What this is
GTM stands for go-to-market: everything a company does to find buyers, win them, and keep them. Marketing, sales, customer success, pricing, and the data that connects them. A GTM engineer is the person who writes the code that makes that machine run: the pipelines, the alerts, the scoring models, the AI agents, and the dashboards that prove what worked.
This course teaches it from zero, in twelve stages. Each stage teaches the concepts, names the tools as they stand in October 2026, and then has you build one real project. Every stage ends with a quiz, a deck of flashcards, and a short list of the best resources we could find. Every fact and price was checked against its primary source when the stage was written, and the date is on each page.
Who it is for
You can read Python, SQL and JSON, or you are willing to learn as you go. You do not need to know anything about sales or marketing yet. Every term is defined the first time it appears, and every term is also in the glossary.
You do not need a company either. Every project has a no-budget path built on free tiers, test modes, sandboxes and mock data.
How each stage works
| Part | What you do |
|---|---|
| The short answer | Read it first. If you can explain it back, you know where the stage is going. |
| Core concepts | The teaching. Each concept has a worked example: numbers, a payload, or a query. |
| Key terms | Flip the cards. Say the definition before you flip. |
| The tools | The vendors and the free options, with the 2026 status of each. |
| Build the project | The real work. Tick each step as you finish it. |
| Check yourself | Five or six questions. Retry until you get all of them. |
| How to learn this faster | A drill and a five-day plan for that stage. |
| Resources | The short list. All of them are also in the resource library. |
How to learn it
The roadmap this course is built on is a list of nouns. Nouns are easy to read about and hard to remember. These five habits turn them into skill.
- Quiz first. Take the quiz at the bottom of a stage before you read it. You will get most of it wrong, and that is the point. Guessing before you study primes you for the answer: in Richland, Kornell and Kao (2009), people who tried to answer questions before reading remembered more of the text, even when their guesses were wrong.
- Build before you feel ready. Read the short answer and the core concepts, then start the project. Go back to the reading when you get stuck. The questions you hit while building are the ones you will remember.
- Test yourself, do not re-read. Retrieval beats review. In Roediger and Karpicke (2006), students who practised recalling a text kept more of it a week later than students who studied it again. The quizzes and the flashcard drill exist for this.
- Space it out. Ten minutes of the glossary drill a day beats an hour once a week. Spacing practice over days is one of the most replicated results in learning research (Cepeda et al., 2006).
- Explain it out loud, then write it down. After each stage, explain the project to an empty room in two minutes, as if to a hiring manager. Where you stall is what you do not understand yet. Then write a short public note. Stage 12 turns those notes into your portfolio.
The twelve-week plan
One stage a week, about five to eight hours each, is a pace you can hold next to a job. Read on Monday, build from Tuesday to Thursday, quiz and write on Friday.
| Week | Stage | Ship by Friday |
|---|---|---|
| 1 | Data architecture and reverse ETL | Stripe and usage data synced into CRM custom objects |
| 2 | Signal and intent pipelines | A Slack alert for three pricing-page visits in a week |
| 3 | AI SDRs and personalization | An agent that drafts one specific cold email from public work |
| 4 | PLG telemetry | A real-time alert when a free user hits the aha threshold |
| 5 | Usage-based pricing | A metered, credit-based pricing model wired to Stripe test mode |
| 6 | RevOps and attribution | A multi-touch attribution dashboard over closed-won deals |
| 7 | AI sales enablement | A battle card generated from a pile of lost-call transcripts |
| 8 | Churn and expansion | A churn-risk flag 45 days before renewal |
| 9 | Integration-led growth | A working Slack or Salesforce integration and its listing |
| 10 | Dark social and community | A survey-based model linking word of mouth to pipeline |
| 11 | The automated QBR | A script that builds a branded ROI deck |
| 12 | Teardowns and portfolio | Three public teardowns and a portfolio page |
In a hurry? Do two stages a week and skip the stretch goals. Stages 1, 4, 6 and 8 are the core: data, product signals, attribution, and retention. If you only have a month, do those four.
Set up your lab once
Every project reuses the same free accounts. Make them in the first hour, before Stage 1.
- A GitHub account and one repo for the whole course, with one folder per stage.
- Python 3.12 or newer, and DuckDB for a local warehouse you can run on a laptop.
- A Stripe account in test mode, so no real money moves.
