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Build and manage multiple datasets, create automated ETL pipelines, integrate datasets, and ensure data quality for QRT's research and trading platform.

As a Front Office Quantitative Developer, you will enhance quant technology for algorithmic trading, collaborating with traders and quants to support 24/7 strategies.

You will design and implement low-latency trading systems, optimize system performance, and ensure reliability at scale within the Core Trading Platform team.

This role focuses on maintaining system reliability and performance, implementing automation tools, and collaborating with teams to optimize operations within QRT.

Deliver analytics of trading data, develop reports/tools for risk management, and contribute to portfolio management and strategic analysis.

Develop and enhance core technology for systematic trading, including front-office systems and automation of trading strategies. Collaborate with traders and quants.

Develop and maintain research and trading software to support quantitative strategies: automate data cleaning, build research frameworks, productionise strategies, improve research workflows, and promote software engineering practices across research teams while working closely with researchers and portfolio managers.

Develop and maintain production-grade analytical and trading applications for the Treasury Desk. Integrate and onboard datasets, optimize performance and latency, design scalable software and data architectures, enable real-time analytics and AI-enabled workflows, and collaborate with stakeholders across the software development lifecycle to support treasury financing and trading decisions.

Lead end-to-end delivery of multiple trading and enterprise infrastructure projects, including colocation site builds, datacenter provisioning, exchange connectivity, and compute allocation. Coordinate with networking, data centre operations, vendors, brokers and exchanges, track progress in JIRA, and ensure resilient, high-performance platforms delivered on time and to quality standards.

Design, build and maintain production-grade Python services and scalable APIs supporting risk analytics. Collaborate with Risk Managers and Quants to productionise models, ensure performance, reliability, observability, and improve engineering standards using cloud-native practices and AI-assisted tools.