Data Analyst — Python · SQL · Power BI I turn messy tables into answers that still make sense after the fifth “why?” — clean joins, explicit definitions, and narratives you can defend when the room goes quiet.

I did not come to data because spreadsheets sounded easy — I came because I enjoy turning vague questions into something testable. A number on its own is rarely interesting; what hooks me is the chain from definition → query → chart → decision. That is why my public work spans different shapes of data and surfaces: mobility (Santander Cycles in R), structured sport results (Premier League in Python), transactional retail (Apple global sales in MySQL with views and stored procedures), plus recent shipped products — Finoks (AR/AP agent with a React UI and guarded autonomy) and a Telegram fitness assistant wired to Claude, Supabase, and provider integrations.

Practically, that means I am comfortable owning a slice end-to-end: load and clean, document assumptions, explore, visualise, and (where SQL allows) leave the next analyst a procedure instead of a one-off SELECT — or ship an API and UI where the artifact is behavioural, not only a notebook. My GitHub profile and repos are the proof surface — open the code without my narration.

I work part-time on the floor at Aqua Shard while studying full-time, which sharpened pace, accuracy, and communication under pressure — the same habits I bring when presenting findings. I am looking for a Data Analyst internship (consulting-flavoured teams especially) where I can support client delivery with rigorous SQL/Python and clear reporting — including Power BI as I grow that stack (see upcoming work below).