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An exciting opportunity has arisen for a high-calibre Data Platform Manager to lead the enterprise data infrastructure strategy for a global organisation in Hong Kong. Leveraging Azure Databricks and cloud-native frameworks, you will drive data governance and DevOps excellence to deliver scalable, secure corporate data assets that power strategic business intelligence and AI.
Job Responsibility
Formulate and execute the end-to-end lifecycle of the centralised data platform architecture, ensuring perfect alignment between commercial goals and multi-regional compliance mandates
Oversee the development, deployment, and optimisation of highly available data pipelines and processing workflows using Azure Databricks and Azure Data Factory across distributed engineering teams
Structure, refine, and validate comprehensive data models and integration paths, leading cross-functional design reviews to translate business needs into robust technical solutions
Supervise data governance initiatives, including automated data quality monitoring, metadata cataloguing, data lineage mapping, and enforcing privacy-by-design principles to satisfy global regulations
Act as the primary technical bridge connecting business operations, analytics specialists, and engineering squads to facilitate requirement gathering and solution workshops
Conduct structured architectural evaluations to verify that new systems adhere to security, efficiency, and quality baselines, identifying and mitigating technical risks early
Drive the adoption of automated workflows, DevOps methodologies, and cutting-edge cloud tools, guiding initiatives smoothly from proof-of-concept to production
Direct root-cause analysis and incident resolution for platform or data delivery issues, designing robust preventative strategies alongside security and risk functions
Establish and communicate engineering best practices through clear documentation, onboarding plans, and targeted training to foster a culture of technical excellence and shared accountability
Regularly monitor performance metrics, system resource allocation, and cloud spend, delivering optimisation roadmaps to executive leadership
Requirements
Minimum 8 years of experience of progressive experience in enterprise data engineering, infrastructure management, cloud-native data architecture, and data governance delivery
Proven expertise in building and maintaining modular, highly secure, and resilient data platform architectures optimised for cloud ecosystems
Advanced capability in data modelling and integration frameworks tailored to support complex business intelligence, AI, and machine learning workloads
Hands-on mastery of real-time streaming technologies (such as Kafka, Apache Flink, or Change Data Capture) combined with deep expertise in the Microsoft Azure ecosystem (including Azure Databricks, Data Factory, Data Lake Gen2, and Logic Apps)
Strong track record of utilising Agile and DevOps methodologies to manage multi-disciplinary engineering projects and ensure iterative deployment cycles
Demonstrated aptitude for diagnosing, troubleshooting, and performing root-cause analysis on intricate, high-throughput corporate data systems
Holder of a Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical discipline
Full professional fluency in English is required
Nice to have
Prior experience working with complex industrial, logistics, or operational enterprise data environments is highly valued
Industry-recognised credentials such as Microsoft Certified: Azure Data Engineer Associate or Databricks Certified Data Engineer are considered a strong asset