Mansi Tripathi

After 8 rewarding years, I bid goodbye to Amazon. I strongly believe that successful companies are built with strong culture at their core, and Amazon’s Leadership Principles are some of the most recognizable ones. I'm grateful that Amazon is where I bootstrapped my career. As I reflect on my journey, I also want to share some personal takeaways that shaped my growth. And of course, in the spirit of “if everything is important, then nothing is”, here goes with my top three. 1. 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐰𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 - You should work on: 1) Thing that matters to your team/org/company. If you are not clear, clarify with your leaders and help define one if needed. What matters? If you work backward from the needs of your customers (those who use your product/service), you will get to it. 2) Thing that matters to you. Ask yourself what motivates you and what can help you learn. Find opportunities that push you closer to what you want to achieve in the long run. If you’re lucky, you get to work on 1 & 2 at the same time. Be conscious of what you work on. 2. 𝐋𝐞𝐚𝐫𝐧 𝐛𝐲 𝐨𝐛𝐬𝐞𝐫𝐯𝐢𝐧𝐠 - Over the years, I’ve been fortunate enough to work with a good set of top talents. I learned the most and the fastest by simply observing how they made decisions at high-stakes moments, how they brought order into chaos, how they knew when to move fast and when to slow down, and how they behaved under high pressure, etc. Try to reason about and internalize those behaviors, and adapt to similar situations you face. Like the phrase “Follow the smart money” in the investment world, go identify and learn from the greats. 3. 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐠𝐨𝐞𝐬 𝐚 𝐥𝐨𝐧𝐠 𝐰𝐚𝐲 - Career is made up of ups and downs. Confidence is about not shying away from advocating for your work and celebrating your wins. Confidence is also about resilience - learning from setbacks, carrying the lessons forward, and moving on. Confidence is about showing up with energy, curiosity, and enthusiasm in a way that inspires those around you to produce positive results. Be confident, and just have fun! P.S. None of the above, in my opinion, will be replaced by AI anytime soon. :) #growth #day1

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When I first moved from IC to manager at Amazon, the hardest thing was letting go of doing everything myself, especially when I took over manager role in the same team where I was operating as individual contributor. As ICs, we get rewarded for solving problems directly. As managers, success depends on enabling others to solve them. That shift feels unnatural at first. As manager you are doing everything backstage to setup your team for success. Your success depends on your ability to force mulitply yourself consistently and repeatedly. Navigating It: • One path is micro-managing: you still feel “in control,” but your team loses trust and creativity. • Another is disappearing: you stop diving in, but your team feels abandoned. • The sweet spot is being a coach—close enough to guide, distant enough to let them own outcomes. Pros/Cons of Approaches: • Micro-manage → ✅ quick results, ❌ long-term team frustration • Hands-off completely → ✅ team feels trusted, ❌ risk of chaos • Coach → ✅ sustainable growth, ❌ requires patience and conscious effort Actionable Tips: • In your next 1:1, ask: “What decisions do you feel comfortable owning without me?” • When tempted to jump in, pause and ask: “Is this a growth moment for them, or a rescue mission for me?” 👉 What was your biggest struggle in “letting go”? Share in comments—and tag a friend stepping into management!

