Top Data Engineer Jobs in Madrid

Develop and maintain high-volume data processing pipelines using cloud and distributed-data technologies. Build jobs and libraries supporting data scientists and analysts, while implementing monitoring, alerting, quality checks, testing, and documentation. Contribute to data platform architecture and collaborate with cross-functional teams. The role requires expertise in SQL, data modeling, workflow orchestration, Python, ETL/ELT design, Spark, and AWS.

Develop high-volume distributed data processing jobs using Redshift, Athena, Iceberg, Spark, and Airflow. Collaborate with data scientists, analysts, and product managers to turn methodologies into products, improve system performance, and support data workflows. Mentor junior engineers on architecture and best practices. The role requires strong Python programming, SQL, database, data modeling, and modern lakehouse expertise, with AWS and streaming experience preferred.

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.

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.

Lead design and evolution of the data platform: architecture, schema design, governance, and infrastructure on AWS using Terraform. Ensure AI-ready, high-quality data; set standards, mentor engineers, and drive cross-team technical direction.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Build and own end-to-end data foundations, including ingestion pipelines, dbt models, data marts, semantic layers, and AI-ready datasets. Integrate fragmented marketing, advertising, ecommerce, analytics, and CRM data across a multi-tenant Snowflake environment. Manage platform API changes, data quality, restatements, performance, and client-specific modeling. Collaborate with product, engineering, AI, BI, and client teams while using AI coding agents to accelerate reliable production development.

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.

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

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

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.

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.

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 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.

Build a greenfield data platform and unified Lakehouse integrating high-volume payment, marketing, and operational data across AWS and GCP. Design ingestion pipelines, create dbt medallion-layer transformations in BigQuery, and own Airflow orchestration, lineage, backfills, retries, alerting, infrastructure as code, and cost controls. The role requires autonomous ownership in a fully remote, asynchronous environment, with data mesh and Starburst Galaxy experience as preferred qualifications.

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.

Design and implement Azure-based data infrastructure: build ETL/ELT pipelines, data storage (Data Lake/Warehouse), optimize data delivery, develop analytics and tooling, and collaborate with stakeholders to deploy and support data solutions.

Design and implement Azure-based data infrastructure and pipelines (hub-and-spoke). Build ETL/ELT processes, data storage (Data Lake/Warehouse), analytics tools, collaborate with stakeholders, and optimize data delivery and automation.

Design and build scalable Azure-based ETL pipelines, data storage and analytics solutions. Collaborate with stakeholders to optimize data delivery, automate processes, support infrastructure, and create analytical tools to surface business insights.

Design, build and optimize Azure-based ETL/data pipelines and analytics infrastructure (ADF, Synapse, Data Lake/Storage). Implement scalable data solutions, automate processes, support data delivery, and create analytical tools. Collaborate with cloud, governance, and data stakeholders to deploy and maintain data solutions.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project 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.