Global Banking & Markets, Quantitative Volatility Trading, Python Developer, Associate Vice President, Hong Kong

Job Summary:

Quantitative Volatility Trading (QVT) is a desk within the Equities Division that engages in quantitative trading and market making in equity derivatives. We are heavily dependent on technology and mathematical modeling to manage risk and continuously quote two-way markets on hundreds of thousands of listed derivatives contracts.

We are seeking a highly skilled and motivated Python Developer to join our team in Hong Kong. This role is crucial for building the foundational infrastructure that underpins our quantitative research and strategy development. The successful candidate will be responsible for designing and implementing a robust backtesting framework and a comprehensive data pipeline from scratch enabling our quantitative researchers to analyze market data and develop trading strategies efficiently.

Importantly prior knowledge of options derivatives Greeks or volatility surfaces is NOTrequired for this role. We are looking for strong technical builders.

Responsibilities include:

• Lead the design development and maintenance of a scalable high-performance backtesting framework using Python.
• Architect and implement end-to-end data pipelines for the ingestion processing storage and retrieval of large-scale financial market data.
• Develop and enforce data quality standards including validation cleansing and transformation processes to ensure data integrity and reliability.
• Collaborate closely with quantitative researchers to gather requirements translate them into technical specifications and integrate their needs into the backtesting and data infrastructure.
• Optimize the performance and efficiency of data processing and backtesting simulations.
• Contribute to the strategic technical direction of the desks quantitative infrastructure promoting best practices in software development and data engineering.
• Ensure comprehensive documentation of all developed systems and processes.

Basic Qualifications:

• Strong academic background in Computer Science Engineering or a related quantitative field.
• Expert-level proficiency in Python programming including experience with relevant libraries for data manipulation (e.g. Pandas NumPy).
• Demonstrable experience in designing and implementing robust data pipelines.
• Proven experience in developing or significantly contributing to backtesting frameworks.
• Solid understanding of database systems (SQL and/or NoSQL) and data modeling.
• Proficiency with version control systems (e.g. Git).
• Strong problem-solving abilities analytical thinking and meticulous attention to detail.
• Excellent written and verbal communication skills with the ability to collaborate effectively in a team environment.

Preferred Qualifications:

• Experience with distributed computing frameworks.
• Familiarity with performance optimization techniques in Python.
• Understanding of software engineering best practices including testing CI/CD and system monitoring.
• Experience working in a fast-paced data-intensive environment.
• While not a prerequisite an interest in financial markets or quantitative trading is a plus.