- A free HubSpot developer test account, or a free Salesforce Developer Edition org.
- A free Slack workspace of your own, for alerts.
- An API key for one large language model provider, with a small spending limit set.
An honest promise
The post this course grew from said that if you learn GTM Engineering you will never be unemployed again. No skill can promise that, and we will not either. What we can say is that each stage leaves you with a working project you can show, and twelve of them make a portfolio that says more than any certificate.
Your progress, quiz scores and flashcard boxes are saved in this browser only. There is no account and nothing is sent to us.
The twelve stages
- 20 min
GTM data architecture and reverse ETL
Learn GTM data architecture: warehouse vs CRM, ELT, dbt, identity resolution, Stripe webhooks, and reverse ETL into HubSpot and Salesforce custom objects.
Build: Sync raw Stripe billing data and product usage through a warehouse into HubSpot custom objects, with a signed webhook receiver and a change-only reverse ETL job. - 20 min
Signal and intent pipelines
Turn website visits and intent data into fast, trustworthy sales alerts: deanonymization, Bombora-style surge, event streams, dedup, Slack and privacy law.
Build: Build a webhook router that posts to Slack when a target enterprise account visits your pricing page 3 times in one week. - 19 min
AI SDRs and Hyper-Personalization
Learn what an SDR does, how Clay and Apollo feed enrichment, how an LLM drafts 1:1 emails from public signals, and how to stay legal and out of spam folders.
Build: Build a draft-only agent that reads a consenting prospect's recent GitHub commits or latest podcast episode and writes one specific 1:1 email for human review. - 24 min
PLG telemetry
Track product events, score product qualified leads, find the aha moment, and alert sales when a free user hits it. Includes tested Python code.
Build: Build a script that sends a Slack alert the moment a free-tier account crosses an API usage threshold and matches your ideal customer profile. - 19 min
Usage-Based Pricing (UBP) and Monetization
Learn to price by usage: value metrics, Stripe Billing Meters, credits, idempotent metering and spend caps. Then build a credit burn-down ledger for AI agents.
Build: Design and build a pricing architecture that charges per agent execution and per token processed, with prepaid credits, overages and a spend cap, instead of per seat. - 24 min
RevOps and Attribution
Learn funnel math, cohort SQL and attribution models from first touch to Markov and Shapley, then build a sourced-revenue dashboard and test what it can prove.
Build: Build a dashboard that splits closed-won revenue across LinkedIn ads, webinars and cold email under five attribution models, then test it against a holdout. - 20 min
AI sales enablement and coaching
Turn recorded sales calls into coaching: pull Gong transcripts, extract objections with AI, search them by meaning, and build a battle card from lost deals.
Build: Analyze 100 lost sales calls and auto-generate an updated objection handling battle card for the sales team. - 24 min
Predictive Churn and Expansion (NRR)
Learn GRR and NRR math, health scores and churn models, then build a classifier that flags accounts with falling API usage 45 days before renewal.
Build: Build a churn-risk model that flags accounts with dropping API usage 45 days before their renewal date, and post the riskiest accounts to a channel each day. - 20 min
Integration-Led Growth
Learn how integrations drive distribution: OAuth with PKCE, Slack and Salesforce marketplaces, Zapier, embedded iPaaS, co-selling, and listing optimization.
Build: Build a Slack app with OAuth install, encrypted token storage and an install-to-activation funnel, then write and test its marketplace listing. - 24 min
Dark social and community
Learn to measure word of mouth you cannot track: self-reported attribution, LLM-coded survey answers, community ops, and honest correlation with pipeline.
Build: Launch a niche technical community and build a survey-based model that tests how word of mouth lines up with enterprise pipeline. - 23 min
The automated QBR
Learn to turn customer usage data into a branded ROI deck: ROI math, chart rules, a guarded AI summary, a CSM review gate and a quarterly schedule.
Build: Build a script that pulls customer usage data and writes a branded ROI deck with a guarded AI summary, which a CSM reviews before it ships. - 24 min
Public GTM Teardowns and Portfolio
Tear down AI pricing pages, credit systems and funnels with dated evidence, then publish three deep-dives and a portfolio that proves your judgment to hirers.
Build: Publish 3 deep-dives on how top AI companies structure their pricing pages, onboarding flows, and credit systems, and turn your 11 course projects into one portfolio.
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