When I joined Google ~14 years ago, I thought the job was about writing great code. I was partly right. But the longer I’ve stayed, the more I’ve realized that the engineers who thrive aren’t necessarily the best programmers - they’re the ones who’ve figured out how to navigate everything around the code: the people, the politics, the alignment, the ambiguity.
These lessons are what I wish I’d known earlier. Some would have saved me months of frustration. Others took years to fully understand. None of them are about specific technologies - those change too fast to matter. They’re about the patterns that keep showing up, project after project, team after team.
I’m sharing them because I’ve benefited enormously from engineers who did the same for me. Consider this my attempt to pay it forward.
It’s seductive to fall in love with a technology and go looking for places to apply it. I’ve done it. Everyone has. But the engineers who create the most value work backwards: they become obsessed with understanding user problems deeply, and let solutions emerge from that understanding.
User obsession means spending time in support tickets, talking to users, watching users struggle, asking “why” until you hit bedrock. The engineer who truly understands the problem often finds that the elegant solution is simpler than anyone expected.
The engineer who starts with a solution tends to build complexity in search of a justification.
You can win every technical argument and lose the project. I’ve watched brilliant engineers accrue silent resentment by always being the smartest person in the room. The cost shows up later as “mysterious execution issues” and “strange resistance.”
The skill isn’t being right. It’s entering discussions to align on the problem, creating space for others, and remaining skeptical of your own certainty.
Strong opinions, weakly held - not because you lack conviction, but because decisions made under uncertainty shouldn’t be welded to identity.
The quest for perfection is paralyzing. I’ve watched engineers spend weeks debating the ideal architecture for something they’ve never built. The perfect solution rarely emerges from thought alone - it emerges from contact with reality. AI can in many ways help here.
First do it, then do it right, then do it better. Get the ugly prototype in front of users. Write the messy first draft of the design doc. Ship the MVP that embarrasses you slightly. You’ll learn more from one week of real feedback than a month of theoretical debate.
Momentum creates clarity. Analysis paralysis creates nothing.
The instinct to write clever code is almost universal among engineers. It feels like proof of competence.
But software engineering is what happens when you add time and other programmers. In that environment, clarity isn’t a style preference - it’s operational risk reduction.
Your code is a strategy memo to strangers who will maintain it at 2am during an outage. Optimize for their comprehension, not your elegance. The senior engineers I respect most have learned to trade cleverness for clarity, every time.
Treat your technology choices like an organization with a small “innovation token” budget. Spend one each time you adopt something materially non-standard. You can’t afford many.
The punchline isn’t “never innovate.” It’s “innovate only where you’re uniquely paid to innovate.” Everything else should default to boring, because boring has known failure modes.
The “best tool for the job” is often the “least-worst tool across many jobs”-because operating a zoo becomes the real tax.
Early in my career, I believed great work would speak for itself. I was wrong. Code sits silently in a repository. Your manager mentions you in a meeting, or they don’t. A peer recommends you for a project, or someone else.
In large organizations, decisions get made in meetings you’re not invited to, using summaries you didn’t write, by people who have five minutes and twelve priorities. If no one can articulate your impact when you’re not in the room, your impact is effectively optional.
This isn’t strictly about self-promotion. It’s about making the value chain legible to everyone- including yourself.
We celebrate creation in engineering culture. Nobody gets promoted for deleting code, even though deletion often improves a system more than addition. Every line of code you don’t write is a line you never have to debug, maintain, or explain.
Before you build, exhaust the question: “What would happen if we just… didn’t?” Sometimes the answer is “nothing bad,” and that’s your solution.
The problem isn’t that engineers can’t write code or use AI to do so. It’s that we’re so good at writing it that we forget to ask whether we should.
With enough users, every observable behavior becomes a dependency - regardless of what you promised. Someone is scraping your API, automating your quirks, caching your bugs.
This creates a career-level insight: you can’t treat compatibility work as “maintenance” and new features as “real work.” Compatibility is product.
Design your deprecations as migrations with time, tooling, and empathy. Most “API design” is actually “API retirement.”
When a project drags, the instinct is to blame execution: people aren’t working hard enough, the technology is wrong, there aren’t enough engineers. Usually none of that is the real problem.
In large companies, teams are your unit of concurrency, but coordination costs grow geometrically as teams multiply. Most slowness is actually alignment failure - people building the wrong things, or the right things in incompatible ways.
Senior engineers spend more time clarifying direction, interfaces, and priorities than “writing code faster” because that’s where the actual bottleneck lives.
In a large company, countless variables are outside your control - organizational changes, management decisions, market shifts, product pivots. Dwelling on these creates anxiety without agency.
The engineers who stay sane and effective zero in on their sphere of influence. You can’t control whether a reorg happens. You can control the quality of your work, how you respond, and what you learn. When faced with uncertainty, break problems into pieces and identify the specific actions available to you.
This isn’t passive acceptance but it is strategic focus. Energy spent on what you can’t change is energy stolen from what you can.
