Asset Management - Senior Data Engineer - VP

Job Summary: As a Data Engineer in the AMC Tech team, you will be responsible for designing, building, and maintaining the data infrastructure that supports our data platform, with a strong focus on fund management business logic, engineering delivery, and platform-level development. You will collaborate closely with business and technology stakeholders to ensure that our data systems are robust, scalable, and aligned with business goals. Job Responsibilities: Responsible for the project management and development of the core modules of the company's 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. Proven experience in designing and developing data API interfaces. Expert-level proficiency in SQL, with extensive experience in relational databases (e.g., Oracle) and MPP data warehouses. Strong hands-on experience with big data technologies, including Apache Spark, Hadoop, for distributed data processing. Experience in real-time or streaming data processing using tools such as Kafka, Flink. Experience in designing and optimizing ETL/ELT workflows and data pipelines. Solid understanding of data modeling principles and data architecture patterns. Deep knowledge of fund management business logic and related data domains.