Data Management Specialist

Job description

The Data Steward is responsible for the execution of the strategy and business processes required to create, maintain, and enhance RMB’s data. The position is ultimately responsible for data cleansing, data definitions, process documentation, as well as serving in a consultant role to functional teams providing best practices, improved processes and architecture, data interpretation and training.

The Data Steward is s also typically the “go to” person within a specific domain for all the queries/issues related to data.



·         Domain level data architecture and data management best practices.

·         Identifying and acquiring new data sources.

·         Creating and maintaining consistent reference data and master data definitions.

·         Publishing relevant data to appropriate users in an organization, and monitoring the published data sources for usage/relevance/quality feedback.

·         Creating and managing business metadata for published data sources to ensure that it is easily discoverable, and meaningful to information workers.

·         Resolving data integrity issues across stakeholders.

·         Analysing data for quality and reconciling data issues.

·         Initiating in-depth analysis of data quality issues and present trends independently to management

·         Identify opportunities to mitigate risk and structurally address root causes of problems

·         Evaluate the data model for areas of risk and implement cross-team solutions

·         Develop tools and reports that enable scalable and repeatable delivery of your analyses

·         Evaluate existing process, tools, and reports for areas of improvement


Key services to be provided by the HDM (but are not limited to):

·         Data Governance

o   Assist with the development, documentation, communication and implementation of policies, standards and procedures in platforms for the definition, creation, storage and usage of master data elements, including, but not limited to, products, customers and vendors.

o   Lead de-duplication and re-qualification efforts for key data elements.

o   Research and implement data enhancements (population of additional data elements) required for reporting & analysis, digital, or other activities.

o   Identify opportunities for standardization of data elements and related definitions. Collaborate with business users to apply necessary changes.

o   Develop an intimate familiarity with RMB’s data and support functional teams on best practices for product classification, customer segmentation, and other master data methodologies.

·          Data Process Management

o   Investigate questions or issues related to master data elements and provide resolution in a timely fashion.

o   Consult in matters of data workflows, master data security, and access rights.

o   Coordinate flow of information between all departments regarding changes to master data or related processes.

o   Provide training or education to the business on master data and data governance.

o   Leverage dashboards/metric reports and conduct data analysis to monitor and audit data quality and completeness. Assist with development of KPIs related to data governance standards.

·          Data Extraction & Repositories

o   Develop an understanding of information flow and system architecture and how it relates to underlying data structures.

o   Analyse and evaluate data gathered from multiple sources and address conflicts or business issues, including integration of new systems. Perform data conversion/onboarding activities as required.

o   Build and maintain a data dictionary that catalogues data elements and usage across systems, functions, and processes.

o   Promote best practices and make recommendations to all business users that will create efficiencies and ensure data integrity.

·          Analytics Support

o   Perform quantitative analysis on various data sets that drive critical business decisions.

o   Generate and distribute reports professionally and effectively to key stakeholders.

o   Fulfil ad-hoc analytical requests for various stakeholders. Determine most efficient and accurate methods for obtaining required information.

Key accountabilities for the Data Steward include (but are not limited to):

·         RMB’s adherence to internal policies, such as the FirstRand Information Governance Framework.

·         RMB’s adherence to external regulations and best practices, such as the Basel Committee’s BCBS 239 document on the Principles for Effective Risk Data Aggregation and Reporting



Qualifications and experience:

·         Bachelor’s Degree required

·         5-10 years of experience in managing financial data – data vendor on-boarding, data quality management rules and alerts configuration, exception resolution etc.

·         Advanced data analysis skills using Excel, SQL and reporting tools to import, analyse and report on data




·         Expertise in specific Financial Services domains, including expertise at the intersection of risk, finance and customer domains;

·         Sound knowledge of data management life-cycle and data governance principles

·         Sound knowledge of data architecture

·         Sound knowledge of metadata, master data, data integration principles and data quality management

·         Sound SQL knowledge

·         Familiarity with reporting tools such as Microsoft Reporting Services and OBIEE

·         Data modelling skills

·         Expertise in business and IT architecture, including familiarity with leading architectural standards such as TOGAF, FEA and/or Zachman;

·         Familiarity with Enterprise Metadata Management (business and IT) and OMG standards;

·         Information management program life cycle experience;

·         Experience in operationalizing Data Governance, Data Stewardship and Data Quality;

·         Expertise in creating and deploying best practices and methodologies;

·         Familiarity with industry data models such as IBM BDW and IIW, Teradata FSLDM, and SAS IIA;

·         Familiarity with software development lifecycles;

·         Familiarity with process modelling, semantic modelling and data modelling.

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