Head of Engineering

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Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this.

Job Description

The Head of Engineering will be accountable for leading the development, modernisation and maintenance of applications, systems and platforms, and will be expected to translate the technology strategy into working systems that are stable, secure, resilient, cost-conscious and capable of supporting digital, data and AI-enabled business outcomes. This is an AI-native leadership role: the successful individual will be expected to embed AI-assisted engineering practices into the software development lifecycle itself, in addition to delivering AI-enabled products and platforms for the business.

The Head of Engineering will be responsible for:

• Software and Product Engineering

• Platform, Infrastructure and DevOps

• Data Engineering and Analytics

• AI, Machine Learning and Automation

• Security Engineering

• Quality Engineering and Site Reliability Engineering

The role is particularly important where legacy platforms, complex integrations, regulatory expectations and increasing business demand create pressure on delivery and operational resilience and the successful individual must be able to improve delivery speed and quality while maintaining production stability, security and governance.

This is a leader of leaders role that sets standards, establishes delivery discipline, builds engineering capability, and holds teams accountable for measurable outcomes.

Key Result Areas:

Quality Assurance

Develop and maintain quality procedures and engineering standards; oversee testing, , certification of technology processes and quality controls for products and platforms.

Project and Delivery Management

Manage delivery of major engineering programmes and take personal accountability for a small number of business-critical projects.

Digital Vision and Strategy Execution

Collaborate on the digital roadmap and lead execution of workflows, processes and enablers that allow new applications, products and services to be delivered quickly while maintaining legacy applications and IT operations at optimal levels.

Digital Talent Development

Execute the strategy to grow engineering and digital talent through development plans, coaching, mentoring and improved employee experience.

Data-Driven Product and Service Improvement

Create the capability to proactively identify opportunities to improve digital products, services and user experiences over the long term.

Current and Target-State Analysis

Document complex current-state and target-state processes and define the changes required to migrate to the target capability.

Leadership and Direction

Translate function strategy into clear actions for teams; explain the link to organisational mission, vision and values; and motivate people to deliver local business goals.

Performance Management

Set objectives for direct reports, manage performance, hold individuals accountable and take corrective action where required to achieve annual business objectives.

Information Security

Define the business impact of security incidents and drive recommendations to prevent recurrence, in partnership with security and risk stakeholders.

Budgeting

Manage departmental budget plans and ensure delivery and modernisation choices are cost-conscious and value-led.

Organisational Capability Building

Assess capability gaps, prioritise development activity, implement formal development frameworks and strengthen the engineering talent pool.

Application Software Development

Oversee complex existing and new applications by identifying areas for modification and improvement, and ensuring new applications meet customer and business requirements.

Application Software Roadmap

Define and maintain the roadmap for complex application software development and prioritise engineering work in line with business needs.

Information and Business Advice

Provide authoritative specialist advice to senior managers to support policy implementation, projects and change initiatives.

AI-Augmented Engineering and Developer Productivity

Drive organisation-wide adoption of AI-assisted development tools (coding assistants, agents, automated testing and code review) across the software development lifecycle, and track their impact on delivery speed, quality and engineering capacity.

Platform Engineering and Internal Developer Platform

Build and scale a self-service internal developer platform with golden paths, reusable components and automated guardrails, reducing cognitive load on engineering teams and accelerating time to production.

AI, Model and Data Governance

Partner with risk, compliance and data stakeholders to embed responsible AI practices, model risk management and data governance controls into engineering delivery, ensuring AI and automation capabilities move from pilot to production safely and with appropriate oversight.

Engineering Metrics and Continuous Improvement

Establish and report on delivery and reliability metrics (for example DORA metrics and SLOs/error budgets) to drive a culture of continuous improvement and evidence-based decision-making.

Chapter and Sub-Chapter Management

The Head of Engineering will establish and lead a Chapter operating model, grouping engineers by discipline (including Software Engineering, Data Engineering, Platform and DevOps, Quality Engineering and SRE, Security Engineering, and AI/ML) across product squads and delivery teams. Each Chapter will be led by a Chapter Lead accountable for technical standards, hiring quality, career progression and skills development within their discipline, working alongside Squad and Product Leads who direct day-to-day delivery priorities.

The Head of Engineering is accountable for the overall health and capability of the Chapter structure, including maintaining competency frameworks and career ladders for each discipline, identifying and closing skills gaps through targeted hiring, upskilling or redeployment, and monitoring chapter-level health indicators such as engagement, attrition, internal mobility and time-to-competency for new joiners. In an AI-native engineering organisation, each Chapter Lead will also be accountable for driving the adoption of AI-assisted tools and practices within their discipline and for measuring the resulting impact on delivery speed, quality and engineering capacity, ensuring that technical excellence and AI-augmented ways of working are embedded consistently across the engineering function rather than left to vary by team.

Qualifications, Skills, Experience Required:

• Bachelor’s degree in computer science, Software Engineering or a related field.

At least 8 years of general technology or software engineering experience, with significant exposure to complex delivery environments.

• At least 7 years of managerial experience, including leadership or mentorship of software engineering teams.
• Experience leading cross-functional development teams and delivering complex, business-critical projects.
• Strong understanding of software development across front-end, back-end, integration and platform technologies.
• Experience defining and maintaining application software roadmaps aligned to business priorities.
• Proven ability to balance delivery speed, production stability, security, risk and quality.
• Excellent problem-solving, debugging, communication, collaboration and leadership skills.
• Commitment to staying current with emerging technology trends, including cloud, microservices, automation, data, AI and AIagentic engineering tools.
• Demonstrated experience leading AI-augmented software engineering organisations, including adoption of AI coding assistants and agentic development tools at scale.
• Working knowledge of platform engineering, internal developer platforms and metrics-driven engineering culture.

Preferred:

• Experience with cloud services such as Azure, AWS or Google Cloud.
• Experience with microservices architecture and modern integration patterns.
• Agile or Scrum certification and familiarity with project management and delivery tooling.
• Experience modernising mission-critical legacy systems.
• Experience moving AI, GenAI or automation capabilities from pilot to production with appropriate governance.
• Experience operating in a regulated or high-control environment where audit, resilience and evidence matter.
• Hands-on familiarity with AI coding assistants and agentic tools (e.g. GitHub Copilot, Claude Code, Cursor) and their governance in an enterprise setting.
• Exposure to LLMOps/MLOps practices and modern data architectures such as data mesh or lakehouse.
• Experience with cloud FinOps and cost optimisation for engineering and AI workloads.
• Awareness of emerging AI regulation and data protection requirements (e.g. POPIA, GDPR, EU AI Act) relevant to a regulated financial services environment.

Skills

Competencies

Education

Closing Date

The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question.

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