Trading Analytics & PnL Engineer

Qube Research & Technologies; Hong Kong; On-site; HKD 700,000 - 1,200,000; Full time

Job summary

Qube Research & Technologies in Hong Kong is seeking a highly capable data-driven professional to join its trading analytics team. You will help build and enhance PnL infrastructure, dashboards, and reporting tools used by traders and researchers.

You will collaborate with front-office stakeholders, design scalable data pipelines, and contribute to automation and data architecture improvements across the platform.

Qualifications

• Strong Python and SQL skills with a focus on scalable solutions.
• Background in Engineering, CS, Math or Quantitative Finance.
• Exposure to AWS is a plus.
• Familiarity with DevOps practices and Python packaging is a plus.
• Strong analytical and problem-solving abilities.
• Interest in financial markets, trading systems, and data infrastructure.
• Excellent communication and ability to work with stakeholders.

Responsibilities

• Act as a trusted partner to traders and researchers, providing insights on PnL and strategy performance.
• Lead investigations into PnL discrepancies and trading anomalies.
• Design and develop dashboards and analytical tools for portfolio monitoring.
• Contribute to reporting platforms through automation and data architecture improvements.
• Perform reconciliations between trading, valuation, and risk systems.
• Work with technology teams to build scalable data pipelines and tools.

Skills

Python

SQL

Data analysis

Problem solving

Communication

Education

Engineering, Computer Science, Mathematics, Quantitative Finance or related field

Tools

AWS

DevOps

Python packaging

Job description

Qube Research & Technologies in Hong Kong is seeking a highly capable data-driven professional to join its trading analytics team. You will help build and enhance PnL infrastructure, dashboards, and reporting tools used by traders and researchers.

You will collaborate with front-office stakeholders, design scalable data pipelines, and contribute to automation and data architecture improvements across the platform.