Messy data in,decisionsout.

I trace dirty ingested data to its source, build QA checks, and turn customer datasets into decisions with SQL and BI tooling.

500GB+

raw broadcast data parsed

70%

ops efficiency via automation

50%

community engagement lift

About me

I am Vansh Bordia, a data analyst who likes dirty data. Tracing a broken metric to its source, building the QA check so it never breaks again, then turning the clean dataset into a decision: that is the whole job and I love it.

Studying Data Science and AI at IIT Madras, I practice this on real systems: half a terabyte of broadcast data parsed into analytics, a validated PyPI library, and BI work tied directly to business outcomes.

Why me

I do the unglamorous work that makes analysis trustworthy: tracing dirty ingested data to its source, building QA checks, validating schemas, then turning clean datasets into decisions.

Final-year DS & AI student doing exactly this work: parsing raw Riot broadcast data into structured analytics, validating with schema checks, and presenting findings so stakeholders can act. Comfortable owning output end to end and documenting so the team can reuse it.

500GB+ of raw Riot broadcast data parsed into structured analytics

PyPI data library with retry, rate-limiting and validation

SQL + Power BI insights tied directly to business outcomes

Role stack

SQL / Python / Power BI / Tableau / Excel / Data Cleaning / QA Checks / Dashboards / A/B Testing / Google Analytics / Statistics / Reporting