About Me
Junior data analyst exploring the fashion world through data to see what really comes to the surface.
Why bulendre?
If you ask a Romanian about “bulendre”, they’ll think of bags of old clothes, not a catwalk. Now imagine the same word said as if it were French. It almost becomes a chic brand! You could picture it on a perfume bottle. That gap between meaning and sound is why I chose it. I’m less interested in the illusion around clothes and more in what the data underneath actually shows.
Technical Skills
SQL
Relational querying, joins, transformation, aggregations, and stored procedures.
Excel
Data cleaning, analytical functions, pivot tables, dashboards.
Power BI
Transforming data, modelling relationships, interactive visuals, and dashboard visualization.
Python
Data analysis with pandas: cleaning, aggregation, visualization, and basic ML.
Project focus
I didn’t start with a grand plan to “fix” fashion. It began with a simple realisation: the way we make and buy clothes today is brand new in historical terms. For centuries, garments were built slowly, altered over time, and made for a specific person. Now, in only a few decades, we’ve normalised fast cycles, global supply chains, and closets full of items we barely wear.
I’m not trying to turn that past into a fairy tale, and I’m not interested in attacking specific companies. Some brands are clearly trying to change things. But the system as a whole still causes enormous harm. The old world won’t return, and there’s no point crying over an idealised version of it.
Since we can’t roll history back, I prefer to focus on what’s left in our hands: the data these systems produce every day. What we do have are the footprints this system leaves behind: prices, inventories, shipping routes, working hours, ratings, returns. All of that is data.
My focus is on using that data, with the skills I currently have, to ask small, concrete questions about how fashion works today and how it might work better tomorrow. Bulendre.org is where I document those questions and experiments, project by project, as a junior data analyst who believes that better information can support better decisions.