Analytics Platform Engineer: JupyterHub on Kubernetes

Ørsted; Gentofte Kommune; On-site; DKK 647,000 - 971,000; Full time

Benefits offered by this job

Pension 11%

Health insurance

Five weeks holiday + 10 days off

Holiday allowance 3%

Vitality program

Phone + laptop

Staff club

Canteen

Job summary

Ørsted is seeking a Business Application Responsible for the Analytics Workspace, a JupyterHub-based platform built on cloud Kubernetes. You will co-develop with Commercial Digital Technology and guide testing and delivery to align with analytics, algo, and model development needs.

You will work within Data Science & Platforms, collaborating with quantitative developers, analysts, model developers, and technology partners to build a scalable platform supporting trading and revenue analytics.

Qualifications

• Proficient Python development with solid engineering practices.
• Hands-on experience with JupyterHub-based platforms and Kubernetes.
• Experience with Airflow and cloud infrastructure in Azure/AWS or similar.
• DevOps or MLOps topics; platform design and delivery.
• Strong collaboration with stakeholders and clear communication.

Responsibilities

• Owning the Analytics Workspace on the Trading & Revenue side.
• Co-developing platform capabilities with Commercial Digital Technology.
• Enable strong platform foundations for analytics, algo development, and ML use cases.
• Support onboarding and day-to-day platform support for users.
• Maintain best practices, tools, and templates for analytics engineering.
• Drive platform roadmap, migrations, and adjacent ML tooling.

Skills

Python

JupyterHub

Kubernetes

Airflow

Cloud infra

DevOps

Communication

Tools

Jenkins

Azure

AWS

CI/CD

Job description

Ørsted is seeking a Business Application Responsible for the Analytics Workspace, a JupyterHub-based platform built on cloud Kubernetes. You will co-develop with Commercial Digital Technology and guide testing and delivery to align with analytics, algo, and model development needs.

You will work within Data Science & Platforms, collaborating with quantitative developers, analysts, model developers, and technology partners to build a scalable platform supporting trading and revenue analytics.