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 discipline3 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 architectExposure to relational data modelsExperience working with dimensionally modelled dataExperience in supporting as well as implementing data infrastructuresProven analytical and problem-solving experience in a complex data environmentExperience with generic financial industry data models (products)Exposure to graph data models and NoSQL modelsRelevant industry experience i.e.
financial services or retail.Grade 12 National Certificate / Vocational in Grade 12 National CertificateBachelor's Degree in Information Technology - IT Engineering or Information ManagementCertification in Data ManagementKnowledge of:Data Modelling tools e.g.
Sparx Enterprise Architect,Erwin & SAP Power Designer, ER/Studio, IBM data architectEntity Relationship ModellingPhysical Database DesignData warehousingData IntegrationMetadataKnowledge of database concepts, objects and data modelling techniques and design principles.Expert knowledge of data modelling principles/methods including conceptual, logical & physical Data ModelsKnowledge of the entire process behind software development including design and deploymentData modelling patterns e.g.
party roleBroad understanding of Data Management (DMBOK), systems development lifecycle methodologies and IT ArchitectureSQLBanking and financial services environmentsBI tools and technologies as well as the optimization of underlying databasesHow to clearly communicate complex technical ideas, regardless of the technical capacity of the audienceKnowledge of the mathematical foundations of data normalisation
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