), collaborate with product teams, handle incidents and on-call duties to meet RPO/RTO and availability SLAs.

As a Data Analyst, you will analyze data for business insights, create dashboards, and collaborate with teams to optimize user experience.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Join a Data & AI team to design and operate an AIOps platform, focusing on reliability, automation, and scaling AI solutions in production environments.

Build and own end-to-end data foundations, including ingestion pipelines, dbt models, data marts, semantic layers, and AI-ready datasets. Integrate fragmented marketing, advertising, ecommerce, analytics, and CRM data across a multi-tenant Snowflake environment. Manage platform API changes, data quality, restatements, performance, and client-specific modeling. Collaborate with product, engineering, AI, BI, and client teams while using AI coding agents to accelerate reliable production development.

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.

Build scalable data architectures and pipelines for API, batch, event, and streaming sources. Develop aggregation, preprocessing, governance, security, and lifecycle processes; support cloud OLTP and OLAP workloads; and integrate machine learning and analytics components, including MLOps. Collaborate with data science and application development teams to create, deploy, monitor, and maintain secure data solutions using AWS, containerization, infrastructure as code, and CI/CD.

Build and maintain data pipelines, ETL/ELT processes, and integrations to populate data warehouses and lakes. Implement tenant data security, support data science/AI needs, automate data quality and monitoring, perform code reviews, and deliver analytics and BI solutions with minimal supervision.