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.