Associate, Software Engineer

优缺点分析

角色解读

• Progress to senior technical leadership roles, leading complex platform transformation initiatives.
• Deepen domain expertise in financial engineering and front-office trading systems.
• Opportunity to move into architecture or team leadership positions globally.
• Architect and develop cloud-native applications on AWS and Kubernetes for equity derivatives pricing and risk management.
• Build scalable, low-latency systems including microservices, serverless, and event-driven architectures to support real-time trading workflows.
• Collaborate with traders and business teams to deliver end-to-end platform features, from UI to compute infrastructure.
• Participate in technical leadership, managing release cycles and guiding QA and support teams.
• Deep expertise in cloud-native technologies: AWS (EKS, RDS, S3), Kubernetes, and hybrid cloud architectures.
• Strong programming skills in multiple languages: C#, Go, Kotlin
• experience with REST/Protobuf services.
• Solid understanding of equity derivatives (flow, structured, convertibles, delta-one) and trading lifecycle events.
• Knowledge of distributed systems, messaging middleware (Pulsar, Solace), and caching (Redis).

申请策略

• Tailor your resume to highlight cloud transformation projects and measurable outcomes.
• Research Nomura's technology initiatives and upcoming industry trends in equity derivatives to demonstrate genuine interest.
• Highlight hands-on AWS and Kubernetes experience with concrete examples of scaling applications.
• Showcase multi-language proficiency (C#, Go, Kotlin) and microservices architecture projects.
• Include any equity derivatives or trading systems experience, even personal projects or training.
• Emphasize ability to deliver in fast-paced environments and cross-functional collaboration.
• Deepen knowledge of equity derivatives products, especially structured products and risk measures.
• Get familiar with messaging systems like Pulsar or Solace

面试指南

• For architecture questions, use the STAR method: describe the Situation, Task, Action, and Result, emphasizing scalability, reliability, and trade-offs.
• For domain-specific questions, combine theoretical understanding with practical examples to show you can apply financial concepts to engineering decisions.
• For behavioral questions, focus on collaboration with stakeholders and ownership during high-pressure situations.
• Describe a time you designed a cloud-native application on AWS and Kubernetes. What trade-offs did you consider?
• How would you architect a low-latency pricing service for equity derivatives?
• Explain your experience with C#, Go, and Kotlin in high-performance environments. Which one would you choose for a specific use case?
• What are the key risk measures for flow derivatives, and how do they impact system design?
• How do you handle incidents in a production trading system? Provide an example.