Top Data Engineer Jobs in Spain

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.

Build and maintain data infrastructure, ingestion pipelines, AWS-based processing systems, and data observability frameworks. Optimize data performance and costs, troubleshoot data quality issues, and standardize DataOps practices through version control, automated testing, and Infrastructure as Code. Collaborate with stakeholders on high-volume telemetry and clickstream data while supporting reliable downstream analytics.

Lead the design, development, testing, and deployment of scalable enterprise data pipelines and products supporting Home Buying. Provide technical leadership across architecture, governance, engineering standards, delivery coordination, and technical decision-making. Build solutions using AWS, Big Data technologies, Snowflake, and dbt; mentor engineers, support recruitment, collaborate with stakeholders, and contribute to GenAI adoption and data platform modernization.

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

Lead data engineering initiatives across large-scale e-commerce datasets. Own data models, transformation architecture, analytics projects, client data products, internal dashboards, documentation, and technical roadmap delivery. Collaborate with technical and business stakeholders, mentor teammates, and translate business needs into scalable data solutions using SQL, BigQuery, Google Cloud, ETL/ELT tools, and visualization platforms.

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

Design and scale cloud-native data platforms, including batch and streaming pipelines, data replication, workflow orchestration, and infrastructure automation. Build Python and SQL transformations, manage Kafka-based real-time systems, establish data governance and quality controls, and develop performant data models for reporting and business intelligence. Partner with business stakeholders to translate requirements into reliable, scalable data solutions.

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.

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.

Design, build, and operate reliable, scalable backend services, APIs, and platform components. Lead complex feature development from planning through release, improve service performance, monitoring, alerting, observability, and operational readiness, and collaborate with engineering, product, and QA teams. Serve as a technical contributor for debugging and architecture decisions while mentoring junior engineers and advancing engineering standards.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

The Data Engineer will design, build, and optimize data solutions, develop data pipelines, and work with stakeholders in BI and analytics environments.

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.

Build reliable datasets and production-grade SQL data models, applying data quality, testing, documentation, and version-control practices. Collaborate with analysts, product managers, and business stakeholders to automate workflows and deliver trusted data products. Use generative AI responsibly for coding, analysis, workflow automation, and data experiences while validating outputs. Contribute to data transformations and progressively develop ownership of small data products and AI-enabled analytics initiatives.

Design, build, and maintain scalable cloud data pipelines, databases, and analytics platforms using Azure and Databricks. Integrate industrial/time-series data, develop ETL/ELT and API-based integrations, define data models and governance, and support analytics, AI-driven solutions, predictive maintenance, and operational reporting in collaboration with cross-functional teams.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitoring, incident response, cost and performance optimization, migrating legacy ETL, onboarding customers, enforcing Delta Lake architecture and multi-tenant isolation, and applying CI/CD, security, and observability best practices to ensure reliable, scalable data delivery.

As a Lead Data Engineer, you will design, build, and maintain scalable data platforms, assess data landscapes, and collaborate with stakeholders to translate requirements into robust data solutions.

The Lead Software Engineer will develop AI-powered software solutions, design backend systems, and collaborate with cross-functional teams to enhance AI capabilities.

Design, build and maintain scalable data pipelines, models and infrastructure to power product analytics. Partner with BI, Data Science and product teams, implement testing/monitoring, and contribute to AI-enabled tooling and analytics platforms.