Top Data Engineer Jobs in Barcelona

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

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.

Develop and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows for data validation, troubleshooting, and operational automation. Implement data quality controls, monitoring, testing, deployment, and production support while creating reusable frameworks and improving pipeline performance, reliability, and scalability.

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.

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.

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.

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 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.

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.

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

Build and automate Evinova’s cloud-based data foundation, including lakehouse architecture, catalogs, batch and streaming pipelines, infrastructure as code, data modeling, governance, security, monitoring, and CI/CD. The role requires hands-on AWS data engineering expertise, mentoring engineers, optimizing reliability and automation, and collaborating with product and security teams to support SaaS data platforms, data science, and AI capabilities.

Build and optimize AWS-based data platforms, lakehouses, catalogs, streaming and ETL pipelines, infrastructure automation, governance, security, and monitoring. Design data models and production-grade cloud frameworks using AWS services, Apache Iceberg or related table formats, Spark, and infrastructure as code. Mentor engineers, establish engineering best practices, and collaborate with product and security teams to deliver reliable multi-tenant SaaS data capabilities.

Lead mechanical engineering design and technical delivery for complex data centre projects. Design resilient cooling, ventilation, automation and control systems; coordinate with electrical and multidisciplinary teams; supervise engineers; produce and review detailed designs; manage resources, timelines and client relationships; and apply building physics, CFD, sustainable design and digital design tools to mission-critical facilities.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Design, build, and operate high-performance backend systems for web data acquisition handling nearly 200 million daily requests. Develop proxy layers, browser tooling, request routing, and protocol-level infrastructure primarily in Rust. Investigate crawler and blocking issues, build reliable interfaces and guardrails, and maintain code quality through testing, observability, and secure scalable solutions. Collaborate with crawler engineering, DevOps, Security, and other teams.

Own and evolve Shalion’s data platform foundations, including Snowflake, AWS lakehouse infrastructure, Iceberg tables, Airflow orchestration, and Terraform-managed infrastructure. Manage access controls, IAM integrations, CI/CD, security, governance, reliability, and cost efficiency. The role requires production experience with data platforms or infrastructure, strong Snowflake and AWS knowledge, infrastructure-as-code expertise, and effective communication with technical and non-technical stakeholders.

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.

Investigate marketing tracking discrepancies, trace and fix end-to-end data pipelines (especially GA4/Salesforce and gclid issues), improve revenue attribution and reporting, and partner with marketing, engineering, and sales to implement reliable tracking and reporting solutions.

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.

Develop and optimize large language models and NLP capabilities for decision support, mission planning, workflow automation, and situational awareness. Responsibilities include data preprocessing, model training and fine-tuning, RAG and agentic system development, evaluation, deployment, monitoring, documentation, and collaboration with engineering and product teams. The role requires strong Python, deep learning, LLMOps, cloud, and MLOps expertise, plus knowledge of responsible AI and bias mitigation.

Lead the architecture and hands-on delivery of an Everything-as-Code data mesh, including Kafka and NiFi event streaming, Kubernetes infrastructure, Terraform and GitOps deployment, schema governance, hybrid lakehouse integration, FinOps optimization, resiliency, and distributed observability across multi-cloud and on-premises environments.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.