Cloud/DevOps Engineer

at  Riverflex

KEY REQUIREMENTS:

• Experience: 4+ years in Data Engineering, Software Engineering, or DevOps.
• Technical Skills:
• Strong proficiency in at least one programming language relevant to data operations (Python, Terraform).
• Extensive experience with cloud environments (Azure; GCP experience is a plus).
• Proficiency in CSD tools (Azure DevOps, GitHub).
• Hands-on experience with Databricks for data operations.
• Platform-Focused Mindset: Skilled in building resilient, automated platforms, distinct from core data engineering roles.
• Automation Expertise: Proven track record in automation within cloud environments, including dynamic provisioning and scaling of data pipelines.
• Observability & Reliability: Experience designing systems for monitoring, troubleshooting, and maintaining service uptime.
• Hybrid Work Model: Ability to work from the Leiden office minimum 2-3 days per week.

ABOUT THE ROLE:

We’re seeking a skilled Cloud/DevOps Engineer with a strong focus on platform automation, observability, and hardening, to support the Data Analytics Platform team. We are supporting a business unit of one of the biggest global furniture companies in the world to build a data & analytics platform.
In this new role, you will play a crucial part in developing and optimizing the D&A platform. This is a hands-on role requiring expertise in Azure, Python, automation, and infrastructure as code (IaC), allowing dynamic scaling and provisioning for efficient data pipeline operations.

KEY RESPONSIBILITIES:

• Create and maintain Infrastructure as code using Terraform (must) for Data & analytics workloads
• Build and Manage Github Actions pipelines for CI/CD and Release Management workflows.
• Manage Github repos and automation.
• Implement security architecture to ensure data security .
• Implement observability patterns using Azure Monitor Azure Application Insights and Log Analytics Workspace.
• Develop Python functions, classes & packages
• Design and build reusable components, frameworks, and libraries at scale to support analytics products (Python, Spark, SQL)
• Implement automated testing for the Data & Analytics platform
• Support in building Data & Analytics Accelerators
• Define and implement the configuration of Azure PAAS and IAAS services