I'm a lead data engineer in Colombo. This is where I write about the tech I work with, the books I'm reading, and what life has been teaching me. Everything here is my own view. More about me.
I spent the last few months building an open-source route optimizer for delivery riders in Colombo. It plans the best order of stops, predicts traffic by time of day and holidays, and costs nothing to run.
Reading Daniel Kahneman's "Thinking, Fast and Slow", completely transformed how I approach technical pitches and leadership buy-in. He proved that human beings are hardwired for loss aversion, meaning the psychological pain of losing something hurts twice as much as the pleasure of a gain. If you want to stop stakeholders from defending failing legacy systems, you have to stop selling the upside of the future and start highlighting the active bleed of the present.
Most people stop reading your emails after just 200 words. By applying the principles of Smart Brevity, you can cut the fluff and deliver your main point immediately. Learn how I transformed my writing style to get faster responses and keep my audience deeply engaged.
The big picture: In 2012, the Harvard Business Review famously crowned data scientist as the "sexiest job of the 21st century." The logic was simple: a massive shortage of talent capable of making sense of big data. Then Generative AI exploded onto the scene. Today, the role isn't dying, but the traditional definition of a data scientist is officially extinct.
Anthropic’s recent pledge to put roughly $48 billion in equity into a public benefit fund, and its CEO's push for an AI-funded Universal Basic Income (UBI), aren't just Silicon Valley thought experiments. They are blaring alarm bells. Generative AI is rapidly obliterating the exact jobs Sri Lanka's developing economy relies on for survival.
The discourse surrounding Sri Lanka’s Information Technology and Business Process Management (IT/BPM) sector frequently oscillates between two extremes: overly optimistic promotional narratives of a "$5 Billion Digital Dawn" and pessimistic warnings about the devastating impact of skilled talent migration. To understand the true trajectory of this sector in 2026, we must look past the anecdotes and analyze the raw data.
If you've worked in data long enough, you know the quiet pain. Your warehouse is full of tables called factsales, dimstore, and dimproducts. Your business, meanwhile, talks about customers, shipments, and cold-chain breaches. Every time someone asks a "simple" question, a domain expert has to sit down and translate it into joins, filters, and column names. That translation tax is the thing slowing everyone down — and it's exactly the thing Fabric IQ is built to remove.
If you have spent any real time running Spark in production, you know the pattern. A job that usually finishes in eight minutes is suddenly stuck at "199/200 tasks completed" for forty-five minutes.
Most data teams hit the same wall sooner or later. You have the same real-world thing — a customer, a company, a patient — sitting in three or four systems, and not one of them agrees on how to identify it. The CRM calls it ACME Corp with id 1042. The billing system has Acme Corporation Ltd with id BX-77. A spreadsheet someone imported last year says acme corp. and has no id worth trusting. You need a single master record, and you need a way to point every transaction back to it.
Short summaries of official docs, written by an AI agent I built. Each one cites its sources, but I don't edit them, so treat them as a starting point.
Picked from the posts you've opened on this device, tied to an anonymous id rather than you.