Our client is a leading global investment management firm seeking an experienced Risk Technology Engineer to join a technology team responsible for building and enhancing systems that support risk measurement, risk reporting, risk monitoring, and quantitative research across the business.

The team partners closely with stakeholders across Risk, Portfolio Management, Investor Relations, and Senior Management, playing a key role in the development of next-generation risk platforms. This is an opportunity to work on large-scale risk systems, support new asset classes, and contribute to the continued growth of a sophisticated investment platform.

• Partner with Risk Management and Quantitative Research teams to deliver scalable technology solutions supporting risk analysis and decision-making.
• Design and develop high-performance applications and services primarily using Python.
• Build core frameworks, tools, and interfaces that enable users to access and interact with risk data efficiently.
• Develop and deploy applications within a cloud-based infrastructure.
• Work with large-scale datasets, focusing on data storage, transformation, and distribution.
• Contribute to the architecture and design of next-generation risk technology platforms.
• Collaborate with technical and non-technical stakeholders across the business in a fast-paced environment.
• Gain exposure to risk modelling and fixed income markets while supporting business-critical initiatives.

• 7+ years of software engineering experience within financial services.
• Strong commercial experience developing scalable, production-grade applications in Python and/or C#.
• Experience working with large datasets and data-intensive systems.
• Strong knowledge of SQL and database technologies.
• Proven ability to take projects from initial concept through to production delivery.
• Excellent problem-solving skills with the ability to work independently.
• Degree in Computer Science, Mathematics, Engineering, or a related quantitative discipline.

• Experience within asset management, hedge funds, or fixed income environments.
• Exposure to time-series datasets and analytical platforms.
• Experience with cloud-native technologies and modern infrastructure tooling, including AWS services, Kubernetes, and Docker.
• Knowledge of risk systems, quantitative analytics, or financial markets is advantageous.