Yashika Hemnani

Most revenue teams run on broken pipes. Marketing generates leads. Sales can't find them. CRM data is stale. Reports don't match. Nobody knows which campaign actually drove the deal. I build the systems that fix that — and I analyze the data that tells you why it broke in the first place. My background spans business analytics, marketing strategy, and GTM engineering. That means I understand both the business problem and the technical solution. I'm not a pure engineer who learned marketing terminology, and I'm not a marketer who learned to write SQL. I sit exactly at that intersection — where GTM engineering meets marketing intelligence. Before moving into GTM infrastructure, I spent years in data analytics and marketing operations — tracking campaign performance, decoding consumer behavior, and turning messy datasets into decisions. That path gave me something most engineers don't have: I know what it feels like to stare at a broken attribution report at 2am before a campaign launch. That's why I approach GTM differently. I don't just build pipelines — I understand what flows through them, what the numbers mean, and how they connect back to real marketing and revenue outcomes. Right now I'm building RevenueOS — a modular GTM data platform that handles lead capture via FastAPI, enriches prospects through third-party APIs, routes them by territory and score, syncs bidirectionally with CRMs, and tracks attribution end to end. It's not a demo. It's a working system with real architecture decisions behind it. Technical stack: Python, FastAPI, SQL, REST APIs, Power BI, HubSpot, Salesforce, webhook infrastructure, AI-powered lead scoring. Whether you're building a GTM engineering practice, scaling a RevOps function, building a marketing analytics capability, or trying to make your revenue stack actually talk to itself — I'd like to be part of that conversation. 📌 RevenueOS on GitHub → github.com/Yashikaa07/revenueOS