Responsibilities To Understand and translate business requirements into data requirements to enable stakeholders to consume data in the most effective and efficient way. To design and maintain optimal data models/structures at both an enterprise level as well as conceptual, logical & physical levels which meet the business and architectural objectives of Company. Qualifications and experience 2 years + experience in the Data Modelling discipline 3 years in a data role such as Data Engineer, Data Analyst or BI Business Analyst. Experience using one of the following data modelling tools e.g Sparx Enterprise Architect, Erwin, SAP PowerDesigner, ER/Studio or IBM data architect Exposure to relational data models Experience working with dimensionally modelled data Experience in supporting as well as implementing data infrastructures Proven analytical and problem-solving experience in a complex data environment Experience with generic financial industry data models (products) Exposure to graph data models and NoSQL models Relevant industry experience i.e. financial services or retail. Grade 12 National Certificate / Vocational in Grade 12 National Certificate Bachelor's Degree in Information Technology - IT Engineering or Information Management Certification in Data Management Knowledge Data Modelling tools e.g. Sparx Enterprise Architect,Erwin & SAP Power Designer, ER/Studio, IBM data architect Entity Relationship Modelling Physical Database Design Data warehousing Data Integration Metadata Knowledge of database concepts, objects and data modelling techniques and design principles. Expert knowledge of data modelling principles/methods including conceptual, logical & physical Data Models Knowledge of the entire process behind software development including design and deployment Data modelling patterns e.g. party role Broad understanding of Data Management (DMBOK), systems development lifecycle methodologies and IT Architecture SQL Banking and financial services environments BI tools and technologies as well as the optimization of underlying databases How to clearly communicate complex technical ideas, regardless of the technical capacity of the audience Knowledge of the mathematical foundations of data normalisation #J-18808-Ljbffr