August 2026
Talk about the problem before the stack
There's an irony here: the business side of a problem excites me just as much as
the technical side does. Yet after an interview, I was once told I'd only shown
the technical part. Looking back, that was an honest mistake on my part: I assumed
the people listening only cared about the stack, so that's what I led with. What
I've realized since is the opposite: going deep into the business problem, actually
understanding why it matters before touching a single pipeline, is exactly what
shows how much I care about the problem itself. The stack is just how I answer it,
not the reason I show up.
Lessons learned
September 2026
The bug is almost never where you're looking
On a customer master data project, I spent days understanding why the same
anomalies kept coming back. The lesson turned out to be simple: in a multi-source
pipeline, the real problem is almost never in the code you're staring at. It's in
an upstream assumption that quietly stopped being true. Since then, I'd rather build
lineage visibility ahead of the transformation layer than bolt on tests afterward.
Data Engineering
September 2026
Working with AI, hand in hand
During my assignment at illicado, we built an agent platform on top of our data
platform, largely using Claude. I'll admit I wasn't convinced at first. Once I
actually started working with it, I felt genuinely conflicted: it can be a
blessing and the exact opposite at the same time. On a topic I already know well,
it makes the work remarkably easy. On something new, it's just as easy to fall
into the trap of building fast and trying to understand the topic through the
code it generates, instead of going straight to the business experts who
actually know it. Lately, I've found a better rhythm: I let it do what it's
fastest at (going through documentation, drafting code), but I take the time
upfront to actually understand the topic myself, often by talking to the people
who know it best, instead of falling into that trap.
AI & Data
September 2026
Why psychology is a passion of mine, alongside data
Psychology is a passion of mine, right alongside data, even though some people
still treat it as a soft science, or worse, dismiss it outright. What convinces me
otherwise is what we're learning about the body: autoimmune conditions, chronic
illness, and even some cancers are increasingly linked to prolonged stress, not
just genetics or bad luck. That's not a fringe opinion anymore, it's an active area
of research. I take it seriously well beyond the headlines: in my day-to-day work,
understanding this makes me more empathetic toward colleagues, and it shapes how I
think about building a healthy team environment: one where people can actually
thrive, not just perform, and where looking out for each other isn't an afterthought.
If the right project came along, at the intersection of data and mental health or
wellbeing, I'd genuinely love to work on it.
Psychology & Data