We are still looking for the very Top Talent…and we would be delighted if you were to join our team
More in details,

UST

is a multinational company based in North America, certified as a

Top Employer

and

Great Place to Work

company with over

35,000 employees all over the world

and presence in more than

35 countries . We are leaders in digital technology services, providing large-scale technological solutions to some of the world's leading organizations.

What we look for?
We are seeking a highly skilled

Data Engineer

with strong expertise in modern cloud data platforms, data transformation, orchestration, and automation.
The adecuado candidate will have hands-on experience with

Snowflake, dbt, Apache Airflow, Python, Azure Cloud , and containerized deployments using

Docker and Kubernetes .
You will play a key role in designing, developing, and optimizing scalable data pipelines and analytics platforms that support enterprise-wide data initiatives and business decision-making.

⚙ Key Responsibilities:
Design, develop, and maintain scalable data pipelines using





Snowflake

and

Azure

services.
Build and optimize robust data transformation frameworks using

dbt .
Develop, manage, and monitor

Apache Airflow DAGs

for workflow orchestration and scheduling.
Create reusable

Python

scripts and automation utilities for data processing and integrations.
Implement CI/CD practices and deployment strategies using

Docker

and

Kubernetes .
Ensure data quality, governance, security, and performance optimization across data platforms.
Collaborate with architects, business stakeholders, analysts, and engineering teams to deliver reliable data solutions.
Troubleshoot production issues and drive continuous improvements in platform reliability and scalability.
Support cloud-native architectures and data modernization initiatives.
Contribute to data governance, security best practices, and operational excellence.

Mandatory Requirements:
5+ years of experience in

Data En

📌 Data Engineer (Madrid)
🏢 UST España & Latam
📍 Madrid