Pierre Brisorgueil
Engineering Leader (Growth & GTM on B2B SaaS) - Open to freelance & advisory
ContactGrowth & GTM
I translate go-to-market needs into reliable engineering execution for operations and end users.
Activation & onboarding, self-serve flows, experimentation, segmentation, lifecycle messaging, and the tooling to run this reliably.
Recent examples: AI calls, customer care with MCP, upgrade and setup flows.
Summary
Engineering leader with 10+ years across B2B SaaS, data platforms and growth.
At TheFork (Tripadvisor), I lead Growth & GTM engineering across B2B web and mobile, billing, Salesforce and internal tooling, scaling squads (0-20 FTEs), shaping the technical vision, and driving delivery with cross-functional partners. Previously at Société Générale (Bank), I built and scaled a DataViz team of 0-18 engineers, delivering a reusable data visualization platform and supporting the scale-up of the broader Big Data organization.
I keep building outside of work to maintain technical depth, it gives me credibility with engineers and sharper judgment on trade-offs. I code with Claude and have built quite a few automations around it.
DevKit
DevKit is an open-source set of production-minded starter stacks to bootstrap modern web & mobile products faster.
Composable frontend/backend building blocks (Vue / Node / Swift), with a shared mindset around maintainability, updates, and delivery (patterns, conventions ..).
Built to start clean, ship early, with a common architecture.
comes.io
Comes.io is a consumer app that sends smart alerts when the right conditions are met for outdoor plans (e.g. surf, ski, weekends).
End-to-end product: data acquisition (scraping at scale), condition/scoring rules, automation, and user-facing UI.
Designed as a pragmatic playground to validate ideas quickly with real users and iterate on the signal quality.
l0u.me
L0u.me is a lightweight tool to simplify public-data extraction when APIs don’t exist or are inconsistent.
Focused on reliability: normalize outputs, alert on changes, and make data reusable as a building block for internal automation.
Used as a pragmatic component in small pipelines rather than a heavy platform.