Data Modeler

Responsibility

• Design and develop conceptual, logical and physical data models to support business intelligence, analytics and operational systems.
• Collaborate with data architect, engineers and business analysts to gather business requirements and translate them into effective and scalable data structures.
• Ensure data models comply with enterprise standards, governance frameworks and regulatory requirements.
• Maintain and enhance metadata repositories and data dictionaries to promote data transparency and accessibility across teams.
• Optimize data models for performance, scalability and long-term maintainability across various platforms.
• Conduct data profiling and analysis to validate model accuracy and proactively identify data quality issues.
• Support ETL/ELT development by providing clear data mappings, transformation logic and documentation.
• Document modelling decisions, data flows and entity relationships for both technical and business stakeholders.
• Apply domain expertise in both Hong Kong Life and General Insurance to ensure data models reflect industry-specific requirements and local regulatory standards.

Requirement

• Bachelor's degree in Computer Science, Information Systems or a related discipline.
• 6+ years of experience in data modelling, preferably with insurance industry datasets (policies, claims, customer or actuarial data).
• Advanced skill in designing conceptual, logical and physical models for Azure Databricks and Azure Data Lake that support structured, semi-structured and unstructured data.
• Experience with data modeling tools such as Erwin, ER/Studio, PowerDesigner or Microsoft Visio, and version control using GitHub.
• Deep understanding of relational, dimensional and data lake modeling techniques (star, snowflake, 3NF), optimized for Databricks/Spark-based processing.
• Ability to model and document metadata, reference data and master data with Informatica, supporting robust data governance and quality.
• Strong SQL and Spark skills for data profiling, validation and prototyping in Databricks environments.
• Familiarity with regulatory and compliance requirements for insurance data (e.g. IFRS 17, Solvency II).