Sandesh Manjarekar

Grateful to be part of AWS She Builds Tech as an AWS Insights Speaker. ☁️ Had the opportunity to contribute an article on designing an end-to-end serverless data pipeline on AWS - covering the journey from raw data to actionable insights using services like S3, Glue DataBrew, SageMaker, and QuickSight. Do give it a read if you’re exploring data engineering, machine learning, or serverless analytics on AWS. #AWSSheBuildsTech #WomenInTech #AWSCommunity #DataEngineering #MachineLearning #Serverless #Cloud AWS UG for Women In Tech India

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As an SDM at Amazon, I often hear concerns that “𝗜𝗻𝘀𝗶𝘀𝘁 𝗼𝗻 𝘁𝗵𝗲 𝗛𝗶𝗴𝗵𝗲𝘀𝘁 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀” can become a bottleneck to delivery. That hasn’t been my experience. When practiced well, this principle isn’t about adding friction at the end of the process or holding teams to vague perfection. It’s about investing in a learning culture where engineers understand 𝘄𝗵𝘆 the bar exists and are empowered to raise it themselves through better design reviews, stronger testing, and clearer ownership. High standards, when paired with strong coaching and trust, actually accelerate delivery. Teams spend less time reworking issues, firefighting in production, or debating expectations. Instead, they build confidence in what they ship and continuously improve how they build it. For me, insisting on high standards is less about saying “no” and more about helping teams learn faster, make better trade-offs, and sustainably deliver great software at scale. That’s not a bottleneck but a leverage. The real question isn’t whether to insist on high standards, but how to practice them in a way that enables teams rather than slows them down. I’ll dive into that in my next post.

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Why does an E3 level SWE at Meta make only ~190k/year, while an E8 level engineer makes over ~$2M/year, even though both engineers are ICs and spend the same time at work? I have spent the last 5 years at Meta as an IC. I joined with a little over 15 years of experience, and I’ve worked with many solid engineers in this time. Here is how I think about that compensation jump. 1. Same hours, completely different “unit of work” An E3’s unit of work is usually a task or a ticket. An E8’s unit of work is a multi year problem for the company. E3: “Implement this service, fix this bug, write this feature.” E8: “How do we cut infra cost by 20 percent across this product” or “How do we make this platform safe to scale to 10x users.” One person is paid to execute. The other is paid to decide what is worth executing in the first place. 2. Radius of impact E3 usually impacts a file, a service, maybe a small team. E8 shapes whole orgs and product lines. If an E3 ships something great, the impact is great but local. If an E8 ships the right platform, hundreds of engineers become faster and the company saves or earns millions every year. Comp tracks the area of the circle you influence. 3. Risk and downside protection At junior levels, mistakes are usually contained and reversible. At senior staff levels, a bad call can burn tens of millions or damage the brand. E8s are paid for judgment under ambiguity. They decide which bets the company should not make, which migrations can wait, which “shiny idea” is going to kill reliability. You pay more to people whose good judgment protects you from very expensive failures.  4. They scale themselves This can happen in a few ways. 1. Delegation with ownership They define the shape of the problem, then hand large pieces to other senior and mid level engineers while keeping the bar and direction clear. 2. Knowledge that travels They write RFCs, public comments, FAQs, wikis, internal posts. One answer helps hundreds of people who will face the same issue next quarter. 3. Tools over heroics Instead of unblocking people manually all day, they build tools, libraries or guardrails so others can unblock themselves. One well designed tool can save thousands of engineering hours every year. This is what “scaling yourself” actually looks like. The company pays for that multiplier.  5. Ownership of the “uncomfortable problems” Junior engineers usually work inside a well defined box. E8s take ownership between the boxes. They pick up problems that:  Span many teams and no one really “owns”  Require aligning leaders who disagree  Have product, infra, legal and security angles at the same time Most people avoid those because they are messy, political and slow. Very senior ICs lean into them. That is where a lot of value sits. Continued ↓

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I saw this as a requirement in a job posting. "8+ years of experience with AI/ML, with last 2 years focused on GenAI as well as technologies like OpenAI, Claude, Gemini, LangChain, Agents, Vector databases, and approaches like Prompt Engineering, fine-tuning, etc." If you posted that job, I have a suggestion for you. If someone has 8+ years experience with AI/ML, your salary is missing a zero. $1,000,000+ per year. I mean... if you're asking for something that doesn't exist, then might as well pay them for it! SMH. If you comment saying AI/LM has been around that long, then you've missed the point entirely. You don't want/need someone with +8 years experience for a tech that changes soo rapidly. MAYBE +6 months. Anything older than that, and you'll be dealing with agency on a tech stack that is no longer relevant. Good luck with that!

