About

I'm a senior data engineer and data architect. Most of my career has centred on treasury and risk data. I've built and operated pipelines for regulatory reporting, risk aggregation and financial controls.

This site records practical lessons from that work. It focuses on data engineering, data architecture, DataOps, data governance and data quality. The examples come from batch and scheduled systems.

Regulated environments put design weaknesses under pressure. Long-running processes, manual inputs, formal controls and audit duties make vague system behaviour expensive. Those examples ground the writing, but they don't restrict its audience.

The writing is for engineers, architects and operators who think about whole systems. It asks how data platforms can fail clearly, expose useful state and produce evidence through normal operation.

How I write

I use AI tools as part of my thinking and writing process. The ideas, experience and judgements are mine. AI helps me test arguments, shape structure and express them clearly. I review every article prior to publication.

AI hasn't explained failures to regulators, retrofitted controls onto legacy systems or built workflows for people who don't use Git. I have. The writing reflects that experience.

Personal views and confidentiality

This is a personal site. The views here are mine. They don't represent my employer, past employers, clients or any organisation I've worked with. I don't publish confidential systems, client information or internal employer positions.

You can find my full background on LinkedIn.