Asha Elza Jacob

How I Made Decisions at Amazon In 5.5 years at Amazon, I made thousands of decisions. Some were small. Scope tweaks. Deadline shifts. Meeting structure changes. Some were big. Launch calls. Escalations. Resource tradeoffs. Risk acceptance. And here’s what I learned: Indecision is usually more expensive than a wrong decision. Amazon forced me to build a decision framework…not rely on instinct alone. Here’s how I approached it: 1/ I asked: Is this a one-way door or a two-way door? ↳ A one-way door decision is hard to reverse ↳ Long-term architectural changes ↳ Big resourcing bets ↳ Major customer commitments These required deep dive, multiple perspectives, and slower motion. ↳ A two-way door decision is reversible ↳ Pilot launches ↳ Process changes ↳ Messaging experiments These required speed. Most people treat two-way doors like one-way doors. That’s how velocity dies. 2/ I defined the real risk before acting ↳ What’s the worst-case outcome? ↳ How recoverable is it? ↳ What’s the cost of waiting? Often the cost of delay was greater than the cost of being imperfect 3/ I gathered just enough signal…not perfect clarity ↳ 70% confidence was often enough ↳ The remaining 30% came from moving ↳ Over-analysis was a hidden tax on momentum 4/ I wrote my thinking down ↳ Framing the decision in writing exposed weak assumptions ↳ It also made it easier for others to pressure test it ↳ Writing sharpened judgment 5/ I owned the outcome…even when it missed ↳ If a decision didn’t land, I didn’t blame the inputs ↳ I asked: What did I miss? What pattern did I ignore? ↳ Decisions compound…so does accountability The biggest shift Amazon gave me was this: Decisiveness is a leadership trait. But structured decisiveness is a competitive advantage. 📬 I write weekly about clarity, ownership, and high-stakes execution in The Weekly Sync: 👉 https://lnkd.in/e6qAwEFc What’s one decision you’ve been overthinking that might actually be a two-way door?

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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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At 3 AM on a Wednesday, Amazon needed me. I wasn’t even on that team anymore. Back when I was an SDE at Amazon, a Sev-2 had hit one of our services. Many customers were impacted. The team had been chasing it for hours. Still, they called me. I rolled out of bed, opened my laptop, and joined the bridge. The tricky part was that the fault started a week earlier. A code change had shipped behind a flag, traffic ramped slowly, and the failure signature was noisy. Logs were chatty in all the wrong places and silent where we needed them most.Dashboards showed errors, but no clear line to the root. We did unglamorous things. We wrote a minute-by-minute timeline. We diffed deployments, configs, and flags across regions. We checked queue backlogs and retry storms. We sampled logs with new filters. We bisected the traffic ramp. We rolled back the smallest safe thing first. Then we added one log, in one hot path, and the picture snapped into focus. By sunrise, we had the root cause and a fix. The impact was contained. Customers recovered. What stayed with me was not the fix. It was why they called. They did not call because I am smarter. They called because, over months, I had done the boring work. I wrote runbooks when no one asked. I paired with juniors on their first on-call. I shared context so others could make decisions without me. I showed up when it was not my ticket. Trust is not built in one night. It is built in all the small days that come before it. Ownership is simple. If it affects your customer, it is your problem. Org charts are for payroll, not for 3 AM. If you are early in your career, remember this: → Document the weird corners when you discover them. → Add one useful log where it hurts, not twenty where it is easy. → Keep a living checklist for rollback, not a static wiki page. → Teach one person the thing you just learned. → Read your dashboards when things are calm, not only when they are red. At 3 AM, you will not rise to the occasion. You will fall to the level of your habits. Build the right ones while the house is quiet. That’s how you earn trust that lasts beyond teams and ownership that outlives org charts. A few days later, this moment turned into an accolade from the team..  a reminder that trust is built long before it’s tested :)

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You are in a system design interview at Amazon for an SDE-3 role and the interviewer has given you the question to Design Netflix. He then asks a follow up to you: How does Netflix knows when to show: Are you still watching? Is it just time-based? If it were time-based, you would see it every time. So what is actually going on under the hood? Both look like a simple pop-up. Underneath, it is a mix of product thinking, client logic, and backend events. 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 [1] Start from the product goals The feature is not only about nagging people. - Save bandwidth and CDN cost if the viewer has slept or walked away - Avoid autoplaying potentially sensitive content in an empty room - Protect kids if parents start a show and leave the TV on - Do all this without annoying active binge watchers So the design must answer one question: "When is the user probably not here any more" [2] Naive design - pure timer Simplest idea: - If playback has been running for 2 hours, show Are you still watching - If user clicks "Yes", reset the timer Why this is weak: - Someone can binge 5 episodes in a row and gets interrupted in the middle of an intense scene - Someone can start a show, walk away after 5 minutes, and the platform will keep streaming for the next 2 hours - It ignores how many episodes were auto played, device type, time of day, user habits  [3] Realistic design - session and engagement signals Think in terms of a "watch session" and "engagement events". Signals the client can track: - Play, pause, seek, volume change - Episode finished and next episode auto started - Remote or keyboard input, UI navigation - Screen on or off events from the device - For mobiles: app background or locked state A common heuristic could be: - Count how many episodes have auto played without any user interaction - Track how long it has been since the last button press or navigation - Only trigger the prompt when both are high enough Example rule: - If 3 episodes in a row have auto played - And there has been no interaction for 45 minutes - Then, before starting episode 4, show Are you still watching This feels much smarter: - Active viewers who pause, skip intro, change volume keep resetting the "engagement clock" - Sleeping viewers do not touch anything, so the next episode is blocked by the prompt  [4] Client heavy vs server heavy design You can talk about two design choices. Client first: - All logic runs inside the app on TV, mobile, or web - Client keeps an in memory session model and decides when to show the popup - It still sends telemetry events to backend for analytics and future tuning Pros: - Works even with flaky network - Highly responsive, no extra server round trip Cons: - Logic must be implemented and updated across many platforms - Harder to quickly roll out rule changes Continued ↓

