Top Data Engineer Jobs in Spain

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.

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.

Lead the technical strategy, architecture, roadmap, and delivery of a cloud-native Engineering Data Platform. Establish governed data products supporting engineering intelligence, operational resilience, security automation, developer experience, and AI-enabled engineering. Drive data quality, lineage, security, compliance, reliability, DevSecOps, DataOps, observability, and automation while influencing senior stakeholders and mentoring engineering teams.

Implement and improve the data centre health and safety management system. Conduct risk assessments, RAMS reviews, inspections, audits, incident investigations, and contractor safety activities. Deliver training and toolbox talks, monitor safety KPIs, maintain emergency response plans, and ensure compliance with legislation, standards, and company policies. The role requires regular site presence and occasional travel between sites.

Design, build, and evolve scalable real-time and batch data pipelines and ETL frameworks. Own data platform performance, reliability, quality, and architecture while collaborating with engineering and product stakeholders. Shape ingestion, transformation, storage, and serving strategies; mentor engineers; and support trader-facing data products. The role requires extensive production data engineering experience, distributed processing expertise, cloud infrastructure knowledge, and proficiency with Kotlin, Python, TypeScript, Flink or Spark, Kafka, Redis, and PostgreSQL.

Designs and maintains dbt and SQL data models, transforms raw data into trusted datasets, defines business metrics, supports self-service BI, and contributes to Python/Airflow pipelines. Partners with engineering and business teams on data quality, governance, observability, data mesh initiatives, and domain-owned data products. Uses modern cloud data warehouses, BI tools, and AI coding assistants to accelerate analytics development.

Lead analytics strategy for Services & Operations by converting complex data into KPIs, insights, and business recommendations. Establish data governance, quality standards, documentation, and prioritization processes. Enable AI and advanced analytics through governed datasets, identify productivity opportunities, and measure business impact. Partner with engineering, architecture, and business teams to improve data accessibility, operational efficiency, and data-driven decision-making.

Design, build, and maintain data ingestion and transformation pipelines; manage storage and processing layers; ensure data quality, performance, and reliability; implement data engineering best practices; provide technical leadership and mentor data engineers.

Integrate and govern manufacturing data across PLC, DCS, SCADA, MES, laboratory, and historian systems. Support AVEVA PI, apply ISA-88 and ISA-95 data models, monitor data flows, troubleshoot quality issues, and coordinate improvements across Automation, IT, Manufacturing, and Data teams. The role also supports site deployments, commissioning, documentation, and continuous optimization across global Scent manufacturing facilities.

Forward Deployed Engineers embed with customers to design, prototype, deploy, and optimize production AI solutions. They translate ambiguous business needs into technical solutions, build agentic AI systems, manage end-to-end delivery, improve performance and reliability, document deployment insights, and contribute code and feedback to internal AI platforms. The role requires strong programming, software engineering, data engineering, cloud, distributed systems, and stakeholder communication skills, with customer-site travel as needed.

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.

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.

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

Design and build Fever’s B2C and B2B products across discovery, AI assistants, data tools, reports, and conversion funnels. Frame user and business problems, validate solutions with SQL and metrics, create polished interfaces and prototypes, and implement production-ready features using front-end technologies. Collaborate with product managers and engineers while using AI responsibly to accelerate exploration and development.

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

Lead applied AI and data science initiatives from experimentation through production. Develop machine learning, NLP, generative AI, LLM, retrieval, RAG, and agent-based solutions using Python, SQL, APIs, and data pipelines. Design evaluation frameworks, work with large-scale structured and unstructured data, and collaborate on deployment, monitoring, and infrastructure. Contribute to responsible AI practices, technical standards, reusable components, and mentoring while translating business problems into scalable technical solutions.

Leads the electrical design and delivery of mission-critical data center facilities, including MV/LV systems, substations, generators, UPS systems, switchgear, grounding, and power studies. Develops calculations, drawings, specifications, and technical documentation; reviews deliverables for standards compliance; coordinates multidisciplinary teams; mentors engineers; engages clients; and supports construction and technical project delivery.

Lead the design and delivery of electrical systems for hyperscale, colocation, and enterprise data centers. Responsibilities include designing critical power infrastructure, defining redundancy architectures, conducting electrical studies, coordinating multidisciplinary teams, reviewing technical documentation, mentoring junior engineers, supporting client meetings and proposals, and developing energy-efficient solutions. The role requires data center experience, engineering leadership, knowledge of industry standards, and proficiency with ETAP, Caneco, AutoCAD, and Revit MEP.

Lead the greenfield design and production implementation of a secure, scalable cloud data platform. Build infrastructure with Terraform and CI/CD, develop resilient ingestion pipelines, create dbt transformation models and a unified semantic layer, and enforce privacy through code-based access policies. The role also involves documenting architecture, supporting governed data access for BI and AI assistants, and integrating data across enterprise systems.

Design, build and maintain end-to-end finance data pipelines, data marts, and Power BI reports using Snowflake, dbt, Azure Data Factory and CI/CD practices. Collaborate with stakeholders, apply data governance and Agile methods, produce technical documentation, and support evolutive, corrective and adaptive improvements.

Build and maintain high-performance data lakehouse infrastructure, including Apache Iceberg maintenance workflows, compaction, snapshot expiry, manifest rewriting, and orphan file cleanup. Develop Rust-based query routing systems, connectors, and production services while ensuring data integrity under concurrency and failures. Responsibilities include open-source collaboration, code reviews, releases, on-call support, and debugging customer environments.

Leads customer-facing clinical data discovery, mapping, validation, integration, and troubleshooting for Evinova SaaS solutions. Advises customers on clinical data standards, oversees implementations and change control, validates data loads, manages defects, and supports integrations. Serves as a product subject matter expert, supports pre-sales and post-implementation questions, develops delivery best practices, trains stakeholders, and partners with Product, Engineering, Security, and customers to deliver compliant clinical data solutions.

Leads customer-facing clinical data discovery, mapping, integration, validation, and implementation for Evinova SaaS solutions. Advises customers on clinical data standards, oversees data loads and integrations, troubleshoots discrepancies, manages change control, and partners with Product and Engineering. Serves as a data subject-matter expert for post-implementation support, pre-sales, training, documentation, and delivery playbook development.

Designs, builds, deploys, and maintains large-scale data ingestion and transformation pipelines processing over 100 TB daily. Develops domain models, modernizes legacy architectures, supports live integrations, troubleshoots complex issues, and improves reliability through observability, automated testing, and automation. Collaborates with product, engineering, support, business teams, customers, and partners while using AI-assisted tools to improve engineering velocity.

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.