By day, I’m a data engineer in the adtech measurement space - the person responsible for why your campaign metrics actually add up (or, more accurately, responsible for figuring out why they don’t). I build data platforms that wrangle messy, multi-channel campaign data into something resembling truth. It involves a lot of pipelines, a fair amount of debugging at inopportune hours, and a deeply unhealthy familiarity with the phrase “eventual consistency.”
I did my Master’s in Computer Engineering (AI/ML) at the University of Waterloo, where I also worked on zero-effort health monitoring at UbiLab - which is exactly as cool as it sounds and twice as humbling to build. Before grad school, I spent a few years as a Data Engineer at ZS Associates (data warehousing and BI for pharma clients) and at Airtel (real-time systems, telecom network metrics, and the kind of scale that makes you rethink everything you learned in class).
The things that keep me up at night in a good way: distributed systems, stream processing, ML infrastructure, and whatever new thing is confidently being declared “the future of data” this quarter. I have opinions. Some of them are even well-reasoned.
This blog is where I think out loud - about data systems, adtech, measurement quirks, and the edge cases that never make it into the whitepaper. Some posts are lessons from production. Some are breakdowns of ideas I found interesting. Some are me arguing with conventional wisdom just to see if it holds up.
Fewer buzzwords. More first principles. Occasional strong takes.