Hong Kong Telecom
Lead Data Engineer
- Compensation
- Not disclosed
- Location
- Hong Kong Island
- Work setup
- Onsite
- Seniority
- Lead
- Employment
- Full time
- Experience
- 10+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or a related quantitative field.
- Core Engineering: Advanced proficiency in SQL and core programming languages relevant to data engineering (e.g., Python, Scala, Java, or R).
- Cloud & Platform Architecture: Demonstrated experience building and operating enterprise-scale Data Warehouses and Data Lakes on cloud environments (e.g., AWS, Azure, GCP, Snowflake, Databricks).
- Data Modeling & Pipelines: Proven expertise in conceptual, logical, and physical data modeling, as well as orchestration tools for distributed data processing.
Full description
The Lead Data Engineer plays a pivotal role in driving the organization’s data strategy by overseeing the architecture, development, and operational excellence of our Enterprise Data Platform. This leadership role is responsible for directing a team of high-performing engineers to build scalable, high-throughput data processing systems that turn vast, diverse datasets into secure, actionable business intelligence. The ideal candidate blends deep technical expertise in modern cloud data architectures with strategic leadership and stakeholder management skills.
Key ResponsibilitiesStrategic Leadership & Governance
- Team Management: Lead, mentor, and foster a team of data engineers, driving high technical standards, professional growth, and alignment on delivery goals.
- Data Architecture & Governance: Establish and oversee data governance framework standards—including metadata management, data lineage, quality assurance, privacy, and end-to-end security protocols to safeguard sensitive information.
- Business Alignment & ROI: Identify innovative opportunities to leverage data technologies that drive operational efficiency, optimize costs, and support revenue-generating business initiatives.
Data Platform Architecture & Engineering
- Pipeline Infrastructure: Architect, construct, and optimize enterprise-grade ETL/ELT pipelines to ingestion, transform, and store high-volume structured and unstructured data streams.
- Storage & Modeling: Design, evaluate, and maintain scalable data lake and cloud data warehouse environments using robust data modeling principles optimized for analytics and reporting.
- Quality & Reliability: Establish continuous integration, automated testing, and comprehensive validation protocols to guarantee high data availability, accuracy, and integrity across all consumption points.
Stakeholder Collaboration & Operations
- Cross-Functional Partnership: Collaborate directly with Business Units (BUs), System Integration (SI/LPS) partners, IT teams, Data Scientists, and Analysts to translate complex business needs into scalable technical specifications.
- Performance Tuning & Monitoring: Monitor infrastructure health, identify and resolve architectural bottlenecks, and implement proactive system optimizations for maximum reliability and uptime.
- Technology Roadmap: Stay ahead of industry trends across big data processing, cloud architectures, and distributed computing to continually refine and modernize the organization’s tech stack.