Head of Trading Infra

原始資料於 2026-11-01 查閱；內容可能已更新，請查看原始職位。

Requirements
Must-have
Bachelor's degree or above in Computer Science, Financial Engineering, Mathematics, or a related quantitative or technical discipline.
Experience designing or leading normalized Feed Handler and Order Entry Gateway frameworks across multiple markets, including protocol abstraction, data standardization, market-specific adaptation, replay validation, and production deployment. Equivalent experience owning end-to-end productionization of Feed Handler / Gateway systems for major markets is also acceptable.
Strong proficiency in C++, with solid experience in low-latency system development and deep familiarity with memory management, concurrency models, network I/O, protocol parsing, CPU/cache behavior, latency measurement, and performance tuning on performance-critical paths.
Deep understanding of low-latency path design and diagnostic systems for trading connectivity, with the ability to guide the team in building tools and capabilities for packet-level / pcap analysis, data replay, protocol-level issue diagnosis, end-to-end latency analysis, failure reproduction, and quality validation.
8+ years of relevant experience at a quantitative trading fund, exchange, market maker, or similar institution, including at least 3 years of people management or technical leadership experience.
Strong understanding of how trading infrastructure impacts strategy performance, trading stability, and business outcomes; ability to effectively align requirements and priorities across trading, research, risk, and engineering teams, and lead the team to deliver efficiently in a fast-paced environment.
Nice-to-have
Deep experience in specific markets, with priority in the following order: US equities > APAC equities > CME and other futures markets > others.
Experience working with large-scale raw exchange market data, including raw historical data parsing, backfilling, completeness validation, replay, quality governance, and online/offline consistency validation.
Familiarity with Python and common data analysis tools for market data validation, issue investigation, research support, and production diagnostics.
Familiarity with major exchanges, raw market data vendors, and connectivity service providers, including their product coverage, data quality, support capabilities, and cost structures; experience evaluating vendors and making technical selection decisions.