Growth engineering is no longer just a niche technical role buried inside marketing or product teams. It’s becoming a critical function across the business — from acquisition and retention to sales enablement and process optimisation. And the rise of AI, particularly generative AI (GenAI), has only accelerated the demand.
The good news? You don’t need a computer science degree to get started. But you do need a growth mindset, a willingness to learn fast, and the ability to blend data with creativity — increasingly through AI-powered tools.
Let’s break down what a growth engineer actually does in 2024, and how to become one.
Growth Engineering Isn’t One Thing — It’s a Family of Roles
Traditionally, growth engineers sat closest to product and marketing. But today, the discipline spans multiple teams, each using experimentation and automation to drive compounding results. The three main types:
1. Marketing Growth Engineers
These are your full-funnel optimisers. They work on landing pages, email flows, ad creative, onboarding journeys — anything that improves acquisition and activation. In the AI era, they might use GPT-4 to generate copy variants for an A/B test, Claude to summarise customer interviews, or Midjourney to mock up creative for an offer test.
2. Sales & Revenue Growth Engineers
More focused on sales velocity, pipeline efficiency, and CRM automation. Think HubSpot workflows, Zapier chains, enrichment tools, or ChatGPT-powered playbooks that help SDRs personalise outreach. Their job? Remove friction from the buyer journey and shorten time-to-close.
3. Process & Ops Growth Engineers
Found in RevOps, CS, and internal operations teams. These are the people using AI to automate manual steps, improve data flow between platforms, or design internal experiments to increase retention, reduce support costs, or improve handovers between teams.
In all cases, the underlying principle is the same: test, learn, optimise — and increasingly, automate.
Why AI Changes the Game
What makes growth engineering so accessible today is how GenAI tools lower the technical barrier. You no longer need to hard-code every experiment or manually crunch data for every insight.
Instead, you might:
Use ChatGPT to create a hypothesis bank for upcoming tests
Use Claude to summarise 50 customer survey responses in 10 seconds
Use AI to rewrite email subject lines based on tone, urgency, or persona
Use Make or Zapier to build AI-enhanced automation across your stack
Even better, these tools are composable. You can chain together AI-enhanced steps with APIs, CRMs, and analytics tools to build lightweight experiments in days, not weeks.
According to Elena Verna, former Head of Growth at Amplitude and Miro:
“Growth is about building repeatable systems — not one-off wins. AI lets us build those systems faster and cheaper than ever.”
The takeaway? AI doesn’t replace growth engineers. It makes them more effective — and gives those without a technical background the tools to participate meaningfully.
The Skills You Need (and How to Build Them)
No matter which track you pursue, most growth engineers share a common skill stack:
Data confidence: You don’t need to be a data scientist, but you should be able to run basic SQL queries, analyse trends, and interpret dashboards.
Experimentation: A solid grasp of A/B testing principles, statistical significance, and designing tests worth running.
Systems thinking: Growth isn’t linear. You’ll need to think in loops, feedback cycles, and second-order effects.
AI fluency: Prompt engineering, tool chaining, and the ability to embed GenAI into your workflow.
If you’re coming from a non-technical background, start small:
Use ChatGPT to rework onboarding emails or summarise product reviews
Use Looker Studio to build your own conversion funnel dashboard
Build a Notion or Airtable-based CRM with AI-generated suggestions for next actions
The goal isn’t to become a developer. It’s to become dangerous enough with data, experiments, and AI to run and scale your own tests — and collaborate with those who can go deeper.
Where to Start
Growth engineering isn’t a degree. It’s a career you build by stacking wins — running tests, improving metrics, learning from failure, and automating where you can.
You can:
Take ownership of an onboarding experiment at your current company
Rebuild a lead capture flow using AI-optimised copy
Join communities like Reforge, GrowthHackers, and ProductLed
Read case studies, subscribe to growth newsletters, and try building in public
What matters most is that you start testing. AI has made the tools more accessible than ever. But it’s the mindset — curious, iterative, user-first — that sets great growth engineers apart.
Final Thought
Growth engineers are the experimenters of modern business. Whether they sit in marketing, ops, or sales, they share one goal: find leverage and scale it.
In the GenAI era, that leverage looks very different — but the fundamentals haven’t changed. Get close to the customer. Ship tests quickly. Learn faster than your competitors.
AI is the accelerant. But you’re still the one holding the match.