Every abstraction is a bet that you won’t need to understand what’s underneath. Sometimes you win that bet. But something always leaks, and when it does, you need to know what you’re standing on.
Senior engineers keep learning “lower level” things even as stacks get higher. Not out of nostalgia, but out of respect for the moment when the abstraction fails and you’re alone with the system at 3am. Use your stack.
But keep a working model of its underlying failure modes.
Writing forces clarity. When I explain a concept to others - in a doc, a talk, a code review comment, even just chatting with AI - I discover the gaps in my own understanding. The act of making something legible to someone else makes it more legible to me.
This doesn’t mean that you’re going to learn how to be a surgeon by teaching it, but the premise still holds largely true in the software engineering domain.
This isn’t just about being generous with knowledge. It’s a selfish learning hack. If you think you understand something, try to explain it simply. The places where you stumble are the places where your understanding is shallow.
Teaching is debugging your own mental models.
Glue work - documentation, onboarding, cross-team coordination, process improvement - is vital. But if you do it unconsciously, it can stall your technical trajectory and burn you out. The trap is doing it as “helpfulness” rather than treating it as deliberate, bounded, visible impact.
Timebox it. Rotate it. Turn it into artifacts: docs, templates, automation. And make it legible as impact, not as personality trait.
Priceless and invisible is a dangerous combination for your career.
I’ve learned to be suspicious of my own certainty. When I “win” too easily, something is usually wrong. People stop fighting you not because you’ve convinced them, but because they’ve given up trying - and they’ll express that disagreement in execution, not meetings.
Real alignment takes longer. You have to actually understand other perspectives, incorporate feedback, and sometimes change your mind publicly.
The short-term feeling of being right is worth much less than the long-term reality of building things with willing collaborators.
Every metric you expose to management will eventually be gamed. Not through malice, but because humans optimize for what’s measured.
If you track lines of code, you’ll get more lines. If you track velocity, you’ll get inflated estimates.
The senior move: respond to every metric request with a pair. One for speed. One for quality or risk. Then insist on interpreting trends, not worshiping thresholds. The goal is insight, not surveillance.
Senior engineers who say “I don’t know” aren’t showing weakness - they’re creating permission. When a leader admits uncertainty, it signals that the room is safe for others to do the same. The alternative is a culture where everyone pretends to understand and problems stay hidden until they explode.
I’ve seen teams where the most senior person never admitted confusion, and I’ve seen the damage. Questions don’t get asked. Assumptions don’t get challenged. Junior engineers stay silent because they assume everyone else gets it.
Model curiosity, and you get a team that actually learns.
Early in my career, I focused on the work and neglected networking. In hindsight, this was a mistake. Colleagues who invested in relationships - inside and outside the company - reaped benefits for decades.
They heard about opportunities first, could build bridges faster, got recommended for roles, and co-founded ventures with people they’d built trust with over years.
Your job isn’t forever, but your network is. Approach it with curiosity and generosity, not transactional hustle.
When the time comes to move on, it’s often relationships that open the door.
When systems get slow, the instinct is to add: caching layers, parallel processing, smarter algorithms. Sometimes that’s right. But I’ve seen more performance wins from asking “what are we computing that we don’t need?”
Deleting unnecessary work is almost always more impactful than doing necessary work faster. The fastest code is code that never runs.
Before you optimize, question whether the work should exist at all.
The best process makes coordination easier and failures cheaper. The worst process is bureaucratic theater - it exists not to help but to assign blame when things go wrong.
If you can’t explain how a process reduces risk or increases clarity, it’s probably just overhead.
And if people are spending more time documenting their work than doing it, something has gone deeply wrong.
Early in your career, you trade time for money - and that’s fine. But at some point, the calculus inverts. You start to realize that time is the non-renewable resource.
I’ve watched senior engineers burn out chasing the next promo level, optimizing for a few more percentage points of compensation. Some of them got it. Most of them wondered, afterward, if it was worth what they gave up.
The answer isn’t “don’t work hard.” It’s “know what you’re trading, and make the trade deliberately.”
Expertise comes from deliberate practice - pushing slightly beyond your current skill, reflecting, repeating. For years. There’s no condensed version.
But here’s the hopeful part: learning compounds when it creates new options, not just new trivia. Write - not for engagement, but for clarity. Build reusable primitives. Collect scar tissue into playbooks.
The engineer who treats their career as compound interest, not lottery tickets, tends to end up much further ahead.
Twenty-one lessons sounds like a lot, but they really come down to a few core ideas: stay curious, stay humble, and remember that the work is always about people - the users you’re building for and the teammates you’re building with.
A career in engineering is long enough to make plenty of mistakes and still come out ahead. The engineers I admire most aren’t the ones who got everything right - they’re the ones who learned from what went wrong, shared what they discovered, and kept showing up.
If you’re early in your journey, know that it gets richer with time. If you’re deep into it, I hope some of these resonate.