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Early in my Amazon career, I made a common management mistake—I spent too much time on small decisions and very little time on big ones. Small decisions are much easier to make than big decisions. They give leaders the illusion of productivity because it feels better to make more decisions per unit of time. But high levels of activity do not necessarily produce better results. Big, challenging bets are what fundamentally changed Amazon, not little actions. In my first few years, my team and I maintained a spreadsheet with a long list of retail merchandising features we wanted to add to our website, like buy-one-get-one-free, discounts for bundles, etc. At the time, these seemed critical to unlocking growth and matching the capabilities of brick-and-mortar retailers who were outselling us. Those things brought in some extra revenue, which was great, but none of them fundamentally changed the business. Basically, Amazon didn’t become Amazon because of a BOGO feature. What blew Amazon through the roof were the big bets: building Prime, creating Kindle, launching AWS. These were decisions with real stakes. They were hard, risky, and they took years to pay off. But, they made all the difference between what Amazon was then and what Amazon is now. When I coach leaders now, I ask, “If this project goes well, what’s the upside? A million dollars? Ten million? A hundred million?” The estimates will always be wrong, but the order of magnitude tells you where to focus. If everything on your roadmap caps out at a 1% impact, then you’ll get 1% growth at best. If you want 10x, you need to make 10x bets. The challenge is that big decisions are hard. They require risk, investment of scarce resources, long-term thinking, and sustained effort. They don’t come with short-term gratification. It feels like nothing’s happening for months or even years. The job of an executive is to make a few big, high-quality decisions each year and drive their execution. This is what will define the success of the business. Spending most of your time on small things feels productive in the moment, but it is actually the best way to make sure the business stagnates in the long run.

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LeetCode Knights who can't ship or collaborate as engineers are like Formula 1 drivers who've never driven in traffic. I say this as someone who has spent years on both sides. I have hired and interviewed at Amazon. I now work as a Senior Engineer at Nielsen. I have seen brilliant problem solvers who could not ship a stable feature. I have also seen engineers who were average on LeetCode but carried entire teams on their backs. LeetCode is useful. It sharpens your thinking, helps you speak the “interview language,” and gives you a structured way to practice. I used it myself when I was preparing for Amazon. I still recommend it to people who want to break into good companies. But here is the issue. Some engineers start treating LeetCode as the main measure of their worth. If they are hitting 200 problems a year, they feel “good.” If they are slow in a contest, they feel “bad.” Meanwhile, nobody is asking simple questions like: > Can you read a messy codebase and make sense of it? > Can you debug a production issue without panicking? > Can you explain trade-offs to a PM in plain language? > Can you design something that another engineer can safely change a year later? That is competence. Software engineering is less about solving a neat, isolated problem and more about dealing with half-baked requirements, legacy code, time pressure, and unclear constraints. It is about making progress even when the situation does not look like a clean LeetCode prompt. If you are serious about your career, you need to optimize for both. When you prepare for interviews: > Pick a fixed slot for DSA. For example, one or two hours a day. Do it with focus, then stop. > Map problems to concepts. “This is a two pointer idea.” “This is a graph traversal pattern.” That way you are building a toolkit, not just counting problems. When you grow as an engineer: > Own a feature end-to-end. Design it, build it, handle the bugs, and listen to user feedback. That pain teaches you more than ten contests. > Read code from seniors. See how they name things, structure modules, handle failure, and write tests. > Ask for feedback on your pull requests beyond “LGTM.” Ask, “How would you simplify this. What smells wrong here.” Good companies do not only look for who can solve the hardest LeetCode problem. They look for people who can: >Think clearly. >Communicate calmly. >Work with others without ego. >Handle ambiguity without freezing. >Take responsibility when things break. If you want a long career, build that person in yourself. By all means, grind LeetCode for a season when interviews are near. It is a tool. Use it well. Then close the tab and go back to building things that live in production. Your future manager will care far more about whether they can trust you with a problem than whether you once solved “Hard problem number 247” in 18 minutes.

