Design, implement, and maintain robust data pipelines and infrastructure to support LME integration across data warehouses or critical to ensure reliability and scalability.
Ensure the robustness and quality of data workloads using Python/Java/Scala and modern data engineering practices, including automated validation, monitoring, and comprehensive testing.
Ensure all technical documentation is accurate, up-to-date, and accessible to relevant stakeholders.
Provide internal data analysis and reporting to support business and technology objectives.
Act as a liaison between technical teams and non-technical stakeholders, ensuring clear and effective communication of project status, risks, and requirements.
Develop and maintain database architectures, including data lakes and data warehouses.
Ensure data quality and consistency through data cleaning, transformation, and validation processes.
Lead incident analysis and root cause investigations for data-related issues, implementing improvements to enhance system stability and performance.
Evaluate possible solutions and designs to establish best approach in terms of customer outcome, architecture and cost. Including prototyping, technical spikes and proofs of concept.
Design, implement and support scalable and robust data pipelines to support analytics and data processing needs.
Implement test or process automation, Test Driven Development, Continuous Integration and Continuous Delivery as required and support the team in implementing best practices.
Requirements
Experience: Minimum 3 years in software engineering, with demonstrable involvement in at least one production-grade data system within financial services or a similarly regulated industry.
Data Quality: Proven ability to validate and govern data pipelines, ensuring data integrity, correctness, and compliance.
Full-Stack Engineering: Hands-on experience with Java (Spring Boot), React (optional), and Python, covering backend, frontend, and data engineering.
Data Engineering Tools: Proficient with modern data engineering and analytics platforms (e.g., Apache Airflow, Spark, Kafka, dbt, Snowflake, or similar).
DevOps & Cloud: Experience with containerisation (Docker, Kubernetes), CI/CD pipelines, and cloud platforms (e.g., AWS, Azure, GCP) is highly desirable and increasingly standard in the industry.
Bonus for knowledge of:
Scripting languages, preferably Python.
RDBMS systems, PostgresSQL, SQL server or similar .
Location:
CN-Shenzhen-HyQ
Shift:
Standard - 40 Hours (China)
Scheduled Weekly Hours:
40
Worker Type:
Permanent
Job Summary:
This role is a critical part of the Enterprise data team involved in the replacement of legacy ETL tool by providing key data engineering activities including pipeline management, analysis & visualisation engineering. The role will be working closely alongside ETL developers and wider technology teams to engineer solutions supporting their strategic roadmap.
This is a high-impact role for a candidate who is passionate about engineering excellence, have a strong technical background and excellent IT skills paired with excellent team working and communication skills. This role is a data engineering role (covering backend, data, infrastructure) and collaborates on solution design, implementation, deployment, testing and support.
Job Duties:
Hkex · China