Data Engineering

You Can't Incentivise a Pipeline That Doesn't Break

I worked alongside a data engineer who was, by every formal measure, the best performer on the team. He was also quietly destroying the platform. Not maliciously. He was optimising for the thing being measured. His work shipped fast because he skipped the edge case analysis. He closed tickets at first resolution without ever checking whether the underlying pattern would recur. He didn’t review anyone else’s PRs (not his KPIs, so why would he?).

Saturday, July 4, 2026 Read

Your Team Already Has Patterns. They Just Don't Know It.

When I started a new role, one of the first things I did was try to understand how data moved through the system. Not the dashboards, not the data models — the pipes. Where did things come from? How did they get in? What happened to them along the way? There were somewhere between twenty and thirty source systems feeding the platform. Not a massive number, but enough to tell a story when you looked at the ingestion layer all at once. What I found was that all the pipelines had originated from two base templates. A sensible starting point. The kind of thing a small team puts in place early to stop complete chaos.

Saturday, June 27, 2026 Read

Keep Moving

Some days I open my laptop and by 5pm I genuinely cannot tell you what I did. Not because it was complicated. Not because there were emergencies. The stand-up happened. A few Teams messages were sent. A ticket was groomed. A document was “reviewed”. A meeting was attended where everyone agreed something was important and then the meeting ended and nothing changed. And then somehow it was evening and the pipeline I meant to fix was exactly as broken as it was in the morning.

Tuesday, June 23, 2026 Read

Five Worlds of Data Engineering

You watch a conference talk about implementing data contracts, and nobody mentions that the advice assumes you have multiple teams producing data — which you don’t. You read a post declaring “if you’re still using stored procedures in 2026, you’re doing it wrong,” and the comments erupt. Half the people are nodding along. Half are furious. Both sides are right. They’re just living in different worlds and don’t realise it.

Saturday, May 2, 2026 Read

Stop Building Salesforce Integrations From Scratch

Let me tell you about Marcus. Marcus was on a team I led a few years back. Sharp, motivated, the kind of engineer who actually read documentation before writing code. When the business asked us to get Salesforce data into our warehouse, Marcus volunteered. He’d done API work before. He figured a few weeks, tops. He scoped it carefully. Built a Python service that authenticated via OAuth, pulled Account, Contact, and Opportunity objects through the Bulk API, flattened the nested JSON into relational tables, handled pagination, managed rate limits. Wrote solid tests. Documented everything. The kind of work you’d point to in a code review and say this is how it’s done.

Saturday, April 4, 2026 Read

Your Data Model Isn't Broken, Part II: The Refactoring Playbook

In [Part I], I made the case that your legacy data model isn’t the disaster it looks like. That the strange WHERE clauses, the bridge tables nobody can explain, and the slowly-changing-dimension-within-a-slowly-changing-dimension aren’t bugs — they’re business rules earned through years of production reality. I argued that big-bang rebuilds fail at alarming rates, that the complexity you’re fighting is mostly essential rather than accidental, and that the impulse to “start from scratch” is driven more by cognitive bias than by engineering judgment.

Saturday, March 28, 2026 Read

Your Data Model Isn't Broken, Part I: Why Refactoring Beats Rebuilding

In the early 2000’s - Netscape’s decision to rewrite their browser from scratch was the single worst strategic mistake a software company could make. At the time, Netscape was winning. They had the dominant browser. They had market share. They had momentum. And then they decided the codebase was too messy, too tangled, too hard to work with — so they threw it all away and started over. Navigator 4.0 became the foundation for a rewrite that would eventually ship as version 6.0. There was no 5.0. Three years of development. No shipping product. And while Netscape’s engineers were busy building their beautiful new browser in a vacuum, Internet Explorer ate their lunch, their dinner, and most of their market share.

Saturday, March 14, 2026 Read

12 Steps to Better Data Engineering

Let me tell you about the moment I stopped trusting architecture diagrams. I was three days into a new role, getting up to speed with the data team. Smart people. Modern stack. On paper, everything looked right. They walked me through a beautiful data platform diagram: clean lines, labelled layers, colour-coded domains. It looked like something you’d see in a data conference. Then I asked a question that changed everything: “Can you rebuild your finance table from scratch right now?”

Saturday, March 7, 2026 Read

The CSV Test Suite Nobody Writes

In October 2020, roughly 16,000 positive COVID-19 test results vanished from the UK’s public health reporting for nearly a week. Not because the tests weren’t run. Not because the labs didn’t report them. The results were collected, transmitted, and received — inside CSV files. The problem? Public Health England was importing those CSV files into Microsoft Excel’s legacy .xls format. The format has a hard row limit of 65,536. When the files grew past that limit, Excel didn’t throw an error. It didn’t warn anyone. It just silently dropped the extra rows. Sixteen thousand people who tested positive for a deadly virus during a second wave went untraced. An estimated 50,000 of their contacts were never notified. And the system this happened in? Part of a £12 billion Test and Trace programme.

Wednesday, March 4, 2026 Read

The Duct Tape Data Engineer

The Engineer Who Ships I want to tell you about a data engineer I worked with. Let’s call her Sarah. Sarah had a reputation. When business stakeholders had an urgent question—the kind that arrives at 4 PM on a Friday with the CEO’s name in the subject line—they went to Sarah. Not to the senior architect with the impeccable data model. Not to the platform team with their carefully orchestrated Airflow DAGs. They went to Sarah.

Saturday, January 24, 2026 Read

When Your Data Quality Fails at 9 PM on a Friday

When everything goes wrong at once It’s 9 PM on a Friday. You’re halfway through your second beer, finally relaxing after a brutal week. Your phone buzzes. Then it buzzes again. And again. The support team’s in full panic mode, your manager’s calling, and somewhere in Melbourne, two very angry guests are standing outside the same Airbnb property—both holding confirmation emails that say the place is theirs for the weekend.

Saturday, November 22, 2025 Read

Building AI Agents with Claude Code

Introduction Imagine you’re reviewing a pull request with dozens of SQL files, each containing complex queries for your data pipeline. You spot inconsistent formatting, or syntax which doesn’t work with your infrastructure. Sound familiar? It’s common for data professionals to struggle with maintaining consistent SQL standards across their projects, especially when working with specialized platforms and it can be time consuming to review these elements within a peer review. It would be better use of time to focus on the hard thinking elements, like logic etc. However these small syntax or style issues, can be distracting. Well at least they are for me.

Saturday, September 13, 2025 Read