Top Data Engineer Jobs in Barcelona

The Senior Analytics Engineer will design scalable data models, translate business needs into data products, ensure data quality, and collaborate with teams on analytics solutions.

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.

Lead technical direction and hands-on development of scalable data pipelines into Snowflake. Mentor junior engineers, liaise with stakeholders and vendors, ensure data governance, quality, and security, and evaluate new data technologies to support BI and enterprise analytics.

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.

As a Data Engineer, you will design, build, and maintain data pipelines, ensure data quality, and collaborate with stakeholders to deliver data solutions.

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.

Lead technical data strategy and architecture for Navify Cervical Screening. Drive data integration, governance, and large-scale pipeline design; mentor engineers; evaluate new technologies; ensure compliance, quality, and timely delivery of data requirements across cross-functional teams.

Lead the development and optimization of data infrastructure for AI initiatives, collaborate with teams to architect, implement, and maintain data solutions for next-generation AI systems.

As a Data Engineer, you'll design, build, and maintain ETL/ELT pipelines, optimize data workflows, and support junior engineers while working in an agile environment on AI products.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

The Senior Product Data Engineer will build, iterate, and launch data products, focusing on large-scale data processing and ETL/ELT pipelines, using various technologies and implementing AI features.

Join Gartner as a Software Engineer contributing to AI-focused software solutions in a hybrid environment. Collaborate with cross-functional teams to design, build, and maintain scalable systems that deliver actionable insights to enterprise clients.

Design, build, and maintain a petabyte-scale market data lakehouse: capture/normalize exchange and vendor feeds, reconstruct order books, build batch/stream pipelines, manage Iceberg-over-S3 storage, develop tooling/SDKs, and ensure reliability/observability for research and trading.

Design and maintain a governed, canonical customer dataset by unifying GTM sources, building enrichment and entity-resolution pipelines, preparing structured and unstructured data for LLM/embedding workflows, implementing monitoring and lineage, and defining standards for AI-ready data across RevTech.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design and maintain real-time data pipelines and vector infrastructure for RAG and agentic AI. Build semantic layers and knowledge graphs, automate data guardrails to detect noise, bias, and PII, and deploy CI/CD for data infrastructure to ensure reliable, sub-second retrieval and production-ready context windows.

Lead design and ownership of Preply's data lake and end-to-end batch and streaming ingestion pipelines. Define data contracts, implement validation and observability, build enrichment and lifecycle management, enforce governance and privacy, and enable self-service ingestion templates and tooling while driving cross-team collaboration and platform reliability.

Design, build, and maintain Snowflake-based data pipelines and transformation models to support credit monitoring, analytics, and ML. Implement transformation frameworks, orchestrate reliable pipelines, onboard data sources, drive data quality, and collaborate with credit stakeholders to enable risk models and reporting.

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

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.

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.

Design and build scalable Azure-based ETL pipelines, data storage and analytics solutions. Collaborate with stakeholders to optimize data delivery, automate processes, support infrastructure, and create analytical tools to surface business insights.

Design, build and optimize Azure-based ETL/data pipelines and analytics infrastructure (ADF, Synapse, Data Lake/Storage). Implement scalable data solutions, automate processes, support data delivery, and create analytical tools. Collaborate with cloud, governance, and data stakeholders to deploy and maintain data solutions.

Design and implement Azure-based data infrastructure: build ETL/ELT pipelines, data storage (Data Lake/Warehouse), optimize data delivery, develop analytics and tooling, and collaborate with stakeholders to deploy and support data solutions.