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10 Years at Amazon. Trust me no free Prime membership 😂. But what I did earn: a few colored lanyards, great friends, & some hard earned lessons that helped me navigate work & life across continents, re-orgs, & time zones. 10 learnings from my #Day1 journey: — 1. Know urself before chasing the next shiny thing Not everyone needs to be a PM or jump into Tech just ’cause it sounds cool. Reflect, ask for feedback, & check if the role fits u — not just ur LinkedIn profile. I’ve seen people chase roles like buying Taylor Swift tickets w/o knowing the lyrics looks cool until it starts. 2. Understand ur manager They hold the keys to ur growth (& ur vacation approvals). So interview them to understand What keeps them up at night? How do they define success? Ur alignment with them determines whether u will thrive or just survive. 3. LPs are more than buzzwords Here, LPs are like air — u don’t see them, but they’re everywhere. Debating product strategy? LPs. Promotion bar raiser? LPs. Explaining “Frugality” to ur spouse during a weekend Costco run? Still LPs. 4. Health is not a badge of hustle Red eyes from late nights aren’t leadership signals. Working 16 hrs a day isn’t a promotion strategy. If ur calendar has space for 1:1s, it has space for a 5PM walk too. It’s ROI positive trust me. 5. Promotions are a milestone, not the mission They’re like an OP1 goal: ambitious, well documented, and somehow always “needs more alignment.” U’ve hit goals,shared crisp docs, & still land in “not this cycle.” So sharpen your skills & double down on growth. Make the title chase you — not the other way around. 6. Embrace the chaos (it’s inevitable) One week ur leading a launch, the next due to a re-org the project gets deprioritized. I’ve gone from “Great job!” to “We need to pivot this entirely” in one meeting. Adaptability isn’t just a skill it’s survival gear. So lace up, lean in, & yes be water, the kind that doesn’t spill during a re-org. 7. Time is the great leveller Whether ur a VP or a new hire in onboarding, we all get 24 hrs. Use them wisely at work & beyond. Put down the laptop sometimes. That Netflix show? Sometimes just as necessary as the doc review. 8. Start with the end The doc culture here taught me to picture a clear end goal not just for work, but for life too. When u know what ur optimizing for, it’s easier to say no to things that don’t serve outcome. 9. Confidence ≠ competence U’ll meet folks who sound like they invented AWS & can’t explain what it stands for. Meanwhile, ur fact-checking every sentence in ur doc like it’s going to the White House. Don’t get distracted steady delivery & thoughtful decisions win. 10. Don’t take urself too seriously. Take the work seriously, yes. But laugh at urself. Keep perspective. Engage with people. One time, I accidentally shared a doc titled “Final_v7_ThisTimeForReal” with my skip manager. He replied: “Bold of u to assume.” Lesson learned: always version ur docs & ur ego. — #10Years #Amazon #Day1

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Have you ever felt a mix of excitement and worry about how AI will change your job? I have! As a Senior Manager in Quality Engineering at Amazon, I have been asked this question multiple times by many professionals from various companies and different tech roles. I see firsthand how AI is reshaping not just testing, but every aspect of how we build and deliver technology. In my journey at Amazon, I have witnessed waves of technological change, each reshaping our roles as QA professionals. The arrival of AI isn’t just another wave—it’s a tsunami. I want to share a candid reflection today—not as an AI expert (I am not!) but as someone who’s spent years advocating for quality in the midst of evolving tech. The AI Shift: From Automator to Partner AI isn’t just another tool—it’s becoming a partner in how we design, build, and test. From AI-powered code analysis to dynamic test generation, we’re no longer just checking for bugs. We are driving the future of trust and innovation. Our Role: From Checkers to Quality Champions QA professionals—and frankly, anyone working with AI—will evolve from traditional roles to: • Curators of Data: Data quality fuels AI. We ensure it’s clean, relevant, and diverse. • Evaluators of Fairness and Ethics: Bias in AI has real consequences. Testing for fairness isn’t optional—it’s essential. • Guardians of Trust: AI decisions must be transparent. We ensure that AI systems earn and keep user trust. How to Lean In- Here’s how I am adapting—and how you can too: ✅ Understand AI basics, even if you’re not coding it. ✅ Involve yourself early—AI testing can’t be an afterthought. ✅ Experiment—play with new AI testing tools. ✅ Stay curious—quality isn’t just code correctness; it’s about user experience and trust. My Takeaway: Quality is More Than Code AI doesn’t change our core mission—it amplifies it. As QA professionals, as engineers, as leaders, we have a front-row seat to guide responsible AI. Let’s not fear this wave—let’s ride it! #AIinQA #ContinuousLearning #Leadership #QualityEngineering #FutureofWork #Amazon #QualityAssurance

