Automation direction
I define how manual operational work becomes repeatable systems, from the infrastructure choices to the automation that runs on top.
I build automation that takes the manual work out of the cloud.
Daniel Orelius sets technical direction for automation in cloud environments, combining hands-on delivery with architecture, infrastructure, and operational judgment.
I am a technical leader and hands-on builder working across Azure, DevOps, PowerShell, infrastructure, and AI. The pattern is consistent: understand the operation, design the platform, then automate the repeated work until teams can move with more confidence.
Focus areas
Technical direction, operational clarity, and automation that survives real use.
Cloud operations
My work sits in Cloud Operations, delivering Azure services through operational responsibility and consultancy.
Architecture and infrastructure
I care about the shape of the platform before the first script is written: ownership, deployment flow, reliability, and long-term maintainability.
AI as a practical tool
AI is a current focus because it expands what automation can do when it is applied with engineering judgment and curiosity.
Experience
From classic infrastructure to Azure-focused automation leadership.
Nov. 2025 to present
Lead Automation Specialist
Mjølner Informatics A/S
Technical direction for automation in Azure-focused cloud operations after Automize became part of Mjølner Informatics A/S.
Sep. 2023 to Nov. 2025
Automation Tech Lead, Automation Specialist, Cloud Consultant
Automize A/S
Azure, DevOps, architecture choices, and the automation standards that shape daily delivery across cloud work.
Aug. 2017 to Sep. 2023
Infrastructure Specialist and apprentice roles
Cloud Factory A/S
Classic infrastructure work across data center hardware, Hyper-V, Windows Server, Nutanix Hyper-Converged, and PowerShell automation.
Skills
Strongest in Azure, PowerShell, DevOps, AI, and infrastructure.
- Azure
- PowerShell
- DevOps
- Infrastructure
- GitHub
- Azure DevOps
- Bicep
- Containers
- Claude
- ChatGPT
- Codex
- ServiceNow
- Architecture
- Technical leadership
- Fast learning
- Innovation
Work pattern
Learning by doing, then turning what works into durable systems.
Map the manual work
Find the repeated operational steps, ownership gaps, and failure points before designing the solution.
Choose the platform shape
Define the architecture, infrastructure, tooling, and delivery path that can support the work long term.
Build the repeatable layer
Use scripts, pipelines, infrastructure as code, and AI-assisted workflows to remove routine manual effort.
Let's talk automation.
I am interested in automation, AI, cloud operations, and the kind of technical work where practical ideas need to become real systems.