Growth engineering is no longer just a niche technical role buried inside marketing or product teams. It’s becoming a critical function across the business — from acquisition and retention to sales enablement and process optimisation. And the rise of AI, particularly generative AI (GenAI), has only accelerated the demand.
The good news? You don’t need a computer science degree to get started. But you do need a growth mindset, a willingness to learn fast, and the ability to blend data with creativity — increasingly through AI-powered tools.
Let’s break down what a growth engineer actually does in 2024, and how to become one.
Growth Engineering Isn’t One Thing — It’s a Family of Roles
Traditionally, growth engineers sat closest to product and marketing. But today, the discipline spans multiple teams, each using experimentation and automation to drive compounding results. The three main types:
1. Marketing Growth Engineers
These are your full-funnel optimisers. They work on landing pages, email flows, ad creative, onboarding journeys — anything that improves acquisition and activation. In the AI era, they might use GPT-4 to generate copy variants for an A/B test, Claude to summarise customer interviews, or Midjourney to mock up creative for an offer test.
2. Sales & Revenue Growth Engineers
More focused on sales velocity, pipeline efficiency, and CRM automation. Think HubSpot workflows, Zapier chains, enrichment tools, or ChatGPT-powered playbooks that help SDRs personalise outreach. Their job? Remove friction from the buyer journey and shorten time-to-close.
3. Process & Ops Growth Engineers
Found in RevOps, CS, and internal operations teams. These are the people using AI to automate manual steps, improve data flow between platforms, or design internal experiments to increase retention, reduce support costs, or improve handovers between teams.
In all cases, the underlying principle is the same: test, learn, optimise — and increasingly, automate.
Why AI Changes the Game
What makes growth engineering so accessible today is how GenAI tools lower the technical barrier. You no longer need to hard-code every experiment or manually crunch data for every insight.
Instead, you might:
Use ChatGPT to create a hypothesis bank for upcoming tests
Use Claude to summarise 50 customer survey responses in 10 seconds
Use AI to rewrite email subject lines based on tone, urgency, or persona
Use Make or Zapier to build AI-enhanced automation across your stack
Even better, these tools are composable. You can chain together AI-enhanced steps with APIs, CRMs, and analytics tools to build lightweight experiments in days, not weeks.
According to Elena Verna, former Head of Growth at Amplitude and Miro:
“Growth is about building repeatable systems — not one-off wins. AI lets us build those systems faster and cheaper than ever.”
The takeaway? AI doesn’t replace growth engineers. It makes them more effective — and gives those without a technical background the tools to participate meaningfully.
The Skills You Need (and How to Build Them)
No matter which track you pursue, most growth engineers share a common skill stack:
Data confidence: You don’t need to be a data scientist, but you should be able to run basic SQL queries, analyse trends, and interpret dashboards.
Experimentation: A solid grasp of A/B testing principles, statistical significance, and designing tests worth running.
Systems thinking: Growth isn’t linear. You’ll need to think in loops, feedback cycles, and second-order effects.
AI fluency: Prompt engineering, tool chaining, and the ability to embed GenAI into your workflow.
If you’re coming from a non-technical background, start small:
Use ChatGPT to rework onboarding emails or summarise product reviews
Use Looker Studio to build your own conversion funnel dashboard
Build a Notion or Airtable-based CRM with AI-generated suggestions for next actions
The goal isn’t to become a developer. It’s to become dangerous enough with data, experiments, and AI to run and scale your own tests — and collaborate with those who can go deeper.
Where to Start
Growth engineering isn’t a degree. It’s a career you build by stacking wins — running tests, improving metrics, learning from failure, and automating where you can.
You can:
Take ownership of an onboarding experiment at your current company
Rebuild a lead capture flow using AI-optimised copy
Join communities like Reforge, GrowthHackers, and ProductLed
Read case studies, subscribe to growth newsletters, and try building in public
What matters most is that you start testing. AI has made the tools more accessible than ever. But it’s the mindset — curious, iterative, user-first — that sets great growth engineers apart.
Final Thought
Growth engineers are the experimenters of modern business. Whether they sit in marketing, ops, or sales, they share one goal: find leverage and scale it.
In the GenAI era, that leverage looks very different — but the fundamentals haven’t changed. Get close to the customer. Ship tests quickly. Learn faster than your competitors.
AI is the accelerant. But you’re still the one holding the match.