Connor Mallon

Background

About Connor Mallon

Software Engineer at Investec via First Derivatives, working on Trade Transaction Reporting systems across MiFID II, EMIR, Bloomberg and SFTR. My current focus is building robust, data-driven backend services in C# and.NET on Azure that support regulatory compliance, data quality, and real-time financial workflows. Master’s in Computer Science from Queen’s University Belfast, specialising in distributed systems, blockchain concurrency, and high-performance computing. Since then, I have applied that foundation in production environments within financial services, developing scalable systems that operate under regulatory and performance constraints. My experience spans:• Financial Systems and Regulatory Reporting. Building and enhancing reporting pipelines, validation frameworks, and reconciliation tools across trading systems • Backend Engineering. Designing low-latency, scalable services using C#.Net • Cloud and Distributed Systems. Developing cloud-native applications on Azure, including hands-on use of Service Bus, Resource Groups, Blob Storage, Queues, and containerised workloads with Podman. • Blockchain and Security. Experience with Hyperledger Fabric, smart contracts, and privacy-enhancing technologies including zero-knowledge proofs and homomorphic encryption • Data and Performance Engineering. Strong grounding in parallel computing, optimisation, and data-driven architectures Futhermore worked in research-focused environments exploring privacy-enhancing technologies, including post-quantum cryptography and secure computation, with a focus on real-time and distributed applications. Outside of engineering, I’m a 1st Degree Black Belt in ITF Taekwondo, which has helped me build discipline, focus, and a strong mindset that I bring into how I solve problems and work with others. I am particularly interested in opportunities at the intersection of fintech, distributed systems, and cloud-native architecture, where I can contribute to building high-performance, resilient systems at scale.