From Textile Engineering to Data: Why I Made the Switch
Merchandising taught me more about data problems than I expected — here's the honest story of transitioning from apparel coordination into data engineering.
Shaharier Shourov
Data Analyst & Aspiring Data Engineer
Where I started
My degree is in Textile Engineering and Management. My first jobs were in merchandising — coordinating between Sales, Design, Sourcing, and Finance for global apparel brands, working on accounts like Ralph Lauren at Dekko ISHO Group, and now at New Era Cap.
On paper, that's a long way from SQL and Power BI. In practice, it's closer than it looks.
"Merchandising is a data problem wearing a fashion industry costume."
What merchandising actually trained me to see
Every day in that role was about tracking orders across systems that didn't talk to each other cleanly — SAP for one thing, Excel for another, email threads for the rest. Reconciling sample statuses, shipment dates, and vendor communication across fragmented sources is, functionally, an ETL problem. I just didn't have the vocabulary for it yet.
The moment it clicked
I started building spreadsheets to track discrepancies between what SAP said and what vendors were actually delivering. Those spreadsheets got more complex over time — until I realised what I actually wanted was a proper database and a query language, not another Excel workaround.
Why I didn't go back to school
Formal certifications — Microsoft's Power BI Data Analyst Associate, DataCamp's Associate Data Engineer in SQL — gave me the structured foundation without requiring a second degree. Paired with hands-on portfolio projects using real, messy datasets, that combination has been enough to build genuine competency.
What's next
I'm continuing to work full-time in product coordination while building out the Python side of my data engineering skill set. The long-term goal is a full transition into data engineering — but the merchandising background isn't dead weight. It's exactly why I care so much about data quality: I've lived the cost of bad data first-hand.
