As a Data Engineer in the AMC Tech team, you will be responsible for the uplifting of the investment data platform. You will ensure the data is accessible, reliable, and integrated to support advanced analytics and business intelligence across the investment process.

 

Job Responsibilities:

• Responsible for the project management and development of the core modules of the company's investment data platform;
• Promote the company's financial technology work, keep up with the development of Fin-tech, continuously absorb and introduce new technology systems, empower business, and improve the company's competitiveness
• Formulate technical routes and overall framework to quickly break through technical difficulties encountered during the development process
• Communicate with business users on demand and complete demand analysis, system design, coding development and project management;
• Responsible for continuously tracking the progress of the project, clearly understanding and submitting the results of each stage, identifying the risks in the project, evaluating and eliminating risks, and ensuring the quality of the project;
• Assist in the development of system specifications, and be responsible for checking suppliers and self-developed codes in accordance with the specifications.

 

Required qualifications, capabilities, and skills:

• Full-time bachelor degree or above in computer-related majors, with 3 years of data engineering experience and independent complete project development experience.
• Expert-level skills in SQL and experience with relational databases (e.g., Oracle) and MPP data warehouses.
• Solid experience with big data technologies (e.g., Spark, Hadoop) for distributed data processing.
• Experience in real-time/streaming data processing (e.g., Kafka, Flink).
• Experience in designing and optimizing ETL/ELT workflows and data pipelines.
• Strong understanding of data modeling principles and data architecture patterns.
• Have a deep understanding of the fund industry's investment related business, and related data domains.