Top Data Engineer Jobs in Spain

The Data Engineer II will develop and optimize data platforms, build scalable pipelines, enhance existing workflows, and collaborate with teams for data solutions.

Design, build, and operate AWS-based, event-driven, S3-backed data pipelines and reusable components. Ensure data quality, metadata, lineage, governance, secure handling, and enable analytics/BI. Use Prefect orchestration, Python and SQL, containerization, CI/CD and monitoring; collaborate with architects and product teams to translate requirements into scalable, production-grade data solutions.

Lead design and delivery of AI-powered visualization and investigation features using LLMs. Build reliable, explainable systems combining deterministic product capabilities with generative models, drive prompt/context engineering, evaluation frameworks, and cross-team product-focused technical leadership and mentorship.

Lead technical direction and hands-on implementation of semantic infrastructure for telemetry interoperability. Build scalable systems bridging OpenTelemetry, cloud and customer data models; define standards, mentor engineers, run on-call rotations, and drive cross-team platform initiatives for schema evolution and AI-enabled observability features.

The Senior Data Engineer designs and implements data infrastructure, oversees data pipelines, and ensures data reliability while mentoring junior staff. Responsibilities include automating governance, optimizing data performance, and collaborating with various stakeholders.

Lead design and delivery of cloud-native, scalable regulatory reporting data platforms and pipelines. Integrate diverse internal/external data, promote engineering best practices, manage technical debt, and collaborate with stakeholders to ensure reliable, test-covered solutions for financial services regulatory and climate disclosures.

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.

As a Data Analyst, you will analyze data for business insights, create dashboards, and collaborate with teams to optimize user experience.

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.

Join a Data & AI team to design and operate an AIOps platform, focusing on reliability, automation, and scaling AI solutions in production environments.

The Junior Data Engineer will design and optimize data infrastructure on GCP, focusing on ETL/ELT pipelines and ensuring data quality, governance, and security.

Design, build, and maintain high-scale distributed backend services and data pipelines for asset discovery, recognition, and enrichment. Integrate AI/ML into workflows, solve concurrency and fault-tolerance problems, participate in architecture and code reviews, mentor engineers, and use AI-assisted development tools in daily engineering practice.

Design, model, and develop AWS-based data ecosystems and pipelines. Gather and document business and technical requirements, optimize data storage (lakes/warehouses), tune performance, troubleshoot processing and infrastructure issues, integrate diverse data sources, mentor junior engineers, and engage directly with clients.

As a Data Engineer, you'll design and maintain data pipelines, ensure data quality and reliability, and support product engineering teams in leveraging the Data Platform.

The Data Engineer designs big data architecture, optimizes machine learning models, collaborates on data projects, and builds data pipelines.

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

Build, own, and maintain the Microsoft Fabric Lakehouse and ETL/ELT pipelines to migrate Excel/SharePoint reports, integrate new sources and ERP (Dynamics 365 F&O), implement data quality/monitoring, and document data models to support company-wide BI and reporting.

The Mid-Level Data Engineer will build and maintain data pipelines, ensuring data quality and integration for strategic revenue analysis. Close collaboration with cross-functional teams is essential.

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.

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

Design, build, and maintain testable, scalable data pipelines and warehouse transformations for hospital EHR data. Implement ETL using Python and Prefect (or similar), write SQL (DDL/DML), develop connectors for medical sources, and provide operational support and maintenance within a cross-functional team.

As a Principal Data Engineer, you'll lead a team to design data solutions for regulatory reporting, ensuring quality and compliance while collaborating across stakeholders.

Lead design and implementation of scalable data infrastructure and pipelines, using IaC, CI/CD, dbt, and streaming frameworks. Define SLIs/SLOs, automate governance, optimize warehouse performance, mentor engineers, and advance observability and reliability.