Hi everyone, I’m Ken. Really glad to be here! :blob-wave: 📍 Location: Toronto, Canada 🍁 🧍♂️ About Me: I’ve spent the last 5 years in RevOps, and I’ve been the first RevOps hire twice – once inheriting a fractured GTM stack from a fractional agency at a Series B fintech, and once building the complete infrastructure from scratch as the first business hire at a seed-stage startup. Between those roles and consulting work with 50+ B2B SaaS companies, I’ve shipped a lot of systems (Salesforce architecture, Make.com workflows, AI-powered lead scoring, MEDDIC implementations) and learned what breaks at scale. That said, I still feel like I’m figuring things out. Most of my experience has been at smaller companies (7-100 people), so I’m here to learn from folks who’ve operated RevOps at real scale – the problems you hit at 500-5,000+ people, how mature orgs handle data governance, what enterprise-grade forecasting actually looks like. 🫴 What I can offer: Hands-on experience building Salesforce instances from the ground up, designing Make.com/Clay automation workflows, implementing PQL scoring models, and the technical problem-solving that happens when systems don’t talk to each other. I’ve probably broken and fixed whatever integration issue you’re currently troubleshooting. Happy to share specific implementation patterns, trade war stories, or just talk through messy RevOps problems. 🕵️ What I’m looking for: My next full-time RevOps role, ideally where my technical depth (tooling, admin work, data structures, workflow design) supports strategic problem-solving rather than being the whole job. Since undergrad, I’ve been trained to focus on the “why” before the “how” – really understanding what’s causing a problem and what the ideal solution looks like before building anything. I’m looking for a team that values that diagnosis phase, not just fast execution, and am open to US/Canada remote roles Also here to learn from operators who’ve scaled beyond where I’ve been. I’m genuinely curious how you think about problems differently at larger companies, what patterns you’ve seen work or fail, and the hard-earned lessons that only come from operating at scale. Looking forward to learning from all of you, and I’m always up for a virtual (or in-Toronto) coffee chat! ☕
I’ve spent hours or days on critical scraping prompts, compared 100 results, manually checked, then looked again etc
What I want is to import a URL, a LinkedIn URL, and then try to enrich the person and send the results back to the webhook, whether that person was enriched or not.
