Shipping fast and staying clean: How to tame tech debt in a high-growth startup?
There’s a moment in every startup’s life when the code starts creaking.
The product is taking off, engineering headcount is doubling, and delivery pressure is peaking. But the codebase — the quiet foundation beneath it all — is groaning under the weight of shortcuts, TODOs, and one-off hacks that were never meant to last. The team is shipping fast, but you can sense the drag. New engineers get stuck. Features take longer. Bugs feel familiar.
Welcome to the invisible gravity of technical debt.
At a fast growth startup, this moment comes fast and hard. You’re hiring aggressively, onboarding new engineers weekly, and running sprints at full tilt. There’s no appetite for a six-month rewrite. But there’s also no future in letting tech debt accumulate unchecked. So the question becomes: how do you sustain velocity without mortgaging the long-term health of your codebase?
Here’s how we think about it.
Integrate tech debt into the sprint — always
Don’t treat tech debt as a separate initiative. It lives inside the sprint (or whatever working model you are adopting), side by side with feature work. That means that every team, every sprint, is expected to allocate a portion of their capacity to paying down debt — reviewing brittle code, refactoring awkward abstractions, deleting dead paths.
Some teams keep it loose, picking up debt items mid-sprint when they hit friction. Others are more structured, blocking off a fixed percentage of sprint time. My preference is to not allocate a fixed capacity and instead deal with it as it arises. Either way, the mindset is the same: tech debt is not a tax, it’s a part of the work. You don’t pause to clean the kitchen — you clean as you cook.
The upside is obvious: you reduce entropy incrementally, without stalling delivery. The risk, of course, is that tech debt becomes an afterthought — deprioritized when deadlines loom. That’s why we pair this practice with something else: visibility.
Visibility: maintain a living backlog of technical debt
It’s tempting to let tech debt live in engineers’ heads. Don’t.
You should maintain a shared, visible backlog of known debt — real items, with descriptions, tags, severity levels, and suggested owners. It’s treated like any other backlog. Product managers, tech leads, and ICs can all contribute. It’s a running inventory of the friction points we’re actively choosing to accept (or not).
And here’s the nuance: don’t just wait for teams to volunteer to fix it. EMs and tech leads should routinely pull from the backlog and assign items into sprints — often framing them as enabling work for upcoming features. This bridges the gap between debt and delivery: you’re not just fixing things for the sake of tidiness; you’re paving the road for what’s next.
Visibility turns technical debt from a vague, ambient concern into a trackable, solvable problem. It also has the pleasant side effect of reminding leadership that these issues exist — and that resolving them pays real dividends.
Invest in a Platform team to fight systemic debt
At some point, your product engineers will hit limits they can’t solve within their own domains. CI pipelines that crawl. Internal tools that nobody maintains. Shared libraries that everyone’s afraid to touch. This is the territory of systemic debt — and it demands a systemic solution.
Enter the Platform team.
Platform Engineering: What is it and why the recent hype?
It’s hard for a day to go by nowadays with reading an article or a Tweet about Platform Engineering. Is the hype warranted? Is Platform Engineering really novel, or is it a rebranding effort for a set of activities that are decades old?
Platform teams are not a catch-all infra group. It’s a focused crew tasked with reducing friction across the entire engineering org. Their roadmap is built from pain: slow test runs, flaky deploys, repeated “how do I…” questions in Slack. They abstract, automate, and clean up the places where local debt becomes global dysfunction.
Crucially, this team isn’t off to the side. They partner closely with feature teams, embed when needed, and treat developer experience as their customer. Their metric is not uptime — it’s flow.
On developer productivity: flow to grow
I recently came across the tweet below, which is certainly one I can relate to. Every startup that I know is facing a dire shortage in engineering talent. But, is hiring the only way out of this conundrum?
The investment isn’t cheap. You’re trading product velocity for foundational improvement. But in a hypergrowth environment, every hour you save across the org compounds. And there’s no better way to amortize that cost than by making the whole system faster.
Foster a culture of quality
Processes and backlogs only get you so far. Culture fills the gap.
Engineers should be expected to leave the codebase a little better than they found it. That could mean renaming a confusing variable, simplifying a conditional, or deleting a function that nobody uses. These aren’t grand refactors. They’re tiny acts of care. Call it opportunistic cleanup. Call it pride. But over time, it adds up.
Code reviews reinforce this ethos. They should encourage questions like: “Is this the simplest thing that could possibly work?” “Is there an abstraction here we’re prematurely introducing?” “What would this look like if we deleted it instead?”
And as new engineers join, this culture needs constant reinforcement. It’s modeled in onboarding, in pair sessions, in Slack threads. And it’s protected by the same people who carry the roadmap: EMs and tech leads who know that velocity today means very little if the engine seizes tomorrow.
Putting it all together
Here’s the punchline.
You don’t solve technical debt by stopping. You solve it by embedding its maintenance into the rhythm of the org. Debt lives in the sprint. It’s tracked like any other work. It’s tackled opportunistically and systematically. And it’s everyone’s job — not a ticket, not a team, not a quarterly offsite.
The beauty of this approach is that it scales. As your team grows, new engineers adopt the habits they see modeled. Leaders prioritize work that enables the next layer of speed. And your codebase — imperfect but healthy — remains a place where people can move fast without fear.
In a hypergrowth startup, you’ll never have zero technical debt. But you can keep it low, visible, and shrinking. And that, more than any perfect abstraction, is what keeps the machine running.
To dive deeper into the principles and practices behind managing technical debt—especially in fast-paced, high-growth environments—here’s a curated list of standout articles and engineering blogs that have shaped our thinking; they offer firsthand insights, frameworks, and field-tested strategies from teams who’ve lived through the same scaling challenges.
