Job Description

Job PurposeCreate and Maintain the Principles, Standards, Policies and Procedures for BI and works closely with the all BI teams across the enterprise to ensure compliance.Explore and recommend emerging technologies, framework, methods to support/enhance all BI Capabilities.Providing BI focused advisory services, guidance, support and training across the BI ecosystem in NedbankCollaborating with stakeholders to understand BI related business / analytical requirements, i.e. stakeholder engagement.Job ResponsibilitiesCustomer EngagementRequirements elicitationSubject Matter AnalysisProject Management from start to endKnowledge ManagementVendor ManagementDemand Management

Responsibilities + Skills


Relevant Bachelors degree. (Comp Science, Engineering)


Min 6 years experienceUnderstanding of data management disciplines outside analytics (data modelling, data integration, metadata architecture, data warehousing, BI etc)Familiarity with BI technologies (Power BI, MicroStrategy)

Job Summary

  • Published on: Monday, 2nd March 2020
  • Designation: BI Data Analyst II
  • industry: Finance
  • Vacancy: 1
  • Employment Status: Full-time
  • Job Location: Johannesburg
  • Salary: on a call
  • Gender:
  • Application Deadline: Monday, 2nd March 2020

About the Company

  • Company Name: Nedbank Recruiting.
  • Address:
  • Website:
  • Company Profile:
  • Staying abreast of the various IFRS 9 modelling methodologies across retail and wholesale productsContributing to development aspects of the loss forecasting and stress testing framework and keeping the framework up to date as the methodology evolvesConducting ad hoc analysis on IFRS 9 PD, EAD, LGD and Survival models, as well as overall impairment calculations, for retail as well wholesale-type portfoliosConducting independent credit risk stress testing related research and using it as input into proposals and strategiesEngaging with various stakeholders across the organisation (including risk and finance) to discuss methodological aspects, business assumptions and results from the loss forecasting work stream

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