Top Data Engineer Jobs in Barcelona

Build and operate a modern data platform: design core data models, implement observable ETL pipelines, enforce data quality and governance, optimize Snowflake, provision cloud infra, collaborate with stakeholders and contribute to roadmap and team strategy.

Build and maintain scalable, secure database platform infrastructure in AWS. Develop infrastructure-as-code, containerized platforms, CI/CD pipelines, data transformation and orchestration solutions using tools such as Terraform, Kubernetes, dbt, and Airflow. Collaborate with product and analytics teams on data access, metadata, and platform capabilities. Participate in roadmap delivery, documentation, on-call incident response, troubleshooting, and observability improvements.

Design, build, and maintain reliable batch and streaming ingestion pipelines and data lake components. Implement data contracts, quality checks, enrichment logic, observability, governance, and enable self-service ingestion templates while collaborating with analytics, ML, and product teams.

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.

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.

Lead data engineering strategy and architecture for diagnostic products. Design and optimize AWS-based data platforms and PySpark, SparkSQL, and SQL workflows; improve scalability, performance, cost efficiency, security, governance, and compliance. Provide technical leadership and mentorship, enforce engineering best practices, and partner with architects, product owners, engineers, and business stakeholders. Drive GenAI and advanced analytics initiatives, including scalable data lakes and warehouses.

Own the financial audit data workstream for Printful and Printify. Deliver accounting reconciliations, investigate transaction discrepancies, build Snowflake transformations, extend Looker models, collaborate with Accounting and auditors, and document pipelines, corrections, and edge cases against fixed deadlines.

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.

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

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, 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.

Build and own end-to-end data foundations, including ingestion pipelines, dbt models, data marts, semantic layers, and AI-ready datasets. Integrate fragmented marketing, advertising, ecommerce, analytics, and CRM data across a multi-tenant Snowflake environment. Manage platform API changes, data quality, restatements, performance, and client-specific modeling. Collaborate with product, engineering, AI, BI, and client teams while using AI coding agents to accelerate reliable production development.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize large-scale processing, support SQL Server/SSIS environments, and collaborate with BI teams to enable analytics and reporting.

Build scalable data architectures and pipelines for API, batch, event, and streaming sources. Develop aggregation, preprocessing, governance, security, and lifecycle processes; support cloud OLTP and OLAP workloads; and integrate machine learning and analytics components, including MLOps. Collaborate with data science and application development teams to create, deploy, monitor, and maintain secure data solutions using AWS, containerization, infrastructure as code, and CI/CD.

Build, maintain, and improve scalable data pipelines and infrastructure to support persona modeling and reporting. Enhance system reliability with monitoring/SLI-SLO, troubleshoot reporting workflows, and collaborate across Product, DevOps, and Core Services to streamline data systems and ad delivery optimization.

The Senior Data Engineer develops data solutions, builds scalable data pipelines, and fosters best practices while collaborating within a multidisciplinary team.

Lead design and evolution of the data platform: architecture, schema design, governance, and infrastructure on AWS using Terraform. Ensure AI-ready, high-quality data; set standards, mentor engineers, and drive cross-team technical direction.

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

The Renewables Measurement & Data Engineer will validate renewable measurement data, support on-site measurements, troubleshoot systems, and analyze resource potential and asset performance.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Design, build, and maintain scalable data architecture and ETL pipelines; ensure data quality, governance, and availability; implement data models for analytics and AI/ML; collaborate with Product and Engineering; document processes and support AI development workflows.

Build domain data products and pipelines for AI consumption: create retrieval-ready, semantically annotated, contract-governed assets; deploy agents for metadata, entity resolution and classification; ensure data quality, lineage, certification, and reuse; collaborate with domain stewards, business teams and IT to model, store and serve structured and unstructured data for retrieval and modeling.

The Data Engineer will build scalable infrastructure for data analysis, ensuring high performance and compliance with data protection standards while collaborating with cross-functional teams.

Design and lead scalable, secure data architectures and end-to-end data flows across cloud and hybrid environments. Define standards, guide pipeline development and modeling, advise on storage, governance and performance, evaluate new tools, and lead technical initiatives across teams.