Redefining Growth Engineering at Opendoor and Masterclass, with Alexey Komissarouk

After four years at Opendoor, Alexey led the growth engineering team at Masterclass during a transformative period. Now, he shares his expertise by advising startups and teaching growth engineering at Reforge. In our conversation, he delves into the essence of growth engineering, its evolution, and the increasing importance of statistical rigor, tooling, and AI.

To Alexey, growth isn’t just another application of software engineering—it requires an entirely different mindset. While traditional engineers focus on improving a product’s core functionality, growth engineers are tasked with optimising the customer journey across the entire funnel.

Alexey emphasises that growth engineers operate with a different set of priorities compared to their traditional counterparts. Instead of aspiring for  long-lasting code, they focus on rapid experimentation, being comfortable with a 30% success rate.

“You’re not building a skyscraper—you’re building a tent. Tents need to be sturdy enough to serve their purpose, but are not meant to last forever. That’s a fundamental shift in how you approach quality and success.”

This mindset allows growth engineers to move quickly, iterate based on results, and avoid getting bogged down by over-engineering.

What makes a successful growth engineer? According to Alexey, it’s a combination of technical skills, business acumen, and a willingness to experiment. He categorises the key traits into three main areas.

Speed and Adaptability
Growth engineers prioritises speed over perfection. The goal isn’t to create flawless solutions but to learn quickly what works and what doesn’t.

Understanding the Business Context
Many engineers lack a strong grasp of marketing metrics or growth principles like CAC (Customer Acquisition Cost) and LTV (Lifetime Value). Alexey makes it a point to teach these concepts to growth engineers to help them make informed decisions. “Even basic business knowledge, like understanding EBITDA or contribution margins, opens an engineer’s eyes to how their work impacts the company.”

Embracing Statistical Rigor
Experiments are the backbone of growth engineering, but they only work if conducted properly. Without statistical rigor, teams risk declaring false positives as successes, leading to wasted resources.

“A company that reached out to me saw its key activation metric fall 3x, over the course of a year despite having a team dedicated to optimising the experience.  How did this happen? First, they weren’t closely monitoring the metric (both globally and after rolling stuff out). Second, when they did roll things out, they didn’t use proper A/B testing to see if it helped or hurt, declaring a win and moving on.”

Alexey went so far as to dedicate a talk to the topic of Growth engineering DNA which is available on Youtube.

In the world of growth engineering, experimentation is everything—but only when backed by robust methodologies. Poor statistical practices can derail even the most well-intentioned efforts.

“I’ve seen teams ship experiments that seemed like winners, only to realise months later they weren’t moving the needle—or worse, they were actively hurting conversion rates.”

He recommends several approaches to ensure statistical integrity. First, using tools like Eppo and Statsig to enforce rigor by limiting premature analysis and ensuring valid results. These platforms help eliminate common pitfalls, such as peeking at incomplete data or ignoring statistical significance thresholds.

Then, empowering your team with data. When data teams are unavailable, engineers must step in with enough statistical knowledge to challenge flawed processes. “Even basic awareness of concepts like false positives or sample size requirements can help engineers flag issues before they spiral into costly mistakes.”

This commitment enables teams to build trust in their results, making decisions that are not only faster but also more reliable.

For organisations considering growth engineering, velocity is everything. Alexey stresses that growth should always begin as a company-wide priority, not an isolated function.

“When you create a ‘growth’ title too early, you risk signalling that it’s no longer everyone’s responsibility. Early-stage company founders should drive growth directly.” There are two critical milestones for introducing growth-specific roles.

Post Product-Market Fit
Growth roles, whether a dedicated team or individual, should only emerge after product-market fit has been achieved. This ensures that growth initiatives amplify a working product rather than attempt to fix fundamental issues.

Scaling Beyond $5 Million ARR
Growth engineering teams are resource-intensive. Alexey advises waiting until a company generates $5 million in annual recurring revenue before hiring a dedicated team. Until then, leveraging MarTech tools can provide many of the same benefits without the overhead. “Before investing in a full growth team, squeeze as much value as possible out of existing tools. Once you’ve hit significant scale, custom engineering will unlock bigger wins.”

Managing a growth engineering team requires a nuanced approach, one that balances creativity with accountability.  This means empowering engineers to ideate alongside product managers.

“A high-functioning growth team isn’t just executing a PM’s roadmap. Engineers, designers, and analysts should be contributing ideas based on the themes set by leadership.”

