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# Data Platform DevOps Engineer

- **Company:** Comunidad de Madrid
- **Location:** Madrid, Spain
- **Contract:** Permanent contract
- **Skills:** Airflow, Data Analysis, Bash Shell, Big Data, Information Engineering, Data Governance, Data Virtualization, Apache Hadoop, Python (Programming Language), Ansible, SQL Databases, Parquet, Data Processing, Scripting, Grafana, Apache Spark, Git, Data Lakes, Deployment Automation, Data Management, Presto, Data Lakehouse, Terraform, Dynatrace, Elk Stack
- **Published:** August 8, 2026
- **Apply:** https://www.jobleads.com/es/job/e5de90f055783758abf14023997ce5036

## About the Role

We are seeking professionals with experience in DevOps environments and data platforms, interested in working with Big Data technologies, automation, observability, and scalable architectures.

## Description

You will be part of an international team responsible for ensuring the availability, performance, scalability, and optimization of a data virtualization platform used by various engineering and analytics teams.

You will contribute to process automation, environment management, platform monitoring, and the continuous improvement of Data Lakehouse architectures.

What will you do?

 * Administer and monitor data platforms in production environments.
 * Manage incidents and provide advanced technical support (L2/L3).
 * Automate deployments and processes through CI/CD pipelines.
 * Optimize platform performance and data query execution.
 * Design and implement observability and monitoring solutions.
 * Participate in the integration and evolution of Data Lake and Data Lakehouse ecosystems.
 * Collaborate with Data Engineering, Analytics, and Architecture teams.
 * Contribute to cost optimization (FinOps) initiatives and continuous improvement.

Requirements

Required tools and technologies:

 * Dremio (or equivalent technologies such as Presto or Trino).
 * Scripting languages (Python, Bash).
 * SQL / Data processing (query execution analysis).
 * Data Lake concepts (Parquet, Iceberg, Polaris, Data Governance).
 * Data Lakehouse / Big Data.
 * Apache Spark (highly desirable).
 * Hadoop.
 * Airflow (orchestration).
 * Observability tools.
 * Grafana.
 * ELK Stack.
 * Dynatrace.
 * Infrastructure & DevOps.
 * Terraform.
 * Ansible.
 * Git.


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