Expertise

Technology Leadership

Leadership of multidisciplinary technology functions covering engineering and operational domains.

• Technology strategy
• Engineering leadership
• Organisational design
• Building and developing technical teams
• Operational governance
• Technology investment decisions
• Vendor and platform strategy
• Cost optimisation
• Technology risk
• Executive communication
• Transformation programmes
• Cross-functional technology leadership

A recurring theme throughout my career has been working across organisational boundaries rather than within a single technology discipline. Modern technology problems rarely belong entirely to infrastructure, engineering, security or data. The most important problems usually exist somewhere between them.

Cloud & Infrastructure

Cloud and infrastructure remain foundational areas of my experience. My perspective has been shaped by operating production environments rather than treating infrastructure purely as an architecture exercise.

• Cloud architecture and operations
• Database operations
• Production monitoring
• Observability
• Capacity and performance management
• Cloud cost optimisation
• Infrastructure modernisation
• Disaster recovery and resilience

Infrastructure should become progressively less visible to the people consuming it.

DevOps

Good platforms allow engineering teams to deploy, monitor and operate services without repeatedly solving the same infrastructure problems.

• Platform engineering
• Infrastructure as Code
• Release engineering
• CI/CD
• Containerised workloads
• Infrastructure automation
• Deployment reliability

Automation is not simply about reducing effort. It is about creating consistency and allowing engineering capacity to move toward higher-value problems.

Reliability & Operational Excellence

Systems inevitably fail. The objective is not to pretend otherwise, but to design organisations and platforms that detect problems quickly, understand them quickly and recover quickly.

• Monitoring and observability
• Automated alerting
• Anomaly detection
• Service health
• Production readiness
• Incident response
• Root-cause analysis
• Capacity management
• Deployment safety
• Operational resilience
• Business continuity
• Continuous improvement following incidents

I see reliability as an engineering discipline rather than simply an operations responsibility. Monitoring should be released with the application. Operational readiness should be part of engineering. And lessons from production should feed directly back into architecture and development.

Cybersecurity

Cybersecurity forms an integral part of my technology leadership responsibilities. My focus is particularly on making security operational, measurable and continuous.

• Information security
• Security operations
• SIEM
• Security monitoring
• Endpoint security
• Cloud security
• Identity and access management
• Vulnerability management
• Security automation
• Threat detection
• Security awareness
• Third-party security
• Information security governance
• Policy and control frameworks
• Audit and assurance
• ISMS
• Incident detection and investigation

Security works best when it becomes part of normal technology operations. Controls that exist only during an audit provide limited protection. The stronger model is continuous visibility: understanding what is changing, identifying meaningful deviations and giving teams the information required to respond.

AI and automation are creating particularly interesting opportunities here, especially where machines can perform the repetitive parts of investigation while experienced people concentrate on judgement and risk.

Data & Business Intelligence

I am particularly interested in moving organisations away from a model where every new business question creates another centrally maintained report. Instead, the objective should increasingly be governed self-service data.

• Reliable data engineering
• Clear ownership
• Consistent business definitions
• Data governance
• Discoverability
• Trusted datasets
• Appropriate access controls
• Data quality
• Business education
• Self-service analytical capability

The emergence of Generative AI makes this even more important. When people and AI systems can query enterprise information conversationally, organisations need to be much clearer about what their data actually means.

Artificial Intelligence

There is an important distinction between using AI to help someone perform a task and designing a process where AI performs part of the operation itself. My work and experimentation in this area includes:

• AI-assisted software engineering
• Coding agents
• AI-based operational monitoring
• Security investigation agents
• Multi-source monitoring agents
• Automated incident analysis
• Intelligent knowledge retrieval
• Conversational access to enterprise data
• AI-assisted operational decision making
• Persistent agent memory
• AI governance
• AI risk assessment
• Model and provider resilience
• AI cost governance

I am particularly interested in agentic systems that can observe information across multiple systems, reason over that information, retain useful context and identify situations that require human attention.

Responsible AI

AI adoption introduces a different class of technology risk. The question is no longer simply “Can we build it?” It is also: What decisions is the AI making? What information can it access? What happens when it is wrong? Where must a human remain responsible? What happens when the underlying AI provider becomes unavailable?

My approach is to encourage experimentation while establishing proportionate governance around risk, security, cost and resilience. Governance should enable responsible adoption rather than become a mechanism for preventing experimentation.

Technology Economics

Cloud platforms, SaaS products and AI services make it extraordinarily easy to consume technology. That does not necessarily make them inexpensive. Technology leaders need to understand both architecture and economics.

• Cloud cost visibility
• Infrastructure utilisation
• Capacity optimisation
• SaaS rationalisation
• Vendor negotiations
• AI consumption costs
• Unit economics
• Build-versus-buy decisions
• Cost attribution
• Technology ROI

Cost optimisation should not simply mean spending less. The better question is: are we receiving sufficient value for what we are spending?