Top Data Engineer Jobs in Spain

Perform end-to-end data processing, statistical analysis, visualization, dashboard development, and reporting. Collaborate with business functions to translate data into actionable insights, maintain analytical tools, ensure data quality and governance, and promote data-driven decision-making through coaching and stakeholder communication.

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.

Build scalable data architectures and pipelines for API, batch, event, and streaming sources. Develop aggregation, preprocessing, governance, security, and lifecycle processes; support cloud OLTP and OLAP workloads; and integrate machine learning and analytics components, including MLOps. Collaborate with data science and application development teams to create, deploy, monitor, and maintain secure data solutions using AWS, containerization, infrastructure as code, and CI/CD.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Lead the design and delivery of scalable data platforms and pipelines supporting analytics, products, and business decisions. Set technical direction across data architecture, storage, transformation, and serving layers while improving reliability, quality, and maintainability. Mentor engineers, conduct code reviews, solve complex technical problems, and influence cross-functional stakeholders. The role requires hands-on engineering across cloud infrastructure, orchestration, APIs, databases, software delivery, and large-scale data environments.

Own data science projects from business questions through customer-facing AI products. Build, evaluate, deploy, and maintain machine learning models; advise internal teams; research advanced AI applications; support important data domains; and collaborate with stakeholders to assess production readiness. The role requires Python-based machine learning development, cloud orchestration, generative AI experience, production deployment experience, and strong communication skills.

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 optimize scalable data systems and ETL pipelines for real-time and analytical use. Ensure data quality, integrate internal and external sources, develop internal tools, and participate across the software development lifecycle to support product, operations, and marketing needs.

Design, build, and maintain scalable data pipelines and architectures within the Microsoft ecosystem. Collaborate with data scientists and AI engineers to ensure data quality, governance, and performance for analytics, ML, and AI agents. Implement ETL/ELT, data models, CI/CD, and automation workflows using Azure, Databricks, and Power Platform tools.

Freelance Senior Data Solution Engineer responsible for delivering approximately two custom client data projects monthly. Duties include qualifying requirements, analyzing large datasets with Python and SQL, building dashboards and predictive models, developing business recommendations, presenting findings to clients, documenting projects, and sharing insights with Product. The role requires fluent Spanish and English, freelance status, and availability for a full-time four-to-five-month mission based in Barcelona or Madrid.

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.

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.

Supports global data management platforms by monitoring data collection, integration, quality, security, and service delivery. Provides user support, troubleshooting, and help-desk assistance, escalates technical issues, and contributes to web application development and technical improvements. The role also involves coaching and supporting data operations specialists while maintaining strong data quality and operational processes.

Design, operate, and improve enterprise Databricks data platforms on Azure. Build shared platform components, automate provisioning through Infrastructure as Code and CI/CD, and ensure reliability, security, performance, cost efficiency, and observability. Enable self-service through reusable templates and standards, advise data teams, support data and AI solutions, and deliver training, documentation, and mentoring across the organization.

Build, test, deploy, and automate reusable data platform components for NN’s Azure lakehouse and Databricks environments. Translate business-unit needs into modular, scalable solutions; optimize architecture, data quality, governance, security, and cost performance; and support data and AI initiatives. Collaborate through agile development, pair programming, mentoring, technical discussions, and community participation across international data engineering teams.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Design, implement, and maintain AWS-based infrastructure and deployment pipelines for an enterprise search platform. Collaborate with stakeholders, ensure SLAs, monitor system health, troubleshoot issues, and improve reliability and cost efficiency across global cloud environments.

Design, implement, and test cloud-based server-side software in Python. Optimize code performance, maintain unit and integration tests, and work with AWS, Docker, DevOps, and CI/CD tools. Preferred experience includes reverse engineering public APIs, PostgreSQL, Django ORM and DRF, network protocols, and web or mobile technologies.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Develop, operate, and optimize a multi-tenant Azure data platform using Data Factory, ADLS Gen2, Databricks, and Synapse Analytics. Build scalable PySpark and Spark ETL/ELT pipelines, maintain data infrastructure, implement CI/CD automation, optimize cost and performance, and support data governance, security, modeling, and reporting. Collaborate with BI and data teams across development, test, and production environments.

Build scalable marketing data models, DBT projects, and data pipelines supporting attribution, funnel analytics, customer journeys, and marketing performance measurement. Partner with Marketing, Operations, IT, and Martech teams to define metrics and reporting solutions. Ensure data quality, testing, documentation, lineage, governance, performance, scalability, and cost efficiency across the analytics ecosystem.

Lead migration from SQL Server to AWS Redshift by developing production-grade dbt models, converting legacy T-SQL procedures into modular Redshift SQL, architecting dbt projects, optimizing batch transformations, and mentoring engineers on scalable data modeling and reporting practices.

Build and maintain Databricks-based Lakehouse pipelines (batch and near-real-time) using PySpark. Design Delta Lake schemas, ensure pipeline reliability and cost efficiency, write tests, and collaborate with product and data teams to enable data enrichment and serving for internal and client products.