About

I build the connective tissue between what a technology can do and what an organisation can actually run.

The technical foundation

I started in engineering. A New Zealand Diploma in Electrical and Electronics Engineering, then a Bachelor of Science in Electronic and Computer Systems, then a Master of Software Development, all in Wellington. In between and afterwards I worked as a software engineer at a scientific equipment manufacturer, a product developer at a university commercialisation office, and a database developer on contract to a government department.

That period matters more than it looks on a CV. It means that when an engineer tells me something is hard, I usually know whether it is hard because of physics, because of the architecture, or because nobody wants to do it.

Into product

In 2020 I co-founded a sports technology startup and spent a year doing product the way you do it when nobody is going to rescue you: talking to coaches, defining an MVP, running a live pilot with a football club, and making uncomfortable trade-offs to ship something usable. In parallel I worked as a data scientist in digital health, building analytics that clinical teams used to track patient outcomes.

Both taught the same lesson from opposite directions. A technically correct product that does not fit how people actually work is not a product.

Enterprise platforms and governed delivery

I joined a New Zealand bank in 2021 as a data engineer and product owner, and helped turn a data science workbench into a shared, governed enterprise machine learning platform. Since 2024 I have owned it: strategy, roadmap, delivery, and a cross-functional squad of five specialists.

The work has three layers.

The first is platform: reusable infrastructure, pipelines, monitoring, CI/CD and onboarding, so that a data science team can start work in days rather than weeks.

The second is governance. In a regulated environment, a model is not finished when it performs well. It is finished when risk, privacy, security, data governance, architecture and model risk have each seen what they need to see. I mapped those requirements into a single repeatable lifecycle with defined gates and artefacts, so teams follow one path instead of interpreting six.

The third is the part that is easiest to underrate: adoption. A platform nobody uses is a cost centre. Getting genuine reuse across business areas, and getting value counted where it lands rather than where it was built, is a different discipline from building the thing.

More recently that has extended into generative AI across two cloud providers, which has been a useful test of whether the governance model I built was principled or merely specific.

What interests me most

The work I find most interesting is where the problem is organisational as much as technical: emerging capability that needs to become governed, funded, adopted and durable.

I am also interested in the wider question underneath all of this. In every technology shift, a large share of the value has gone not to whoever invented the thing, but to whoever worked out how to make institutions able to use it safely. That is the question I keep circling, and most of what I write here is some version of it.

Outside work

I trained and competed in powerlifting for several years, and won a national title in 2019 in the 59kg equipped class. It is the only hobby I have that taught me anything transferable: progress is a function of showing up under load for a long time, and almost nobody is willing to.

A note on what I write

All writing here reflects general professional experience and publicly available information. It does not disclose confidential employer information, and it does not represent the views of any current or former employer.

If you want the compressed version of my background, it is on the Start here page, and the fuller version is under Experience.