Alexey suggests fostering ownership through shared context, by educating engineers about customer behaviour and the company’s core metrics. This helps them make informed contributions.

Another important elements for the teams is flexible prioritisation – Alexey advocates for aligning on metrics rather than rigid tasks, allowing teams to adapt quickly based on results.

Theme based roadmaps are the last piece of the puzzle: by focusing on strategic themes instead of specific experiments, teams can stay aligned on goals without stifling creativity.

“When you give engineers the tools and context to think like PMs, you get more innovative solutions—and a team that feels invested in the outcomes.”

One of the cornerstones of effective growth engineering is prioritisation. Without a clear system, roadmaps often default to a PM’s intuition—an approach that limits innovation.

“You need a system where ideas compete based on merit, not hierarchy. Otherwise, you miss counterintuitive wins, the ones that don’t ‘feel’ right but end up driving massive results.”

At Masterclass, Alexey advocated for a dual-step prioritisation framework:

Brainstorm and Rank
Teams collaboratively generate ideas, then rank them based on criteria like ease of execution, potential impact, and confidence in success.

Refine the Shortlist
The top-ranked ideas are subjected to deeper analysis, including dollar-value impact estimates and detailed engineering assessments. This ensures resources are allocated to experiments with the highest ROI.

“Counterintuitive wins often emerge from this process. It’s a great feeling to be proven wrong by the data—it means you’re learning.”

Alexey sees AI as a critical force in reducing the bottlenecks that have historically slowed growth engineering. By automating tasks that once required significant human effort, AI is enabling faster, more scalable experimentation.

“With AI agents, you can assign them a task like improving a headline or optimising a push notification. They’ll ideate, test, and iterate autonomously, freeing up your team to focus on higher-order challenges.”

He highlights specific breakthroughs AI has brought to the field:

Dynamic Content Optimisation
AI tools can now personalise content, such as email subject lines or landing page copy, across dozens of languages. This was previously impossible without massive human effort.

Automated Experimentation
Companies like Coframe are already leveraging AI to manage entire landing page flows, running experiments continuously with minimal human oversight.

Strategic Insights
AI isn’t just about execution; it’s also reshaping how teams think about growth. By analysing vast datasets, AI can uncover patterns and opportunities that would take humans weeks to identify.

However, AI’s success depends on maintaining oversight:

“Your AI agents might run experiments, but they’ll occasionally generate ideas that don’t align with your brand. Human approvals are still necessary to ensure quality.”

As AI and automation evolve, Alexey envisions a shift in what it means to be an engineer. He believes that growth engineering’s focus on metrics and business outcomes positions it as the natural successor to traditional software engineering.

“More and more parts of the code will no longer require traditional software engineering. The value will lie in understanding the business and using that insight to move key metrics.”

Growth engineers—those who integrate technical expertise with a deep understanding of customer behaviour and company strategy—will be in higher demand than ever. This shift will require engineers to adopt a more business-oriented mindset, working seamlessly with product, marketing, and leadership teams.

“The engineers of the future won’t just write code—they’ll be translators, bridging technical execution with business impact. That’s where growth engineering thrives.”

Reflecting on his Reforge course, Alexey outlines the importance of understanding the philosophy behind growth engineering. It’s not just a set of practices but a way of thinking that empowers engineers to innovate and drive measurable results.

“Growth engineering is fundamentally different from traditional engineering. Once you understand that, you can rethink hiring, training, and execution to achieve better outcomes.”

Whether through AI-driven experimentation, rigorous prioritisation, or building collaborative teams, Alexey’s approach offers a blueprint for scaling companies in an era defined by rapid change.

For those considering a career in growth engineering—or simply looking to optimise their existing efforts—Komissarouk offers a few final pieces of advice:

1. Focus on Learning
   “Understand the why behind your work. Knowing how your code impacts the business makes your job more meaningful.”
2. Embrace Experimentation
   “Get comfortable with failure. Most experiments won’t succeed, but the lessons they provide are invaluable.”
3. Think Beyond Code
   “The best growth engineers aren’t just technical—they’re curious about psychology, statistics, and customer behaviour.”

As the discipline continues to evolve, Alexey remains optimistic about its potential to reshape how companies grow. His insights offer a clear roadmap for teams looking to stay ahead in a competitive landscape.

If you liked these ideas and frameworks, make sure to check Alexey’s Reforge class where you can dive a lot deeper and discover all the best tools to engineer growth. He also publishes articles and other interesting thoughts on his website. Finally, Alexey is a growth advisor to startups and scale ups– hit him up if you have any needs!