Data Governance Lead

My client in the consulting industry is looking for a Data Governance Lead. The Data Governance Lead is accountable for establishing, embedding, and continuously maturing enterprise data governance framework, operating model, policies, controls, stewa...

• Relevant Bachelor’ Degree or equivalent (NQF
  7) qualification is essential.
• Postgraduate qualification in Data Management, Information Systems, or Business Administration (advantageous).
• CDMP (Certified Data Management Professional) – Practitioner or Master level or working towards certification (highly advantageous / preferred).
• Exposure to AI governance frameworks or responsible‑AI training (e.g., ISO/IEC 42001, NIST AI Risks Management Framework) advantageous.

• Experience in the mining industry would be a key advantage.
• Minimum 8 – 10 years’ experience in data management, business intelligence, or enterprise data roles.
• Minimum 5 years leading governance, data quality, stewardship, master data or enterprise data initiatives in a large enterprise.
• Demonstrated experience implementing data governance frameworks in a large, complex organisation – mining, industrial, or heavy‑asset environments strongly preferred.
• Experience in establishing operating models, governance forums, policies, controls and adoption roadmaps.
• Exposure to governing data for AI and big data initiatives – e.g. defining data quality or provenance requirements for a model, participating in AI risk/ethics review, or managing data supply for predictive analytics use case – advantageous.
• Proven ability to influence senior stakeholders across business, IT, risk, audit and compliance.
• Familiarity with POPIA and other data privacy/regulatory regimes relevant to a JSE/LSE‑listing mining company.

• Deep working knowledge of DAMA‑DMBOK2 knowledge areas:data governance, data quality, metadata management, master data management, data architecture.
• Strong understanding of enterprise data architecture and BI ecosystems (SAP BW/4
  
  HANA, HANA, Business Objects, Tableu, AWS Data Lake).
• Working knowledge of data cataloguing/lineage tooling and data quality management platforms.
• Understanding of mining value chain data (geology, mine planning, production, processing, metallurgical accounting) advantageous.
• Working understanding of AI/ML data lifecycle concepts – training vs. production data, model drift, bias, explainability – sufficient to set governance requirements without needing to build models.
• Awareness of emerging AI governance and regulatory frameworks (e.g. ISO/IEC 42001, EU AI Act, NIST AI RMF) and their practical implications for the South African mining group.