- Could you introduce yourself and tell us about you? Your current / recent jobs and employers? Previous companies and jobs?
He has been in software engineering for 12 years, with 8 years focused on systematic trading, mostly in options and derivatives. He started in C++ low-latency development at XXXX, then moved into crypto exchange connectivity and market making, and is now Director of Engineering for the options trading business at XXXX. He leads 18 engineers across Hong Kong and Singapore, covering the trading platform, market data, pricing and risk, and exchange connectivity, and works closely with the head of trading and the quant research team. His coverage is APAC, with listed derivatives in Hong Kong, Singapore, and Japan, plus crypto venues such as Deribit, OKX, and Bybit. In his current role, he led the rebuild of the options trading platform from a C++ and Python stack into a Rust-native platform over 20 months. It covers automated quoting, execution, theoretical pricing, and real-time risk across about 1,500 live option instruments. Before that, at XXXX, he led the engineering team behind the options market making stack, and at Nodeflow Digital he built the exchange gateways to the major crypto venues.
- What skill(s) / experience would you self-describe as strongest or specialist in?
His strongest skill would be building low-latency options trading systems end to end in Rust and C++, from market data handling and pricing to quoting, execution, and risk controls. Another one is his leadership as he currently leading engineering teams in a trading environment. He is also good at bridging traders, quants, and engineers, and turning trading requirements into a clear platform roadmap. In the current role, he cut median tick-to-quote latency from about 450 microseconds to under 60 microseconds after the Rust rebuild, reached 99.98% quoting uptime, and reduced severity-1 production incidents by 55% year on year. The platform handles 40K+ quote updates per second at peak and about 300M USD of average daily notional.
