Financial Analytics Engineer - Python, Kubernetes & Cloud

Experis (Austria); Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

Experis in London is seeking a Financial Engineering Specialist to join a leading global bank's Analytics, Digital & Architecture team. You will help design, build and deploy sophisticated models and analytics capabilities to empower strategic decision-making across Global Finance.

You’ll work at the intersection of software engineering, data analytics and financial modelling, leveraging Python, containers and cloud tech to deliver scalable, production-ready solutions across the organisation.

Qualifications

• Extensive Python experience including APIs and microservices.
• Experience with Docker and Kubernetes.
• Experience with large-scale data processing.
• Familiarity with Google Cloud Platform (GCP).
• Experience with C++ is advantageous.
• Knowledge of the software engineering lifecycle.

Responsibilities

• Partner with modelling teams to translate complex financial models into robust software solutions.
• Build and maintain production-grade models with CI/CD, automated tests and stable deployments.
• Develop analytics libraries and Python applications, APIs and microservices.
• Leverage Docker, Kubernetes and cloud tech to ensure scalable deployment.
• Automate development workflows to reduce manual effort and risk.
• Collaborate with stakeholders across business, IT and data science.
• Provide BAU support and testing during model deliveries.

Skills

Python

Docker

Kubernetes

Data processing

Cloud (GCP)

C++

Software engineering

APIs & microservices

Tools

CI/CD

Job description

Experis in London is seeking a Financial Engineering Specialist to join a leading global bank's Analytics, Digital & Architecture team. You will help design, build and deploy sophisticated models and analytics capabilities to empower strategic decision-making across Global Finance.

You’ll work at the intersection of software engineering, data analytics and financial modelling, leveraging Python, containers and cloud tech to deliver scalable, production-ready solutions across the organisation.