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If you are an SF engineer at $150K, you are likely Level I. Your H‑1B chances just dropped by 48%. The new H‑1B Cap Weighted Selection Final Rule is here. Random lottery is gone. Wage‑weighted selection is in. Data. Under the old system, everyone sat at about 35% odds. 118K out of 336K registrations in FY26. No advantage for higher wage levels. The new rule rewrites those odds: → Level I: 15.29% → Level II: 30.58% → Level III: 45.87% → Level IV: 61.16% For Level I, odds drop nearly in half. For Level IV, odds more than double. A $150K SF engineer is often Level I on OES. → Early career people, Lower salary but important roles, they get hit the hardest here. There is more. Registrations must now include: → OEWS wage level → SOC code → Work location Later the petition must prove that wage level was correct on the day of registration. USCIS can deny, revoke, or block amendments if it looks like you changed terms to game the lottery. The logic from the government is clear. → Higher pay, higher odds. → Your economic value now shapes your chance to stay. If you are an employer, start planning before the next window. If you are a worker, learn your wage level and study other paths, not only H‑1B. I will keep sharing data as more details come out. Hi! My name is Sumit Gupta, I share data, AI, immigration updates and make you a smarter immigrant!

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Just heard Amazon Prime laid off so many employees, Because they shipped faster and automated things with AI. And this just scares me but at the same time I feel there’s huge gap between how managerial side is thinking about AI v/s the Reality of AI: Because: 1. AI writes the code but fails at quality, I need to spend time to get good final version 2. Doing LLDs with AI is too hectic, it skips what it doesn’t understand or comes up with solution which isn’t optimised 3. HLDs aren’t even AIs cup of tea yet This just about coding part, there’s lot of more that we do as engineers. So, is it managers illusion which is taking away job ? But whatever it is someone needs to wake these companies to reality and show how important engineers are for the company. Done with listening AI can replace engineers.

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Positive thoughts to my ex-colleagues at Amazon. A tough period indeed. The first few days after a layoff feel surreal. The silence after the news can be deafening, and the urge to immediately fix everything is strong. But here’s the truth most people don’t tell you , it’s okay to pause. Rest. Let your brain and body catch up to what just happened. Clarity only comes after you exhale. Once you’ve had time to breathe, start treating the next chapter like a strategic reboot. Use this moment to re-evaluate what kind of work excites you and what kind you never want to return to. The layoff might have closed a door, but it also cleared space for something better aligned with who you are now. When you’re ready to re-engage, AI tools can take a lot of the heavy lifting out of the job search. ChatGPT can help you tailor your résumé for each role and practice interview questions. Tools like Rezi and Teal can optimize your resume for applicant tracking systems. Even LinkedIn’s own AI features can help you write stronger “About” sections or suggest better keywords to match your profile with recruiters’ searches. Use this time not just to find another job, but to redesign your career story. A layoff isn’t the end of your professional identity. It’s a forced pause that can turn into your most creative rewrite yet. #Layoffs #CareerGrowth #AItools #JobSearch #Resilience #LinkedInCommunity

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H1B Landscape is Changing The H1B world is shifting, and I see a few trends that could shape demand: 🔹 Premium H-1B Talent – only highly specialized roles will justify sponsorship costs. 🔹 Domestic Talent – citizens, green card holders, and OPT/CPT candidates may get first priority. 🔹 Hybrid Teams – blending domestic and offshore talent will be the new norm. 🔹 Offshore/Nearshore Expansion – some projects will move to cost-effective geographies. From my view, contracting isn’t disappearing. It’s evolving. Fewer H1B-heavy roles, more domestic opportunities, and selective premium placements for specialists. 💡 How is your team preparing for these changes? #H1B #TalentStrategy #Staffing #HybridWork #ITStaffing #FutureOfWork #Cloudbridge

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Big Tech just told H-1B holders — Come back before Sunday or risk $100K per head! 🚨 Trump just announced a $100K annual fee for every H-1B visa. Microsoft, Amazon, JP Morgan have sent internal letters asking all H-1B/H-4 employees outside the U.S. to return before Sept 21, 12:01 AM ET. 💭 What does this mean for Indian talent? -->Cost of hiring H-1B workers will skyrocket. -->Many companies may freeze or slow down H-1B hiring. -->This could lead to a surge in remote work opportunities and offshore roles — hiring Indian developers to work from India instead of relocating them to the U.S. -->Big Tech may challenge this move legally, but in the short term, there’s a lot of uncertainty for those outside the U.S. 🌏 My take: This could be a turning point. Indian IT talent may see more remote, high-paying roles as companies look to avoid massive visa costs — but it might also limit chances for on-site opportunities in the U.S. Day-22 #365days #H1B #RemoteWork #TechIndustry #Immigration

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