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.