Quant Developer - Eka Finance

Eka Finance; Hong Kong; On-site; HKD 900,000 - 1,300,000; Full time

Job summary

Eka Finance is seeking a skilled Quantitative Developer to join our high‑performing systematic trading group in Hong Kong. The role focuses on building low‑latency infrastructure, data pipelines, and robust live trading systems, collaborating with researchers and engineers.

You will optimize performance, ensure system stability through testing and monitoring, contribute to software design, and explore new technologies to improve workflows and market data usage.

Qualifications

• Master's degree in Computer Science, Engineering, or a related discipline.
• 5+ years of software engineering experience within financial markets (e.g. hedge funds, proprietary trading firms, or investment banks).
• Strong programming expertise in C++ and/or Python, alongside experience with databases such as SQL or KDB.
• Solid grounding in algorithms, data structures, and software architecture.
• Strong analytical thinking, attention to detail, and the ability to work effectively in a collaborative environment.
• Prior exposure to systematic or algorithmic trading systems.
• Familiarity with DevOps tooling and continuous integration / deployment pipelines.
• Experience supporting trading workflows, including risk systems and trade lifecycle troubleshooting.
• Knowledge of market data feeds and APIs (e.g. Bloomberg, Reuters, exchange-native protocols).
• Exposure to cloud platforms such as AWS or Azure.
• Understanding of machine learning techniques or data-driven modelling.

Responsibilities

• Build and enhance low-latency trading infrastructure, including market data pipelines, execution systems, and order management platforms.
• Continuously refine existing systems to improve speed, efficiency, and scalability.
• Maintain system stability and resilience through comprehensive testing, monitoring, and incident response.
• Contribute to code quality through reviews and adherence to best engineering practices.
• Explore and adopt new technologies to improve development workflows and system performance.

Skills

C++

Python

SQL

KDB

Algorithms

Data structures

Software architecture

DevOps

CI/CD

Education

Master’s degree in Computer Science / Engineering or related discipline

Tools

Bloomberg API

Reuters API

AWS

Azure

Job description

We are looking for a skilled and driven Quantitative Developer to join a high-performing systematic trading group. This role is suited to someone with a strong engineering foundation, a curiosity for financial markets, and a desire to build scalable, low-latency trading infrastructure. You will collaborate closely with quantitative researchers and engineers to deliver robust systems that directly support live trading.

Key Responsibilities

• Build and enhance low-latency trading infrastructure, including market data pipelines, execution systems, and order management platforms.
• Continuously refine existing systems to improve speed, efficiency, and scalability.
• Maintain system stability and resilience through comprehensive testing, monitoring, and incident response.
• Contribute to code quality through reviews and adherence to best engineering practices.
• Explore and adopt new technologies to improve development workflows and system performance.

Requirements

• Master’s degree in Computer Science, Engineering, or a related discipline.
• 5+ years of software engineering experience within financial markets (e.g. hedge funds, proprietary trading firms, or investment banks).
• Strong programming expertise in C++ and/or Python, alongside experience with databases such as SQL or KDB.
• Solid grounding in algorithms, data structures, and software architecture.
• Strong analytical thinking, attention to detail, and the ability to work effectively in a collaborative environment.
• Prior exposure to systematic or algorithmic trading systems.
• Familiarity with DevOps tooling and continuous integration / deployment pipelines.
• Experience supporting trading workflows, including risk systems and trade lifecycle troubleshooting.
• Knowledge of market data feeds and APIs (e.g. Bloomberg, Reuters, exchange-native protocols).
• Exposure to cloud platforms such as AWS or Azure.
• Understanding of machine learning techniques or data-driven modelling.