About me
I'm a Platform Data Engineer based in Cheshire, UK. My work sits at the intersection of data engineering, platform reliability, and AI — building the systems that ingest, transform, and surface data, and more recently the agents that ask questions about it at 2am so the on-call engineer doesn't have to.
Before I landed in tech full-time, I spent a decade in commercial and operational roles — managing teams, contracts, and multi-million-pound budgets. It turns out that background is genuinely useful: I can translate between "what does the business need" and "what do we actually need to build" without losing either thread. Or my mind. Usually.
Outside of work you'll find me fuelled by an irresponsible amount of coffee, tinkering on a side project, or over-engineering something that could have been a spreadsheet. I try to spend as much time as possible with my family — which these days includes acting as a surprisingly committed Pokémon card advisor to my son. The market research involved is, genuinely, not trivial.
I also play darts, which I enjoy partly for the game and partly because it gives me a very legitimate reason to spend time in the garden room. Said garden room is equal parts man cave, bar, and the single greatest home improvement decision I've ever made — a fact I mention mostly to balance out the DIY project list, which remains stubbornly "nearly done" on all other fronts.
Tech stack
Languages
Data & Storage
Transformation & Orchestration
AI & Agents
Cloud & IaC
DevOps & Quality
What I do
Software Engineering
Backend services and APIs in Go and TypeScript. Clean architecture, sensible abstractions, and code you can come back to six months later without immediately needing a lie-down.
Data Engineering
Large-scale pipelines, PySpark, dbt, and getting billions of rows from A to B reliably — and without 3am pages.
AI & Agents
Production AI agents with RAG, vector retrieval, and tool-use. The kind that actually ship and do useful things in the real world.
Platform & Infrastructure
Containers, cloud, IaC, and the unglamorous-but-essential bits that keep everything running quietly in the background. The kind of work nobody notices until it stops working.
What I can deliver
AI & Intelligent Agents
Design and ship production AI agents that do genuinely useful things — RAG over internal knowledge, tool-use for live investigation, and frontends that surface it accessibly. Not demos. Systems that run in production and quietly save people a lot of time.
Real-Time Data Integration
Build ingestion systems that move data reliably from source to platform — CDC, event streaming, schema evolution, and data contracts included. The kind of thing that holds together even when the upstream system decides to reinvent itself without telling anyone.
Platform Cost & Efficiency
Identify where a platform is spending money it doesn't need to and do something about it — migrating workloads, re-architecting pipelines, right-sizing infrastructure. The kind of work that tends to get a lot of stakeholder enthusiasm once the savings land.
Scalable Pipeline Estates
Build and evolve large pipeline estates that process data at scale, with proper test coverage and the kind of documentation that means the next engineer isn't starting from scratch. Migrating legacy workflows to modern tooling without breaking everything downstream.
Background
Before data engineering, I spent a decade in senior commercial and operational leadership — managing teams of 15+, multi-million-pound contracts, and the kind of stakeholder conversations that make you very precise, very quickly. A Business Management degree from The Open University (seven years part-time, because why do things the easy way) and a BSc in Computer Science from Teesside round it out on paper. The real education was probably the decade in between.