AI & Data FinOps Lead

Job Summary

The AI & Data Fin Ops Lead is responsible for establishing, leading and scaling a new AI Fin Ops capability within Absa's Chief Data & Applied AI Office (CDAIO), with a mandate that extends across Absa Technology. The role is accountable for delivering and maintaining, through continuous iterations, end-to-end transparency, control and optimisation of AI and data‑related consumption across the organisation. This includes the AI Gateway, AWS AI services, Microsoft Copilot, Git Hub Copilot and direct use case licensing.

Over time, the capability will expand to cover all data‑related costs across the CDAIO under a broader Data Fin Ops remit. The purpose of the role is to ensure that AI and data consumption is fully transparent, attributable and charged back to consuming business units on a monthly variable basis – with transfer pricing retained only for genuinely centralised shared services.

The successful incumbent will bridge deep technical execution with strong commercial acumen and will serve as the primary interface between CDAIO and the existing Cloud Fin Ops team.

• Design, build and ope rationalise foundational dashboarding with insights within the first six months, covering the AI Gateway, AWS AI services, Microsoft Copilot and Git Hub Copilot.
• Develop and maintain API integrations across all AI consumption sources to provide real‑time visibility of token usage, licence allocation and AWS infrastructure charges.
• Continuously enhance tooling, dashboards and data pipelines as new use cases, vendors and consumption models are onboarded.
• Lead the expansion of the capability into Data Fin Ops, covering all data platform, storage, compute and pipeline costs across the CDAIO.

• Design and implement dynamic chargeback and auditable show‑back models that allocate AI and data costs to consuming business units on a monthly variable basis.
• Build workflow automation across systems and business areas to enable accurate, auditable and timely cost attribution.
• Reduce reliance on annual management account transfer pricing and retain it only for genuinely centralised shared services where consumption‑based allocation is not feasible.
• Leverage and rationalise existing enterprise tooling such as Magic Orange / Harness to industrialise chargeback processes where appropriate.

• Build and maintain accurate monthly data that can be used as part of forecasting process for multiple stakeholders across Absa Technology and business units.
• Implement automated alerting and shut‑off mechanisms triggered by defined consumption and budget thresholds wherever possible.
• Partner with Finance to align AI and Data Fin Ops forecasts to budget cycles, variance reporting and outlook submissions.

• Establish a robust framework to track financial impact provide insights into the value realisation outputs linked to each AI use case into our CDAIO Strategy capability.
• Provide clear unit economics – including cost per token, cost per user, cost per inference and cost per use case – together with ROI insights for CDAIO leadership and business sponsors to enable go/no‑go decisions and identify where optimisation opportunities exist.
• Identify and surface optimisation opportunities, driving cost‑out initiatives across AI and data consumption.

• Serve as the primary interface between CDAIO and the existing Cloud Fin Ops team, ensuring alignment of methodology, tooling and reporting.
• Engage credibly with VP, Principal and executive stakeholders across Technology, Finance and the business units.
• Partner with CDAIO technical teams – including engineering, platform and data science – to embed Fin Ops practices into delivery life cycles.
• Contribute to relevant governance forums, steering committees and executive reporting cycles.

• Develop a clear roadmap to scale the AI & Data Fin Ops function, including future team structure and operating model.
• Mentor and uplift Fin Ops literacy across CDAIO…