GTM Engineer School Podcast E1: "Get the (GTM Engineering) Reps" | Jorge Macias
About our guest — Jorge B. Macías
Jorge Macias is an industrial engineer turned GTM engineering wizard who scaled the first Puerto Rican YC-backed startup from zero to $3M ARR.
He's now a go-to-market engineering consultant and advisor helping B2B SaaS companies turn messy data into synchronized revenue engines.
Jorge operates and advises multiple startups on sales, PLG, and GTM engineering while sharing his workflows with thousands of GTM operators on LinkedIn. We recorded this just one month after he became a dad — proving that if he can revolutionize go-to-market operations with a newborn, there's no excuse for the rest of us.
Core takeaways
* The perfect definition of GTM engineering: "RevOps and growth hacking having a baby"
* How to replicate your best seller's "secret sauce" and scale it across your entire team
* The Series A GTM stack: Essential tools for companies approaching $1M ARR
* His favorite technographic play using job descriptions for targeted campaigns
* The two emerging skills every GTM engineer needs: patience and clarity
* Why real-life use cases beat endless LinkedIn scrolling and course consumption
* The simple LinkedIn data hack that revives stale leads every 3-6 months
* How AI transforms manual 45-minute meeting prep into automated prospect research
* Why GTM engineering is "more art than science" and what that means for you
Top quotes
Best definition of GTM Engineering: "If RevOps, marketing operations, sales operations, data engineering and growth hacking had a baby... It's turning messy data, tools that are scattered around, half-baked playbooks into one automated synchronized work."
The transformation: "Instead of having one awesome seller and a lot of mediocre salespeople, you're gonna have a lot of average salespeople, which is good for business models because it's gonna be more predictable."
On tool selection: "Look for the tools that are right for the stage that you are in your company and the stage that you are in your go-to-market journey."
Learning philosophy: "The real value in go-to-market engineering comes from practice and from building workflows that are going to be living out there in the world... New skills are like sports—you need to get the reps."
Referenced tools and resources
* HubSpot: Jorge's go-to CRM for most clients
* Salesforce: Enterprise CRM option
* Attio: Modern CRM Jorge wants to test
* Clay: Primary data orchestration and enrichment platform
* Lemlist: Preferred sequencer for multi-channel outreach (LinkedIn + email)
* Smartlead & Instantly: Alternative email sequencers
* RB2B: Website visitor tracking and identification
* Notion: Jorge's current CRM and wiki repository
* Apify: Go-to scraping tool with extensive actor library
* Phantom Buster: LinkedIn automation and network growth
* ChatGPT: Browser-based AI conversations
* Claude (Anthropic): API integrations within other tools
* Gemini, DeepSeek: Additional AI model options
* Reoon: Underrated email verifier ($80 for 100k verifications + 500 daily credits)
* NeverBounce: Alternative email verification
* Apollo, Lead Magic, Prospeo: Lead generation databases
Timestamps
* (00:00) Introduction to Jorge Macias and GTM engineering fundamentals
* (01:56) Jorge's definition: "RevOps and growth hacking having a baby"
* (02:57) The biggest transformation: Replicating your best seller's secret sauce
* (04:43) How AI scales what already works without reworking everything
* (06:38) Lightning Round: CRM preferences - HubSpot vs Salesforce vs Attio
* (07:24) LLMs: Claude vs ChatGPT for different use cases
* (07:52) Top enrichment tool: "Clay all the way baby, I'm married"
* (08:43) Most underrated tool: Reoon email verifier at $0.0008 per verification
* (10:05) Jorge's current GTM stack: Notion CRM, Clay orchestration, RB2B tracking
* (13:40) Why Notion as CRM: 8 years of familiarity and wiki integration
* (15:11) Series A GTM stack recommendations for $1M ARR companies
* (18:14) Jorge's favorite GTM plays: Technographic data + job description mining
* (21:27) Automated meeting prep: From 45 minutes to AI-generated prospect research
* (24:12) Emerging GTM engineering skills: Patience with complex workflows
* (25:09) Communication clarity: Explaining technical concepts to average users
* (27:28) Learning advice: Real-life use cases beat endless content consumption
* (28:42) Sports analogy: Practice builds mental connections, not just watching
* (29:19) Practical tip: Download LinkedIn data for automated prospect revival
* (30:42) Where to find Jorge and his new GTM engineering consultancy
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