// gtm engineer · san francisco
Henry Marble.
I build the systems
behind GTM motions.
5+ years living inside outbound sales — Cloudflare, Pave. I know where the CRM breaks, how leads go cold, and what makes all pieces of the GTM org hum. Now I build the tools to make it come to life and help reps do one thing: sell.
// beacon — gtm intelligence platform
A unified GTM intelligence platform built across five modules — each targeting a distinct failure point in outbound sales operations. One shared data layer. One MCP server. Five tools reps and RevOps teams actually use.
MCP server giving GTM reps instant access to product knowledge, objection handling, and competitive intel — from any interface they already use. The server is the product; channels are wrappers.
What's built: FastMCP server with ask_product tool · PostHog knowledge base (objections, competitors, personas, pricing) · llm.py provider abstraction (Anthropic, OpenAI, Gemini) · ingest.py pipeline · Slack bot with gap logging to Notion · Railway / Docker deployment
Closes the attribution gap Outreach explicitly admits exists: no step-level meeting attribution. Ingests sequence data from Outreach and Salesloft, maps it to Salesforce pipeline outcomes at the step level, diagnoses failure modes in underperforming steps, and generates persona-aware LLM rewrite recommendations. Includes "Ask Beacon" — natural language analytics with streaming responses and auto-generated charts.
What's built: Position-adjusted Bayesian attribution model with U-shaped multi-touch credit allocation · step intent classifier (6 types) with intent-specific thresholds · 5-module classifier pipeline with RewriteEngine deep module and LLMClient Protocol abstraction · 34KB messaging intelligence knowledge base · 6-tool FastMCP server (Railway, SSE) · Next.js dashboard with 7 views, 3 role personas (Manager, RevOps, Rep), streaming Ask Beacon, and interactive rewrite drawer · 9 Supabase migrations · test suite with boundary tests and canned LLM client
Rep signal prioritization engine. Configurable weighted scoring across multi-source account signals with plain-English reasoning per score — built for teams too small for 6sense but drowning in intent noise.
What's planned: Configurable weighted scoring model · signal ingestion adapters (intent, job change, product usage, CRM activity) · time decay functions · Claude API plain-English reasoning layer · prioritized account list UI
Account hierarchy normalization engine. Ingests hierarchy data from multiple sources, resolves conflicts, flags discrepancies, and produces a clean sellable account structure — replacing the spreadsheet every RevOps team is hiding.
What's planned: Multi-source ingestion (SEC EDGAR, OpenCorporates, CRM) · probabilistic entity resolution via Splink · conflict detection and confidence scoring · clean hierarchy output with source attribution · Claude API disambiguation layer
AE/SDR pairing intelligence layer. Unified account-level touchpoint timeline across both reps, pre-inbound attribution surfacing, and pairing-level messaging performance — the operating environment paired sales teams don't have anywhere else.
What's planned: Unified touchpoint timeline (Outreach + Salesforce) · pre-inbound attribution detection with configurable lookback window · dead messaging flagging · shared account book view · pairing performance analysis via Loop data layer
Full Salesforce build simulating the RevOps infrastructure of a fictional Series B security SaaS. Custom data model, lead routing, SLA enforcement, and operational reporting — the kind of system a GTM Engineer would own on day one.
What's built: 7 custom Lead fields · 6-rule routing chain (segment + persona → rep or queue) · Tier 1 SLA breach Flow · post-conversion Apex trigger with test coverage · 4 operational reports · GTM Ops dashboard · scoped permission set
HubSpot mirror of the DOOM Inc GTM stack — same fictional Series B security SaaS, different CRM. Built to demonstrate cross-platform RevOps fluency: contact lifecycle management, deal pipeline configuration, and automated workflows without writing a line of Apex.
What's built: Custom contact properties (ICP Fit, GTM Segment, Persona, SLA Tier) · deal pipeline with stage-gated automation · enrollment workflows for SLA breach alerting and post-conversion follow-up · active lists for segment-based reporting · operational dashboard surfacing pipeline health by segment and rep
iOS app that tracks your kitchen inventory, predicts expiration using USDA shelf life data, and surfaces recipes built around what's about to go bad. Built solo from zero to TestFlight. The average household wastes $1,500+ in food per year — Kitchi starts from what you already own.
What's built: Receipt scan → pantry (Claude Haiku OCR) · USDA FoodKeeper shelf life DB (612 records) · Spoonacular recipe matching · household sharing with RLS-enforced data isolation · shopping list ↔ pantry sync · CO₂ + $ impact tracking
CRM / GTM
Code / Data
Automation
Dev Tools
I've spent 5+ years working inside GTM motions — Cloudflare BDR, Pave SDR. Sequences, CRM hygiene, pipeline data, tool sprawl. I know where the systems break because I've felt it firsthand: leads routed to the wrong rep, Tier 1 inbounds sitting cold for hours, no visibility into queue depth, conversion with zero handoff.
I'm building the technical skills to fix that. Currently working through Salesforce development, GTM systems design, and shipping my own app on the side.
UC Berkeley, Anthropology. UCLA Extension, Cybersecurity Boot Camp.