Data Governance Specialist

The Data Governance Specialist is responsible for managing the organisation’s master data and metadata management practices, ensuring data governance, quality, integrity, and accessibility across all data sources. The role is responsible for implementing and operationalising data governance practice, maintaining data catalogue platforms, supporting data acquisition strategies, and enabling trusted data usage across the organisation with clearly defined ownership and stewardship roles.

a) Digital Acumen

The ability to understand, contribute to, and drive the adoption of digital technologies and processes within an organisational context to improve efficiency, create new value, and transform operations.

b) Data Management

Defines a data management plan and drives this through respective function.

Defines policies for file storage.

Develops a backup plan and schedule and backs up data accordingly.

Ensures proper files are selected and backed up and stored off site and in proper storage conditions on site.

Defines backup test procedures and recovery on a regular basis.

Defines Cross checking procedures and policy.

c) Data Collection and Analysis

Based on knowledge of the reasons behind the analysis, is able to define the most appropriate means of data collection.

Is able to develop formats for data collection.

Is able to define the most appropriate internal and external data/information sources.

Identifies key facts in an array of data, recognises when pertinent facts are incorrect, missing, or require supplementation or verification.

Breaks down data into component parts to understand the nature and relationship of the parts.

Has a broad knowledge of statistical data-handling techniques.

Can undertake more comprehensive analysis of data/information but is not required to draw conclusions.

d) Quality Management

Ability to develop quality management plans and inspection protocols. Implements systems and processes to ensure quality is built into the design and planning stages and conducts audits and reviews to verify compliance with standards.

e) Reporting

Prepares both standard and non-standard reports to time and quality standards.

Collate and analyses readily available data for inclusion in a report.

Minimum Qualification

A bachelor’s degree in computer science, Information Systems, Informatics, or a related field.

Professional certification in data management (e.g. DAMA or equivalent).

Minimum Experience

A minimum of 8 years’ experience in data management, master data and metadata management across multiple domains and platforms.

Experience working with large datasets and deploying models into production environments.

Proven experience in master data and metadata management across multiple domains.

Strong understanding of data governance frameworks, data quality principles, and regulatory compliance.

Experience working with data warehousing platforms and data integration tools.

Desirable Requirements

Postgraduate qualification in Data Management or related fields.

Certifications in data management, data architecture, or database technologies.

Certifications in data privacy and compliance.

Experience in a development finance, public sector, or regulated environment.

*The KPA’s, competencies and relationships listed in this document, is not exhaustive and the incumbent will be expected to undertake additional duties within their capacity to meet the needs of business and/or the business unit.*

1. Master and Metadata Management

Define and enforce standards for master data domains to ensure consistency and reduce duplication.

Develop and maintain metadata repositories, including data definitions, lineage, and classifications.

Ensure metadata is accessible and supports data discovery and literacy across the organisation.

Configure, maintain, and optimise MDM platforms and tools.

Support system integration between MDM and other enterprise systems (ERP, CRM, etc.).

Implement controls to prevent duplication, inconsistency, and outdated records.

Maintain data regulatory compliance, operational efficiency, and effective decision-making.

2. Data Warehousing and Platform Maintenance

Maintain and update data warehousing platforms to ensure performance and scalability.

Implement data loss prevention and backup strategies to safeguard data integrity.

Monitor platform performance and proactively address issues to ensure availability and reliability.

Ensure consistent data flow across DBSA production systems and platforms.

Manage data migration and onboarding of new data sources into Data Lake environment.

3. Data Acquisition

Develop and implement data acquisition strategies aligned with business needs.

Ensure data is collected, validated, and integrated from various internal and external sources.

Monitor data ingestion pipelines to ensure timely and accurate data availability.

Evaluate and recommend enhancements or innovative technologies.

4. Data Quality Assurance

Monitor and improve the quality of master and metadata through profiling, validation, and cleansing.

Implement controls to ensure data accuracy, completeness, and consistency.

Identify and resolve data quality issues proactively to support reliable analytics and operations.

Track and report on data quality metrics and improvements.

5. Data Governance and Compliance

Implement data governance practices aligned with internal policies and external regulations (e.g., POPIA, GDPR).

Support audits and compliance reporting by maintaining accurate and well- ocumented data practices.

Facilitate data governance forums and feedback sessions to promote accountability and transparency.

Ensure ethical handling of sensitive data and reduce legal and reputational risks.

Establish and maintain clear data ownership and stewardship structures.

6. System Integration and Enablement

Support integration of master data across platforms to ensure consistent and accurate information flow.

Enable interoperability and data consistency across business processes and systems.

Collaborate with technical teams to ensure seamless data exchange and alignment.

Contribute to the development of scalable and sustainable data architecture.

7. Reporting and Monitoring

Produce accurate, insightful reports and continuously monitor key metrics to assess progress against objectives.

Analyse data to identify trends, inform strategic decisions, and provide stakeholders with clear, actionable information.

Ensure reporting supports ongoing performance improvement and accountability.

Maintain high standards of accuracy and confidentiality in all reporting activities.

8. Collaboration and Project Delivery

Collaborate with ICT teams to integrate analytics solutions into enterprise systems.

Support automation, digitalisation, and ICT portfolio management through data-driven approaches.

Work closely with business units to understand analytical needs and define project objectives.

Align data science initiatives with business goals, ensuring relevance and impact of analytical solutions.

Partner with various business units and Operations teams to deliver cross- functional data solutions.

Integrate data science into broader business projects, enhancing collaboration and accelerating delivery.

Ensure timely delivery of high-quality data science outputs aligned with business priorities. Meet deadlines and quality standards, driving measurable value through data-driven solutions that support strategic objectives.

Key Measurements of Outputs

1. Frequency and accuracy of metadata and master data updates.

2. Quality of data resolution within SLA.

3. Improvement in data quality and integrity scores over time.

4. Maturity improvement of Information Management capability.

5. Compliance with internal data governance frameworks and external regulations.

6. System uptime and performance of data platforms.

7. Timeliness and completeness of data acquisition and integration.

8. Stakeholder satisfaction with data accessibility and usability.

9. Timeliness and accuracy of governance and audit reporting.