The Unified Loyalty Platform
A fragmented, multi-vendor loyalty programme brought into one governed platform — the trusted foundation behind 100+ KPIs leadership, marketing and data science read every day.
Platform architecture & governance model
Twelve years architecting enterprise data platforms end to end — from the systems data is born in, through to the number a board finally acts on — and leading the teams that build them.
Ingestion, storage, curation, governance and serving designed as one coherent system — so a number on an executive dashboard can be traced all the way back to the system it came from.
Building and running cross-functional data teams — hiring, mentoring, and setting the standard a large delivery leans on.
Bringing AI into live delivery workflows, so the better pattern becomes the default rather than an experiment.
The deliverable is never the pipeline. It's a number a board will act on without asking which version it is — governed, reconciled, and trusted at the point of decision.
Exactly what the source sent, kept immutable. Messy, duplicated, inconsistent — and never thrown away.
Deduplicated, typed, conformed to shared definitions. The trusted core everything else is built from.
Aggregated to the grain the business actually asks questions at. This is the layer leadership reads.
A fragmented, multi-vendor loyalty programme brought into one governed platform — the trusted foundation behind 100+ KPIs leadership, marketing and data science read every day.
High-volume signals from diverse enterprise systems standardised into reliable, analytics-ready data — product adoption and usage visible as it happened, not a week later.
The spine connecting core business applications end to end — including secure, high-volume transaction processing that had to stay correct under real load, every hour of every day.
Two organisations becoming one, on a deadline that did not move. Large-scale data migrated and validated across systems while reporting stayed accurate throughout the transition.
Organisations are moving fast on artificial intelligence, and most of the value leaks out in the same place: AI gets adopted as a novelty rather than designed into the way work already flows.
I've introduced AI into live engineering and delivery workflows and helped teams take it up properly — starting from the work rather than the tool, proving it on something real, then training people and setting practical guardrails so the better pattern becomes the default.
Start from the work, not the tool. Where do teams actually lose hours — hand-offs, boilerplate, documentation, repetitive analysis?
Run it against a live workflow and judge it on whether it saves time and holds up to scrutiny — not on how well it demos.
Train the team, set practical guardrails, and make the pattern that works the default way of working rather than a one-off.
Connected Claude directly to the git repository so the assistant works from the real codebase and its history, not a pasted snippet — context lives where the code lives.
Claude · GitInstalled and configured review agents that read every pull request and leave substantive comments, so the first pass is done before a human reviewer opens it.
PR review agentsSet up SonarQube to analyse each pull request automatically, so code smells, coverage gaps and vulnerabilities surface at review time instead of after merge.
SonarQubeSubmitted to a peer-reviewed venue on enterprise data-platform architecture. Currently awaiting the review outcome.
A blog on the best ways to actually put AI to work in a real working environment — the workflows that earn their place, and the ones that don't.
I'm a data platform architect. My work is end to end — I design the whole system and lead the teams that build it, so what finally reaches a decision-maker is scalable, governed and genuinely trusted.
Across five organisations and four industries I've led cross-functional teams and set the technical standard for large deliveries. The stack shifts with every client; the architecture discipline is the constant.
As organisations modernise, I've taken on a second thread: helping them adopt AI optimally inside their existing delivery process, and writing about what actually works.