Top Data Engineer Jobs in Barcelona

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Develop AI, advanced analytics, optimization, and machine learning solutions for supply chain planning, fulfillment, manufacturing, and network optimization. Translate client needs into measurable data-driven outcomes, collaborate with data scientists, engineers, and consultants, and support business development through proposals and innovative AI offerings. Work with forecasting, predictive modeling, generative AI, cloud platforms, supply chain software, large-scale data processing, and visualization tools.

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

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 operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

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.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

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.

Maintain and extend distributed ETL pipelines and BigQuery datasets, build and operate REST microservices, optimize analytical queries, operate AI-powered tooling, and ensure backward compatibility across authentication flows.

Maintain and govern core data tools, automate data processes using Python, LLMs, workflow tools, APIs, and MCP, and support GCP cost governance. Improve Looker and self-service data support, identify data-consumption improvements, and mentor interns. Partner with senior data engineers and business stakeholders while developing reliable data transformations, documentation, validation practices, and AI-enabled data solutions.

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.

Lead the design and delivery of scalable data platforms and pipelines supporting analytics, products, and business decisions. Set technical direction across data architecture, storage, transformation, and serving layers while improving reliability, quality, and maintainability. Mentor engineers, conduct code reviews, solve complex technical problems, and influence cross-functional stakeholders. The role requires hands-on engineering across cloud infrastructure, orchestration, APIs, databases, software delivery, and large-scale data environments.

Own data science projects from business questions through customer-facing AI products. Build, evaluate, deploy, and maintain machine learning models; advise internal teams; research advanced AI applications; support important data domains; and collaborate with stakeholders to assess production readiness. The role requires Python-based machine learning development, cloud orchestration, generative AI experience, production deployment experience, and strong communication skills.

As the Data Engineer, you will design and maintain data pipelines, scale data infrastructure, ensure data quality, and collaborate with analysts to enhance decision-making capabilities.

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 maintain high-performance data ingestion pipelines and features for ML models, write clean testable code, participate in code reviews and architectural decisions, monitor deployments for reliability and performance, and collaborate in an agile team following a "you build it, you run it" philosophy.

Develop, operate, and optimize a multi-tenant Azure data platform using Data Factory, ADLS Gen2, Databricks, and Synapse Analytics. Build scalable PySpark and Spark ETL/ELT pipelines, maintain data infrastructure, implement CI/CD automation, optimize cost and performance, and support data governance, security, modeling, and reporting. Collaborate with BI and data teams across development, test, and production environments.

Design, build, and maintain ETL/ELT pipelines, model and optimize NoSQL data (MongoDB), create Metabase dashboards, prepare datasets for AI/LLM projects, support web back-end (Node.js), and collaborate with product teams to integrate AI components and ensure data quality and freshness.

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.