Job Description

We are looking for a highly skilled and self-driven Data Engineer to strengthen our quantitative research and investment technology infrastructure. The successful candidate will work closely with members of the Investment Team to design, build, and optimize in-house dashboards, data pipelines, and analytical workflows ensuring seamless data accessibility, operational efficiency, and the delivery of robust, high-quality data across the research and investment processes.

Key Responsibilities:

Data Infrastructure & Management. Design, maintain, and enhance the team's core data architecture, covering structured and alternative datasets. Build robust ETL workflows for efficient data ingestion, transformation, and storage across databases and cloud-based environments. Implement version control, data lineage tracking, and quality assurance frameworks to ensure auditability, reproducibility, and long-term data integrity. Monitor and optimize data pipelines with automated validation, logging, and performance diagnostics to minimize manual intervention. Quantitative Dashboards & Analytics Tools. Develop and maintain scalable, interactive dashboards for real-time monitoring of macro, multi-asset classes, factor, strategy, and portfolio-level analytics. Collaborate with the Investment Team to translate research requirements into well-structured, maintainable data solutions with clear documentation. Support portfolio construction and evaluation workflows by integrating attribution, risk, and optimization models into the analytics and reporting infrastructure. Model Development & Quantitative Support. Build and maintain data and computational frameworks that enable quantitative models for performance attribution, risk decomposition, and portfolio optimization. Ensure that model pipelines are seamlessly connected to the core data infrastructure for consistency, scalability, and reproducibility. Technical Enablement & Cross-Functional Support Provide tools and automation scripts to enhance data access, research workflows, and reporting processes across teams. Coordinate with external vendors on cloud infrastructure, compute scaling, and cybersecurity to maintain a secure and high-performance research environment. Maintain internal documentation repositories and knowledge platforms to support onboarding, collaboration, and business continuity.

Requirements:

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. 3-5 years of practical experience in data engineering, quantitative analytics, or data science in a production or research environment. Strong proficiency in Python and SQL, and familiarity in tools and frameworks such as Airflow, Flask, Streamlit, Pandas. Hands-on experience with cloud platforms such as AWS, GCP, or Azure, including data storage and pipeline deployment. Strong analytical and problem-solving skills, with the ability to manage and process large-scale, complex datasets efficiently. * Self-driven, collaborative, and able to thrive in a cross-functional, bilingual (English and Mandarin) environment.