Growth Engineer

Location: Bengaluru (Hybrid)  

Employment Type: Full-time 

Build the engine that turns a great product into a great business. 

At Flywl, we're building an AI-native revenue platform for the cloud ecosystem. 

A great product doesn't grow on its own. Someone must relentlessly test, measure, and rebuild the parts of the funnel that decide whether people discover Flywl, activate, convert, and stick around. 

We're a small team building software where AI isn't a feature layered on top. It's fundamental to how the product works and how we build it — and that includes how we grow it. 

We're looking for an engineer who wants to turn growth into a system: build an experiment, measure it, learn from it, and do it again — at a pace most teams can't match. 

The Role 

We're looking for a Growth Engineer. Not a marketer. Not someone running ad campaigns. Not someone waiting for a roadmap handed down from the product. 

We're looking for someone who treats growth as an engineering problem — who can look at signup, activation, or conversion and immediately start forming hypotheses, shipping tests, and reading the data. 

You'll work at the intersection of product, engineering, and business — instrumenting the funnel, running experiments across onboarding and conversion, and building the internal tools that let the rest of the company test ideas without waiting on you. 

The tempo: we're aiming for something closer to Duolingo's 30 experiments a week than the quarterly test-and-learn cycle most teams run. Some weeks you'll ship a pricing experiment. Other weeks you'll build a self-serve onboarding flow, wire up an attribution pipeline, or fix a broken analytics event before it costs a week of bad data. Experiments are the unit of work here — you measure success in decisions made and dollars moved, not tickets closed. 

What You'll Own 

You'll own outcomes tied to real business metrics. Not a backlog. 

That means you'll: 

• Design and ship experiments across acquisition, activation, conversion, and retention, end to end. 
• Instrument the product so every experiment produces a trustworthy answer — not just a shipped feature. 
• Build and run a continuous A/B testing program, from hypothesis to rollout or rollback, at a cadence of [X] experiments a week. 
• Build self-serve tooling so product, marketing, and founders can test ideas without engineering as a bottleneck. 
• Own the experimentation and analytics platform — feature flags, event tracking, dashboards — as it scales with the company. 
• Design, ship, and instrument AI-powered growth surfaces — not just use AI to move faster, but build the AI into the product experience itself: smarter onboarding, personalised flows, AI-assisted lifecycle messaging. 
• Build evals and guardrails around those AI surfaces, and measure whether they actually move the number — the same experimentation discipline you apply to everything else, applied to AI features. 
• Work across backend, frontend, and data plumbing, choosing the fastest path to a real answer over the most elegant one. 
• Partner directly with founders and product on which metrics matter and which experiments are worth running. 

What Makes Someone Successful Here 

The engineers who thrive in this role tend to have a few things in common. They: 

• Think in hypotheses, not features — every build starts with what will this teach us 
• Care more about a real answer than a clean pull request. 
• Are comfortable being data-driven: forming a hypothesis, running the numbers, and killing an idea they liked if the data says no. 
• Know when to build fast and disposable versus when to build to last. 
• Move easily between product sense, statistics, and code. 
• Take ownership of a metric, not just a task. 
• Are naturally curious about the business, not just the codebase. 

Growth Engineering, the Flywl Way 

We think about growth engineering across three areas: 

• Business-facing work — running experiments that directly move signup, activation, conversion, or expansion revenue. 
• Empowerment work — building tools so non-engineers (founders, marketing, sales) can test and iterate without needing you in the loop for every change. 
• Platform work — the experimentation infrastructure, analytics, and reusable components that make every future test faster to ship. 

We build to learn before we build to last. If a scrappy, semi-manual version of an experiment can answer the question in days instead of weeks, we ship that first — and only invest in the durable version once the data says it's worth it. At our target pace, that discipline isn't optional — it's what makes [X] experiments a week possible. 

AI Is Part of the Job 

We expect AI fluency the same way we expect SQL fluency. But at Flywl, AI isn't just a tool you use to work faster — it's a surface you build and are accountable for. 

That means: 

• Building AI-powered growth surfaces, not just prototyping them: smarter onboarding, personalised flows, AI-assisted lifecycle messaging, shipped and owned like any other experiment. 
• Instrumenting what you build — evals, guardrails, and monitoring for the AI surfaces you ship, so you know when they're working and when they're not, not just that they shipped. 
• Measuring impact with the same rigour as everything else — an AI feature is a hypothesis like any other; it lives or dies on whether it actually moved the number. 
• Using coding agents and AI-assisted development to ship experiments daily. 
• Using AI to accelerate data analysis — pulling insights out of experiment results faster than a manual query would. 
• Knowing where AI genuinely speeds up an experiment cycle — or improves the product — and where it's just noise. 

AI isn't a nice-to-have at Flywl. It's something you build, instrument, and are measured on. 

What We're Looking For 

We're intentionally not optimising for years of experience or a specific tech stack. We're looking for engineers who've shown they can move a number, not just ship a feature. 

You may be a great fit if you've: 

• Run A/B tests or growth experiments and can talk through what you learned, not just what you shipped. 
• Built or improved a signup, activation, onboarding, or pricing flow. 
• Worked comfortably across the stack — backend, frontend, and data/analytics — instead of specializing narrowly. 
• Made calls based on statistical significance, not gut feel alone. 
• Built internal tools that removed yourself as a bottleneck for another team. 
• Built or shipped an AI-powered feature and measured whether it actually worked — not just prototyped one. 
• Worked in a high-ownership startup environment where you defined the problem, not just the solution. 

Whether your background is in Go, Python, TypeScript, SQL, or something else matters far less than your ability to form a hypothesis, ship it, and read the result honestly. 

You'll Probably Enjoy This Role If... 

• You get more excited about a surprising experiment result than a clever piece of code. 
• You'd rather ship something scrappy today and learn than something perfect next month. 
• You like digging into why a number moved, not just that it moved. 
• You want to build AI into the product, not just use it to move faster. 
• You enjoy switching between building, analysing, and talking to the business side. 
• You want ownership of a metric, not a corner of a codebase. 

Why Flywl 

Joining Flywl means helping define how the company grows, not just contributing to an existing playbook. 

Here, you'll: 

• Work directly with the founders on what to test and why it matters. 
• Own real growth metrics from your first month. 
• Build the experimentation and analytics platform as Flywl scales. 
• Help shape how growth engineering works here — before there's a rulebook. 
• See the direct revenue impact of your work, fast. 

Don't Self-Reject 

We're not looking for someone who checks every box. If this role excites you, but your experience doesn't perfectly match everything above, we'd still encourage you to apply. 

Great growth engineers rarely come from a single mould. We're far more interested in your curiosity, your instinct for what to test next, and your comfort with ambiguity than whether your resume matches every bullet.