Top Data Engineer Jobs in Spain

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

The role involves finding audio data sources, operating and extending cloud infrastructure, and collaborating to enhance data quality and models.

Design, build and maintain end-to-end finance data pipelines, data marts, and Power BI reports using Snowflake, dbt, Azure Data Factory and CI/CD practices. Collaborate with stakeholders, apply data governance and Agile methods, produce technical documentation, and support evolutive, corrective and adaptive improvements.

The Tableau Developer will design, build, and maintain Tableau dashboards, optimizing performance and ensuring data accuracy while collaborating with stakeholders.

Own deployment, configuration, and optimization of high-speed Data Transfer Appliances bridging HPC and AI infrastructure. Resolve network and I/O bottlenecks, integrate with object/NAS/parallel storage, automate deployments (Ansible/Kickstart/Red Hat Satellite), monitor performance, produce user documentation and tools, and provide cross-functional technical support.

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.

Manage and deliver reference data for Maintenance Information Systems, lead digitalization projects, define IT requirements, oversee subcontractors, ensure data integrity, support customer data migrations and reporting, and resolve technical queries to support aircraft maintenance and service operations.

Coordinate delivery, integrity and integration of aircraft maintenance reference data for customer MIS/ILS systems. Lead digitalization initiatives, define IT requirements, manage subcontractors, support on-site data migrations and uploads, resolve data inconsistencies, generate KPIs/reports, and drive continuous improvement.

Lead design and evolution of scalable real-time and batch ETL pipelines and data platform. Own performance, reliability, and data quality; drive architecture and tooling choices; collaborate with product and backend teams; mentor engineers and shape target architecture to support trader-facing analytics and low-latency data needs.

As a Senior Data Engineer, you will deliver complex projects, manage customer relationships, and mentor junior engineers while using Databricks and various cloud platforms.

Build and deploy data-driven applications by implementing ML models, designing scalable data pipelines and automated workflows, performing large-scale data analysis and testing, collaborating with stakeholders, and guiding data engineering best practices to optimize asset usage.

Lead the design, build, and scaling of cloud-native AI data platforms and pipelines. Act as Technical Product Owner and squad lead to deliver governed data assets, MLOps, feature stores, vector DBs, and CI/CD to operationalize Generative AI, LLMs, RAG, and analytics solutions while ensuring security, compliance, and platform adoption.

As a Data Platform DevOps Engineer, you will develop and maintain customer data API integrations, improve CI/CD pipelines, and ensure scalable, production-ready solutions.

Build and own end-to-end features for Fever's native Android app: shape product with PM/Design, implement Kotlin/Compose solutions in a multi-module Gradle codebase, integrate APIs and payment SDKs, instrument experimentation/analytics, iterate from data, and bridge AI/data capabilities into the user experience.

Build a greenfield data platform and ingestion pipelines for payments, marketing, and operational data across AWS and GCP; implement a Lakehouse with medallion-layer transformations in dbt/SQLMesh (Redshift/BigQuery); own orchestration (Dagster/Airflow), lineage, backfills, retries, alerting; apply IaC with Terraform and cost controls; operate autonomously in a remote, async environment.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

Design and oversee the implementation of the Collibra Marketplace, ensuring integration with data platforms and maintaining compliance while optimizing performance.

The role involves managing and innovating data governance platforms like Databricks Unity Catalog, ensuring platform security and compliance, and collaborating with cross-functional teams to implement data governance solutions.

Partner with business stakeholders to translate requirements into data models, analytical datasets, dashboards, KPIs, and self-service BI. Build and maintain data pipelines in Databricks using SQL and Python, ensure data quality and documentation, integrate local sources, and support Data & AI use case design alongside Data Engineers.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Design and implement end-to-end data architecture (Postgres CDC -> Kafka -> Snowflake). Own ingestion, orchestration, transformation, streaming pipelines, and data quality. Define data contracts, tenant separation, replay/reload, and external delivery. Start hands-on and grow a small data engineering team with a 3/6/12-month roadmap.

Design and build a greenfield, high-scale backend data platform: own architecture and stack, implement low-latency data pipelines and APIs, collaborate with Growth and Product, and grow a team.

The role involves managing data collection processes for AI model training, operating cloud infrastructure, and working with scientists to enhance data quality and scale.

Maintain and extend distributed ETL pipelines and BigQuery datasets, build and operate REST microservices, optimize analytical queries, operate AI-powered tooling, and ensure backward compatibility across authentication flows.

Own and evolve Enode's data platform and pipelines to support the Flex product: build reliable datasets, ensure data quality and monitoring, enable self-serve analytics, and partner with data science on feature pipelines and model readiness. Drive pragmatic platform improvements, documentation, and tooling to scale data reliability and trust across delivery, billing, and reporting.