Description

• Data Engineering & Pipelines: Design and implement robust ETL (Extract, Transform, Load) pipelines to process and deliver large-scale datasets efficiently.
• Data Quality & Monitoring: Develop and maintain data Quality Control (QC) systems and monitoring tools, ensuring the integrity and reliability of data across production and research environments.
• Collaboration & Integration: Partner with internal research and technology teams to seamlessly integrate new and diverse datasets into existing systems.
• Quantitative Analytics Tools: Develop frameworks for the systematic back-testing and signal analysis of quantitative trading strategies.
• Portfolio Analysis: Create sophisticated analytics tools and interactive dashboards for comprehensive portfolio risk assessment and performance attribution.
• Infrastructure Management: Oversee the configuration and ongoing maintenance of container orchestration environments.

• Education: Bachelor's degree in Computer Science, Engineering, or a related technical discipline.
• Programming Languages & Libraries: High proficiency in C/C++ and Python, along with essential data processing libraries (e.g., Pandas, Polars, NumPy).
• Database Technologies: Expertise in various database products, including MySQL, MongoDB, and ClickHouse.
• Backend & Concurrency: Strong knowledge of backend data storage technologies, multi-processing, and multi-threading programming techniques.
• Distributed Systems: Practical experience with parallel computing, distributed systems, and cloud service providers such as AWS.
• Financial Product Knowledge: Familiarity with asset classes including equities, derivatives, and fixed income.
• Soft Skills: Strong organizational skills, keen attention to detail, excellent communication (written and verbal), self-motivation, and the ability to work both independently and collaboratively within a team.