I’m often asked how I translate lessons from hypergrowth engineering organizations into practical playbooks for product and platform teams. In this piece, I unpack the patterns I’ve seen repeatedly work—anchored by what I admire about Will Larson’s approaches at Carta, Calm, Stripe, and Uber—and how I apply them to build resilient, high-velocity orgs.
Will Larson is a case study in modern engineering leadership. As CTO at Carta—an ownership and equity management platform—he helped guide the company after it raised at a $7.4b valuation in 2021. Before that, he was CTO at Calm, founded Stripe’s Foundation Engineering org, and led Uber’s Platform Engineering people and strategy. He’s also the author of Staff Engineer and An Elegant Puzzle, both essential reads for leaders leveling up from line management to org design.
When I craft an engineering strategy, I start by writing down a small set of clear principles. This isn’t performative; it’s an alignment mechanism. Principles reduce decision thrash, make trade-offs explicit, and help teams navigate ambiguity without constant escalation. I’ve found the discipline of writing them down upfront pays off 10x in execution quality later.
For the strategy document itself, I structure it so anyone can understand the why, what, and how in one sitting. A useful pattern: a sharp problem definition, a few guiding policies, and a concise set of coherent actions. That scaffolding keeps the strategy legible and actionable across functions—especially as it ladders into product roadmaps, platform investments, and talent plans.
Every engineering strategy has two parts. First, compounding capabilities: the platform, tooling, and architecture that unlock future velocity. Second, targeted bets: focused initiatives that advance near-term outcomes. Neglect either and you either stall out later (too many quick wins, no compounding) or fail to ship value now (all compounding, no customer impact).
Turning strategy into action requires ruthless translation. I map each guiding policy to a small number of initiatives with owners, milestones, and outcome metrics—not output. This is where outcomes vs output OKRs matter: measure the user or business result, not just the deliverable. It’s also where you surface dependencies early and avoid the Hidden Variable Problem that quietly derails timelines.
I’m particularly intrigued by Carta’s unique “navigator” model, which blends technical leadership with cross-functional guidance to accelerate execution while preserving autonomy. In my experience, similar patterns work when leaders are explicitly accountable for both system health and product outcomes—reducing the gap between platform decisions and customer value.
Engineering velocity is explainable, measurable, and optimizable. I anchor on DORA and the research from Accelerate (book), and I complement it with the SPACE (framework) to account for satisfaction and collaboration, not just delivery. The story I tell executives is simple: pick a few canonical measures, instrument them consistently, and then drive the feedback loops—branching strategy, CI/CD hygiene, change size, and operational excellence.
Choosing the right metrics for an engineering org matters as much as the metrics themselves. I use a balanced set: delivery (lead time for changes, deployment frequency), quality (change failure rate, availability), and flow (work in progress, batch size). Then I pair these with narrative context so the numbers inform decisions rather than become a game to win.
On policy, nuance beats orthodoxy. Great leaders define clear, default rules while acknowledging real-world exceptions. I’ve learned to document the policy, define who can grant exceptions, and track exception volume to spot design flaws. The goal isn’t rigidity—it’s predictable operations with a safe on-ramp for edge cases.
Micromanagement is a symptom, not a root cause. Telling someone “don’t micromanage” is often counterproductive. Instead, I focus on what’s missing—trust, clarity, or visibility. If leaders can see the plan, the risks, the checkpoints, and the demo cadence, they don’t need to hover. If they still do, fix incentives and accountability, not just behavior.
I avoid management anti-patterns by watching for early signals: policies without principles, roadmaps without strategy, meetings without decisions, or dashboards without actions. The best engineering executives pair systems thinking with crisp communication. They’re close enough to the details to ask sharp questions, yet disciplined enough to scale through managers and staff engineers.
Executive communication is an asymmetric game. I tailor the message to the decision horizon: one slide for the ask, one for the trade-offs, one for the plan and risks. The Minto Pyramid (framework) helps—lead with the answer, then support it. In meetings, the fastest way to derail progress is to lack a clear owner, a time box, or pre-reads. Fix those and you reclaim hours every week.
For presentation feedback, I’ve found a cadence that works: clarify the objective, highlight the single biggest risk, and eliminate anything that doesn’t move the decision forward. A bad sign with direct reports is when updates are status-only and insight-light; I coach toward “what changed, why it changed, and what you need.”
For early-career engineers, the most durable advantage is compounding learning: pick hard problems, write more than you think you should, and seek out leaders who invest in your growth. For team development, I borrow a simple model: staff your keystones, instrument your systems, and build a culture where the best ideas win, not the loudest voices.
If you want to explore the foundations behind these practices, start here.
Accelerate (book): https://www.amazon.com/Accelerate-Software-Performing-Technology-Organizations/dp/1942788339
Good Strategy, Bad Strategy (book): https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239
DORA: https://dora.dev/
SPACE (framework): https://queue.acm.org/detail.cfm
Minto Pyramid (framework): https://untools.co/minto-pyramid
Carta: https://www.carta.com/
Calm: https://www.calm.com/
Stripe: https://www.stripe.com/
JavaScript: https://www.javascript.com/
KAFKA: https://kafka.apache.org/
Ruby on Rails: https://rubyonrails.org/
To go deeper on Will’s writing and perspective, these are great starting points.
Twitter/X: https://twitter.com/lethain
LinkedIn: https://www.linkedin.com/in/will-larson-a44b543/
Personal website/blog: https://lethain.com/
An Elegant Puzzle (book): https://www.amazon.com/Elegant-Puzzle-Systems-Engineering-Management/dp/1732265186
Staff Engineer (book): https://staffeng.com/book
What are the two parts of an engineering strategy described in the article?
The two parts are compounding capabilities and targeted bets. Compounding capabilities include the platform, tooling, and architecture that unlock future velocity. Targeted bets are focused initiatives that advance near-term outcomes.
How should strategy turn into action?
Strategy translates into action by mapping guiding policies to a small number of initiatives with owners, milestones, and outcome metrics—not outputs. It also requires surfacing dependencies early to avoid the Hidden Variable Problem that derails timelines.
What metrics are used to measure engineering velocity?
The article recommends a balanced metric set: delivery (lead time for changes, deployment frequency), quality (change failure rate, availability), and flow (work in progress, batch size). It also emphasizes instrumenting these measures consistently and adding narrative context so numbers inform decisions.
What is the 'navigator' model mentioned?
The navigator model blends technical leadership with cross-functional guidance to accelerate execution while preserving autonomy. It helps reduce the gap between platform decisions and customer value.
What is the recommended approach to executive communication?
Executive communication should be concise: one slide for the ask, one for the trade-offs, and one for the plan and risks. The Minto Pyramid framework helps lead with the answer and support it.
How should policy exceptions be handled?
Document policy, define who can grant exceptions, and track exception volume to spot design flaws. The goal is predictable operations with a safe on-ramp for edge cases.
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