Top Data Engineer Jobs in Spain

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.

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.

Design, develop, deploy, and maintain reusable data engineering solutions for complex pharmaceutical R&D workflows. Translate scientific and business needs into configurable architectures, implement cloud-based data processing, ensure data quality and metadata integrity, and apply FAIR+T principles. Lead technical delivery, evaluate trade-offs, coordinate internal and external contributors, mentor colleagues, and support AI-enabled automation for preclinical research data management.

Designs, develops, deploys, and maintains reusable data engineering solutions for pharmaceutical R&D workflows. Translates scientific and business requirements into configurable platforms, applies cloud and modern engineering practices, supports FAIR data and metadata quality, and evaluates AI-enabled automation. Provides technical leadership, coordinates internal and outsourced contributors, mentors colleagues, manages delivery risks, and collaborates with architects, data engineers, and scientific specialists to scale reliable data capabilities.

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.

Build and maintain data infrastructure and pipelines for a globally deployed Customer Data Platform. Manage both development and operations, ensure platform stability, quality, compliance, and test automation, and collaborate with internal teams and implementation partners. Drive issue resolution, negotiate requirements and scope, and introduce technology-enabled process improvements. The role requires SQL, Python, data platform, DevOps, incident management, and Agile delivery experience.

Develop and maintain scalable production data pipelines, machine learning data pipelines, and data flows supporting financial crime detection solutions. Build tools for data science and analytics teams, optimize large-scale datasets, perform data transformation and validation, and collaborate with product, R&D, and customer teams. The role also involves training customers, managing technical relationships, and traveling domestically and internationally when required.

Build and maintain ETL pipelines using Python and SQL, create reliable datasets and data models, apply testing and CI/CD practices, and collaborate with Product, Operations, and Finance teams. The internship includes mentorship, project ownership, and hands-on experience with cloud-based data engineering.

Build and maintain data infrastructure for autonomous AI systems, including RAG pipelines, vector databases, knowledge graphs, semantic layers, and automated data-quality guardrails. Develop ETL and real-time data pipelines using cloud tooling and relational or NoSQL databases. Resolve source-data quality issues and deploy CI/CD pipelines that keep AI context current, reliable, and secure. Collaborate with technical and non-technical stakeholders to translate business requirements into scalable data solutions.

Design, develop, and optimize scalable batch and streaming data pipelines using Python, PySpark, and SQL. Build data models, process large datasets, implement data quality and validation controls, troubleshoot pipeline issues, and improve performance and reliability. Collaborate with engineering, architecture, and business teams in Agile environments while applying modern cloud data engineering and software development practices.

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.

Designs and maintains scalable data pipelines integrating SAP and non-SAP sources. Develops database solutions, data transformations, quality assurance processes, and analytical data models. Optimizes data systems and collaborates with machine learning engineers, data scientists, analysts, and stakeholders. The role requires SAP data and analytics expertise, Python and SQL proficiency, database knowledge, cloud platform familiarity, and strong communication skills.

Design and maintain ETL/ELT pipelines on Google Cloud Platform, optimize data processing workflows, and collaborate with stakeholders to develop data solutions.

Design and maintain ETL/ELT pipelines, integrate and model data, ensure data quality, and develop scalable Power BI dashboards and reports. Analyze business data, define KPIs, optimize reporting performance, and collaborate with technical and business teams. The role also explores LLM and AI capabilities in data workflows and requires strong SQL, Python, DAX, and Power Query skills.

Designs, develops, and optimizes scalable Databricks data pipelines and ETL/ELT processes using cloud-native technologies. Builds data models, quality frameworks, monitoring solutions, and reliable data products supporting reporting, analytics, AI, and machine learning. Collaborates with technical and business stakeholders, troubleshoots platform performance issues, contributes to CI/CD and infrastructure automation, and follows governance, security, and compliance practices.

Build and deploy production knowledge graphs in Stardog using semantic web standards, ontology modeling, SPARQL, mappings, and virtual graphs. Integrate life science data from diverse sources, automate graph builds and CI/CD workflows with Python, enforce quality and validation, and support governance, lineage, and reusable semantic assets. Collaborate with subject matter experts and data and AI engineering teams in agile environments.

Build and maintain ETL/ELT pipelines, data models, and automated data flows. Ensure data quality, consistency, freshness, and database performance. Create Metabase dashboards and optimized queries, support internal data users, and recommend KPIs. Design MongoDB schemas and migrations, contribute to Node.js web projects, and develop data-processing solutions using Python or TypeScript. Apply AI/ML concepts and collaborate with development and business teams in a hybrid Barcelona-based role.

Build and maintain data ingestion services and robust pipelines supporting machine learning models in a high-performance real-time bidding platform. Responsibilities include designing data features, writing tested and efficient code, participating in code reviews and technical decisions, monitoring deployments, supporting production reliability, and collaborating in an agile engineering team.

Connects renewable energy assets, SCADA systems, industrial historians, and enterprise platforms. Responsibilities include designing OT/IT integrations, managing industrial communications, standardizing data and asset metadata, monitoring pipelines, resolving connectivity and data-quality issues, supporting asset onboarding, coordinating EPC and technology partners, and ensuring operational data supports reporting and analytics. Requires strong experience with SCADA, historians, industrial protocols, OT cybersecurity, SQL, APIs, networking, and technical documentation.

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.

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 production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

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.