About this role
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 (Informatica) 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: Responsibilities:
• 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. • Compose high quality documentation and specifications. • Demonstrate a good understanding of the broader toolset available for data access, analytics, manipulation etc. in order to continually evaluate/assess the suitability of tool choices. • Work with other data scientists and business teams to onboard Jupiter playbooks/python apps to robust infrastructure with appropriate standards and monitoring. • Work with Technology Governance Board to contribute towards technical standards and patterns to ensure consistency and adoptability across multiple service teams.
Required Knowledge and Level of Experience:
• Experience: Minimum 7 years in data or software engineering, with demonstrable lead 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. • noSQL or distributed databases (MongoDB etc) Experience with working on streaming pipelines.
Personal Qualities:
• Curiosity & Proactivity: Demonstrates a passion for continuous learning, improvement, and staying current with industry trends. • Collaboration: Works effectively across departments and disciplines, building strong relationships with both technology and business colleagues. • Outcome-Driven: Motivated by delivering real-world outcomes, improving enterprise value, and supporting business strategy.
Company Introduction: ITD SZ
港交所科技（深圳）有限公司，是2016年12月28日于深圳市前海自贸区成立的外商独资企业。
作为港交所的技术子公司，港交所科技（深圳）有限公司主要是为集团及其附属公司提供计算机软件、计算机硬件、信息系统、云存储、云计算、物联网和计算机网络的开发、技术服务、技术咨询、技术转让；经济信息咨询、企业管理咨询、商务信息咨询、商业信息咨询、信息系统设计、集成、运行维护；数据库管理、大数据分析；以承接服务外包方式提供系统应用管理和维护、信息技术支持管理、数据处理等信息技术和业务流程外包服务。