Top Data Engineer Jobs in Spain

Build and maintain cloud-based media data pipelines and user-facing data products. Design, deploy and govern BigQuery/DBT pipelines, APIs and dashboards, ensure data quality/security, manage vendors, and drive product-oriented delivery and adoption.

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

Lead design and delivery of shared observability data platform capabilities to reduce developer friction, improve operational visibility, enable cost intelligence, build self-service operational tooling, drive reusable services, mentor engineers, influence cross-team architecture, and participate in on-call to improve platform reliability.

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.

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.

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.

The Data Engineer will design and optimize data pipelines for AML solutions, support data scientists, and manage technical relationships with customers.

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

The Data Engineer will design data pipelines and architecture, optimize machine learning models, and collaborate on data projects for international brands.

As a Data Engineer, you will model data using dbt, manage data infrastructures, design data pipelines, and deliver training and presentations to clients.

As a Technical Data Engineer, you will develop and maintain the Aircraft Maintenance Manual, ensuring compliance with specifications while managing technical documentation and process harmonization.

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.

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.

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 operate Nexthink's cloud data infrastructure across AWS regions. Automate provisioning and self-service (Crossplane), run and onboard data stores, ensure resiliency, DR and observability (Datadog), collaborate with product teams, handle incidents and on-call duties to meet RPO/RTO and availability SLAs.

Lead a team to design, build and maintain scalable Azure data platforms and pipelines (ADF, Databricks, Synapse). Own data modeling, architecture, performance optimization, CI/CD, automation, troubleshooting, and stakeholder collaboration to support analytics and transformation initiatives.

Design, build, and maintain scalable Azure data pipelines and architectures using Data Factory, Databricks, and Synapse. Implement ETL/ELT, data modeling, storage (Data Lake, Azure SQL), performance optimization, automation, CI/CD deployment, troubleshooting, and documentation while collaborating with product owners, analysts, data scientists, and DevOps in an Agile transformation.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

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

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.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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.

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.

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.