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.
about the company.
Our client is a prestigious, global logistics and operational enterprise with a rapidly expanding digital footprint. Known for driving large-scale technological innovation, they are heavily investing in cloud-native data capabilities to maintain their competitive industry edge. The organisation fosters a collaborative, forward-thinking corporate culture that deeply champions technical excellence and continuous professional growth.
about the team.
You will direct a dynamic, multi-disciplinary data engineering division comprising both onshore and offshore engineering squads. Partnering closely with data science teams, diverse business units, and cross-functional technology stakeholders across the global IT division, this collaborative group focuses on translating complex operational requirements into high-throughput, cutting-edge data products within an Agile environment.
about the job.
... 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.
skills & experience required.
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.
Prior experience working with complex industrial, logistics, or operational enterprise data environments is highly valued.
Holder of a Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical discipline.
Industry-recognised credentials such as Microsoft Certified: Azure Data Engineer Associate or Databricks Certified Data Engineer are considered a strong asset.
Full professional fluency in English is required; proficiency in Mandarin is an advantage.
If you are interested in this role, please click 'Apply Now' or send your CV directly to allison.lin@randstad.com.hk
