Software Engineer

We are seeking a highly skilled Quantitative Software Developer to join our front office technology team focused on building and enhancing risk platforms for a leading trading or investment firm. The ideal candidate will have strong expertise in Java development and deep experience working within quantitative or risk-focused environments, particularly in designing, developing, and optimizing systems that support real-time and end-of-day risk calculations.

You will collaborate closely with quants, traders, and risk managers to develop scalable, high-performance platforms that process large datasets and support complex pricing and risk analytics across asset classes.

Key Responsibilities

• Design, develop, and maintain Java-based risk platform components that support pricing, market data integration, and risk analytics.
• Collaborate with quantitative analysts and model developers to integrate risk models into production systems.
• Build robust data pipelines and interfaces for market data, trade data, and risk sensitivities.
• Ensure low-latency and high-throughput performance across the platform.
• Participate in architectural decisions for the evolution of the risk platform, including microservices, cloud migration, or messaging integration.
• Support daily operations and participate in the on-call rotation for production risk systems.
• Write clean, testable, and well-documented code; contribute to CI/CD practices.

Required Qualifications

• Bachelor's or Master’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field.
• 3+ years of experience in Java software development, ideally within financial services or a trading environment.
• Proven experience building or supporting risk platforms, pricing systems, or valuation engines.
• Strong knowledge of object-oriented programming, data structures, and design patterns.
• Familiarity with market risk, credit risk, or counterparty risk concepts.
• Experience with messaging systems (e.g., Solace, Kafka, or RabbitMQ) and distributed architecture.
• Solid understanding of multi-threaded and low-latency system design.
• Exposure to quant libraries, risk factor decomposition, or sensitivities is a strong plus.