Data Engineer (Snowflake/Azure) - Remote

• 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.

• 5+ years of experience in Data Engineering roles.
• Minimum 3 years of hands-on experience with Snowflake development and administration.

• Strong expertise in: *

• Snowflake Development & Administration
• Data Modeling (Dimensional Modeling and Data Vault concepts)
• Snowflake Performance Tuning & Optimization
• Snowflake Security, RBAC, and Data Governance

• Hands-on experience with dbt (Data Build Tool): *

• Reusable transformation models
• Data quality testing
• Documentation and best practices
• Strong experience developing and managing Apache Airflow workflows and DAGs.
• Advanced Python scripting and development skills.
• Experience with API integrations and automation frameworks.
• Solid knowledge of Microsoft Azure services.

• Experience with: *

• Azure Storage
• Azure Functions
• Azure Key Vault
• CI/CD Pipelines
• Docker Containerization
• Kubernetes Deployment & Orchestration
• Experience delivering end-to-end cloud data solutions in Azure environments.
• Experience working within Agile delivery teams.

Nice to Have:

• Azure Data Factory
• Azure Databricks
• Terraform / Infrastructure as Code (IaC)
• Snowpark
• Apache Kafka
• Azure Event Hubs
• Streaming and real-time data architectures
• Exposure to DevSecOps practices
• Experience in Financial Services or Asset Management environments
• Cloud-native architecture and modernization projects
• Strong problem-solving skills and automation mindset

• 23 days of Annual Leave plus the 24th and 31st of December as discretionary days!
• Numerous benefits (Health Care Plan, Internet Connectivity, Life and Accident Insurances).
• Flexible Benefits Program (Meals, Kindergarten, Transport, Online English Lessons, Health Care Plan…).
• Free access to several training platforms.
• Professional stability and career development plans.
• UST also compensates referrals, allowing you to benefit when referring professionals.
• The option to choose between 12 or 14 salary payments per year.
• Real Work-Life Balance measures (flexibility, WFH or remote work policy, compacted hours during summertime, etc.).
• UST Club Platform discounts and gym access discounts.

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 ideal 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.