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.

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