Data Analyst Data Modeler

Key Responsibilities:

• Design implement and document data architecture artifacts and data modelling solutions
  
• Develop conceptual logical and physical data models using data modelling tools such as Erwin
  
• Build data lineage by stitching business terms in Group Data Management tools eg Collibra to logical and physical data model elements
  
• Provide guidance and assistance to data engineering teams regarding physical data object implementation
  
• Perform data discovery attribute mapping and data artifacts documentation generation eg data dictionary leveraging AI where applicable
  
• Provide guidance on data quality rules and facilitate data checks and profiling for Sources of Record SoRs deployment
  
• Support data preparation, data provisioning and data self-service automation initiatives
  
• Collaborate with business stakeholders and product owners to clarify business requirements definitions and processes and implement fit for purpose data models efficiently
  
• Align Application Logical Data Models ALDM with Enterprise Logical Data Models ELDM and enterprise standards
  
• Draft and align data standards and naming conventions for data artifacts across different data platforms
  

Requirements

Required Skills Experience:

• Experience in data modelling, data architecture and data management
  
• Strong hands on experience with data modelling tools such as Erwin
  
• Experience working with data governance and metadata management tools such as Collibra
  
• Knowledge of conceptual logical and physical data modelling principles
  
• Experience with data lineage data quality data profiling and data dictionary creation
  
• Familiarity with data engineering concepts and physical data object implementation
  
• Experience gathering and translating business requirements into data models and data artifacts
  
• Strong stakeholder management and communication skills with the ability to work closely with business users, product owners and technical teams
  
• Experience in defining and implementing data standards naming conventions and enterprise data governance practices
  
• Exposure to AIenabled data discovery documentation generation and data management activities is an advantage
  

Languages: Business-level proficiency in Mandarin, Cantonese, and English is mandatory.