Top Data Engineer Jobs in Barcelona

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.