Top Data Engineer Jobs in Madrid

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.

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 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.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

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.

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.

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

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

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Lead design and implementation of a cloud data platform on Azure. Build ingestion and transformation pipelines (Data Factory, Databricks), enable downstream reporting/APIs (SQL, Cosmos, Synapse), automate deployments (ARM templates, DevOps), advise on data models, integration, security, and BI, and collaborate with architects and engineers to deliver short- and long-term enterprise data solutions.

Lead design and implementation of a cloud data platform: plan milestones, build Azure Data Factory pipelines and Databricks transformations, integrate diverse data stores (SQL, Cosmos DB, Synapse), automate deployments (ARM templates, DevOps), advise on data models, APIs, security, and BI, and provide short-term solutions and best practices for enterprise data needs.

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

Build end-to-end features for a data orchestration platform across Java/Micronaut backend and Vue 3/TypeScript frontend. Implement data-aware orchestration (assets, lineage, freshness, quality), create integrations/plugins (dbt, Fivetran, Airbyte, warehouses), improve UI (flow editor, topology, dashboards), collaborate on 8-week releases, review PRs, and engage with the open-source community.

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.

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.

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

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Design, build, and scale low-latency market data systems and public APIs. Influence architecture, implement backend services, maintain quality and tests, participate in reviews, troubleshoot incidents, and optionally serve on-call.

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.