Top Data Engineer Jobs in Madrid

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.

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.

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 the finance data domain by developing financial data products and models, integrating and improving data quality, monitoring freshness and completeness, and resolving reconciliation issues. Build reports, alerts, automations, and LLM agents using Python, SQL, Snowflake, Airflow, and dbt. Communicate data issues clearly to Finance leadership and ensure financial reporting and planning data remains accurate and reliable.

You will design and develop data solutions, create ETLs using Spark, work on cloud projects, and build scalable data pipelines.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

Maintain and expand a high-scale data platform: build and run data pipelines, implement DataOps, collaborate with analytics teams, ensure data quality and security, and design enterprise data models.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Designs and maintains scalable, real-time data infrastructure, including ingestion, pipelines, storage, analytics, data models, APIs, and observability. Develops AI-powered workflows and integrates AI services into cloud-native SaaS products. Owns features through production, ensures data quality and reliability, collaborates with cross-functional engineering and product teams, and supports production-scale systems using streaming, cloud, containerization, and relational database technologies.

Design and build scalable real-time and batch data pipelines for programmatic advertising, integrating SSP, DSP, ad server, and reporting data. Develop data models, warehouses, lakes, ETL/ELT workflows, auction analytics, yield optimization, and quality monitoring. Support BI teams with performant datasets while ensuring GDPR and CCPA compliance. The role requires ownership of architecture through deployment and collaboration with engineering and analytics teams.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Own, run and evolve the Data Privacy Orchestrator that manages data retention, deletion, anonymization and pseudonymization. Build safe-by-default platform abstractions, self-serve tooling, docs and observability; operate production (deploys, alerting, on-call), align legal/security/product, and extend platform coverage across data stores and third parties as the company expands.

Build and maintain Snowflake-based data assets and metrics layers using dbt, SQL, and Python. Design reliable data models and pipelines, enable self-service analytics, collaborate with stakeholders, document definitions and best practices, and deliver scalable analytics solutions.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Lead design and implementation of scalable, real-time data infrastructure. Build and optimize production-grade streaming pipelines, employ spec-driven development and agentic AI workflows, implement testing/monitoring, take ownership of systems, and mentor junior engineers.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Design, build, and maintain streaming ingestion pipelines and migrate SaaS pipelines to in-house infrastructure. Deliver resilient, idempotent data into Snowflake, improve AWS streaming stack, manage IaC with Terraform, monitor and troubleshoot production pipelines, and collaborate on safe data access for AI tooling.

Design and build a scalable cloud data platform on Google Cloud, create reliable batch and streaming ETL/ELT pipelines, enforce data quality and observability, automate IaC/CI-CD, and support analytics, ML, and product teams with high-quality data.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.