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You are in a system design interview at Google for the L5 Senior Engineer role, and the interviewer leans in and asks: “Why does Spotify keep playing when I drive into a tunnel with no signal, but YouTube Music often stops or buffers? If you were designing a music streaming system, what different design choices would lead to these two behaviors?” Here is how you break it down. Btw, if you’re preparing for system design/coding interviews, check out our mock interview tool. You can use it for free here: https://lnkd.in/gpCn7t2T We’ve added new features as well:  -Company-specific interviews -In-built interview scheduler  -performance insights & trends — Both apps look like simple music players. Under the hood, they are optimized for very different priorities. [1] Spotify style – Cache first, stream second Idea: The client behaves like a smart offline player that happens to stream. The backend is built to support aggressive prefetching. What happens when you hit play - Client requests the track from a CDN or edge node. - Instead of tiny chunks, it downloads a big buffer ahead of the playhead. - In parallel, it starts pre downloading the next few tracks in the queue. - Data is written to an encrypted cache on disk, not just memory. - Playback reads from that local cache, not directly from the network socket. What this means for tunnels and bad networks - When the network drops, the player already has tens of seconds or entire tracks cached. - Because the next one or two songs are already downloaded, you can be offline for a while and never notice. - Cold start cost is a bit higher. First play might take slightly longer, but then everything feels smooth. - It burns more local storage and possibly more data, because not every prefetched song will be listened to fully. In a design answer, you can mention - Local disk cache with eviction policies (LRU per user, per device). - Background prefetch of N upcoming tracks based on queue. - Download manager that adapts how aggressively it prefetches based on network quality and user settings. - CDN tuned for larger object delivery and range requests. - Explicit offline mode that pins playlists into cache. [2] YouTube Music style – Stream first, cache is minimal Idea: Treat audio like video streams. Cost and bandwidth are optimized first. When you hit play - Player requests audio (and sometimes video) via HLS or DASH-style chunks. - Each chunk is only a few seconds long. - Client keeps a small rolling buffer in memory, not a large queue on disk. - Prefetch of future songs is limited, because video tracks are large and expensive to fetch speculatively. What this means for tunnels and bad networks - If your connection dies, the player only has a few seconds of buffered data. - As soon as those chunks are consumed, playback stalls. - Startup can feel snappy and data usage is controlled, especially for casual listeners. - Works very well on stable networks, feels fragile in spotty coverage.

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Hypotheses on layoffs - no organization owes you any entitlement, you need to simulate your finances knowing that layoffs will happen sooner or later - the macro printing money phase is coming to an end & with it the coasting in big tech is going to be rare, understand this new reality don't fight it, brace for more deep work x hard work x competition - if you didn't plan your finances accounting for a layoff, it's on you, not on your company, you are a one person business, remember that

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All this talk about the PRD being dead but the funny thing is getting AI to write a detailed PRD with requirements, tech stack, design guidelines, and clear milestones upfront dramatically improves its subsequent coding output.

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Why “good intentions” don’t scale, but mechanisms do Early in my management career, I believed that if everyone meant well, we’d eventually get to the right outcomes. I was wrong. At Amazon, I learned the difference between intent and impact,and why mechanisms matter so much more than messages. Example? We used to remind teams in reviews: “Let’s write better postmortems.” Guess what? Nothing changed. Then we introduced a simple mechanism: • A postmortem template with quality criteria • A weekly rotation for reviews • A lightweight scoring rubric • And a dashboard showing postmortem quality across teams In 6 weeks, the quality improved. In 3 months, it became part of the culture. No extra meetings. No more “gentle nudges.” Just a repeatable system that turned intention into habit. That’s the lesson: Intentions inspire. Mechanisms deliver. If something matters , whether it’s better hiring, better documentation, or better customer response times ,don’t just talk about it. Build the mechanism that makes it happen, even when no one’s watching. What’s one mechanism that changed how your team operates? #Leadership #AmazonStyle #MechanismsNotIntentions #EngineeringCulture #OperationalExcellence #Metashift

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