Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT's collaborative mindset which enables us to solve the most complex challenges. QRT's culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

The Treasury function is the financing backbone of QRT, managing the firm’s funding, collateral, margin, cash balances, and counterparty relationships across geographies and asset classes. The Treasury Analytics team builds the data infrastructure and tooling that gives the desk visibility into financing costs, margin requirements, and cash movements. The systems we build direct inform how the firm deploys its capital.

Join our Treasury Analytics team in London as a Data Engineer, supporting and evolving the data infrastructure that powers Treasury's analytics platform, enabling the desk to analyse and optimise how the firm finances its positions in real time.

Role responsibilities

• Design, build and maintain data pipelines to ingest, transform, and serve Treasury data across multiple sources and analytics systems
• Develop and maintain analytical tools to optimise margin, collateral, cash balances, and financing positions across counterparties
• Build the datasets and APIs that power Treasury's analytics platform, enabling the desk to optimise financing costs, monitor counterparty exposure, and manage liquidity in real time
• Evolve the infrastructure as the desk grows by onboarding new data sources, adapting to new requirements, and improving performance and reliability over time
• Onboard new storage and modelling layers, leveraging the right frameworks for different workloads
• Collaborate with desk leadership, traders, and engineers to implement robust, maintainable solutions

Required experience and skills

• 3–6 years' experience building high-performance intraday data applications, ideally in financial services
• Strong SQL, Python, and data modelling skills
• Understanding of data storage formats and trade-offs
• Experience with query optimisation and performance tuning
• Experience with modern orchestration frameworks (e.g. Airflow), cloud platforms, backend web services and networking protocols
• Knowledge of streaming systems and real-time data applications a plus
• Strong technical communication skills and ability to work effectively with a variety of stakeholders

Location

London