Job Description:

• Design and develop data pipelines architecture using SQL
• Automate and streamline the data process to improve overall data quality and efficiency for different users who can utilize the data with security, accuracy and celerity
• Work with internal users and external vendors to resolve data-related issues and support data infrastructure needs
• Create and maintain the analytics infrastructure of a data platform that enables various data functions
• Maintain data analytics tools to provide users more useful data in a usable format
• Comply with established standards and change deployment policy to meet regulatory and audit requirements
• Participate in project lifecycle including gather user requirements, analyze user requirements, solution design and development, UAT, documentation, etc.

Requirements:

• Bachelor Degree or above in Computer Science or equivalent
• Minimum 1 to 2 year working experience in SQL and relational databases, as well as working familiarity with a variety of databases, such as MySQL, MongoDB, PostgreSQL, etc.
• Experience in programming language with Python, VBA, Java, C/C++, .Net
• Proficiency in Microsoft products including Microsoft office, Power Automate, Power BI
• Excellent command of written and spoken English, Mandarin and Cantonese skills
• Excellent in problem-solving and analytical skills

Please email your resume together with a cover letter and your expected salary to us.  Please also quote the applied position on the email subject.

Zeal Asset Management Limited is a specialist asset management company based in Hong Kong with a global clientele. Zeal manages award winning hedge funds and retail funds that invest in the Greater China region and has received accolades from Morningstar, AsianInvestor, HFM, and Eurekahedge. Currently, its biggest client is a European sovereign wealth fund, and other clients include pension funds, financial institutions, foundations, family offices, as well as individual investors from Hong Kong and China. Zeal is firmly committed to bottom-up stock-picking based on original fundamental research.