Data Quality

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

Write-Audit-Publish with Iceberg Tables in Snowflake

It was a Tuesday afternoon when the analyst pinged me on Microsoft Teams: “Hey, the Total Portfolio numbers just jumped 40% overnight. Did we land a whale?” We hadn’t. What actually happened was more mundane and significantly more painful. A schema change in the source system introduced a currency conversion bug. Our pipeline dutifully loaded the corrupted data into production at 3 AM, the dashboards updated by 6 AM, and the Department Head opened her morning report to numbers that looked like champagne-worthy growth.

Friday, February 27, 2026 Read

The Four Stages of Data Quality: From Hidden Costs to Measurable Value

This is the fundamental problem with data quality. You know it matters. Everyone knows it matters. But until you can quantify the impact, connect it to business outcomes, and build a credible business case, it remains this abstract thing that’s important but never urgent enough to properly fund. I wrote a practical guide to data quality last week that walks through hands-on implementation—the SQL queries, the profiling techniques, the actual mechanics of finding and fixing data issues. Think of that as the “how to use the tools” guide. This article is different. This is the “why these tools matter and how to convince your organization to actually use them” guide.

Monday, November 24, 2025 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

Continuous Integration for Data Teams: Beyond the Buzzwords

The Day Everything Broke (And How CI Could Have Saved Us) Picture this: It’s 9 AM on a Monday, and your Slack is exploding. The executive dashboard is showing impossible numbers. Customer support is fielding complaints about incorrect billing amounts. The marketing team is questioning why their conversion metrics suddenly dropped to zero. You trace it back to a seemingly innocent change you merged Friday afternoon—a simple column rename that seemed harmless enough. But that “harmless” change cascaded through your entire data pipeline, breaking downstream models, dashboards, and automated reports.

Saturday, June 28, 2025 Read