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🔥 “𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗼𝘄𝗻 𝗿𝗲𝘃𝗲𝗻𝘂𝗲.” That one line almost broke me early in my PM career. I developed a '𝗰𝘂𝘀𝘁𝗼𝗺 𝗔𝗜/𝗠𝗟 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺' which needed vendor and engineering team and cross-functional owner collaboration. On paper, I was unblocking bottlenecks, driving execution, and pushing delivery forward.The leadership delivered a direct message about operations work when it was time to demonstrate impact by stating “𝗢𝗽𝘀 𝘄𝗼𝗿𝗸 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗼𝘃𝗲 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱𝗹𝗲.” But one project turned that thinking on its head. 💡 Instead of stopping at technical fixes, I partnered with 𝙛𝙞𝙣𝙖𝙣𝙘𝙚.We dug into the ripple effects:  1. The vendor delays resulted in the company missing the holiday launch window.  2. The optimization of models directly affects the BOM costs which results in higher costs.  3. The rework cycles operated in the background to exhaust budget allocations. The numbers were eye-opening: The company achieved an 18% decrease in rework expenses through process improvements at the vendor level.  • 3 weeks shaved off time-to-market, securing a seasonal launch  • The execution system produces more than $20M in revenue because it links operational activities to business results. That project didn’t just deliver a technical win — it reframed how leadership viewed product execution. Suddenly, “ops” wasn’t background noise; it was a 𝗽𝗿𝗼𝗳𝗶𝘁 𝗹𝗲𝘃𝗲𝗿.The recognition (and promotion) I received came from showing how 𝗰𝘂𝘀𝘁𝗼𝗺 𝗔𝗜/𝗠𝗟 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 = 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲. The real lesson? 👉PMs and TPMs don’t just own delivery; we engineer ROI through trust, partnerships, and execution that protects revenue and margin. I will always be thankful to my coach Shilpa Kulshrestha-Murdering Mediocrity who made me look at delivery metrics from a business perspective. PMs, TPMs and tech leaders who read this should investigate if they have ever felt invisible in terms of business impact. Connect process → efficiency → revenue. That’s where the hidden ROI lives. I have created slots on 𝗧𝗼𝗽𝗺𝗮𝘁𝗲 to assist others in creating their own impact story by converting “behind-the-scenes work” into career-defining ROI. Let’s connect, link: https://lnkd.in/gQN3KvYn

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Amazon is rapidly transforming into a factory of AI agents. According to CEO Andy Jassy, generative AI is a once-in-a-generation technology that is already reshaping everything from shopping to logistics. The latest Alexa+ can now perform actions, not just answer questions. Shopping is getting smarter with AI tools like “Buy for Me” and “Recommended Size.” Internally, generative AI is optimizing demand forecasting, warehouse management, and even auto-generating product descriptions. But the real shift is toward agency. Jassy is convinced that AI agents will soon be everywhere—coding, searching for information, translating, automating daily tasks, and acting as true colleagues. Amazon is already building over 1,000 generative AI services, and this is just the beginning. Expect workforce changes: AI will replace some functions but also create new roles. Jassy urges employees to build their AI literacy and learn to use and develop agents, because those who adapt will stay ahead. Is your organization ready for this new era of AI-driven agency?

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⚙️ How Redis Made QuizlerHQ’s Leaderboards Instantly Reactive (and Smarter) Small improvement, massive payoff. We used to rely on a cron job to update the QuizlerHQ leaderboards. It worked, but it lagged behind the action. Players would finish a game, check the board, and still see old scores. So, I rebuilt the system using Redis sorted sets for real-time updates. Now, every time a player completes a game: ⚡ Their score is pushed instantly to Redis and batch-saved to the database. ⚡ The leaderboard updates within milliseconds. ⚡ And, this is the fun part, we can now return the live leaderboard snapshot as part of the game summary view. That means as soon as a match ends, players see their new rank in context — not just “you scored 920 points,” but “you’re now #7 in Nigeria for General Knowledge.” It turned a routine backend task into a real moment of motivation. Technical wins that make emotional sense — that’s what I’m here for. #BuildInPublic #BackendDevelopment #Redis #Gamification #QuizlerHQ

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Amid new H-1B visa curbs getting implemented this year, the major US tech firms-Facebook (Meta), Amazon, Apple, Microsoft, Netflix, and Google (Alphabet) closed 2025, collectively hiring over 32,000 new employees in India. FAAMNG companies reported an 18% YoY uptick in hiring in specialised roles in India, with another 3,000-5,000 active job openings. https://lnkd.in/gynKeext

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Imagine you're sitting in an interview for an L5/L6 Software Engineer role at Google, and you're asked this question: "Design Uber for a metro city." Here's how I would break it down if I were the one being interviewed. There are lots of aspects we can cover here, but I'll only cover the high-level design. 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. Requirements The classic pain points in ride-hailing apps: - Matching is slow because the system can't efficiently find the nearest available driver from a pool of hundreds of thousands in real time - Consistency is fragile because concurrent ride requests can accidentally assign the same driver to multiple riders - Location tracking is expensive because millions of driver pings per minute overwhelm traditional databases Functional requirements - Rider inputs a start location and destination, gets a fare estimate and ETA - Rider requests a ride based on that estimate and gets matched with a nearby available driver in real time - Driver receives a ride request notification and can accept or deny it - Driver navigates to the pickup location, then to the destination, updating status along the way - Drivers periodically send their current location so the system knows where they are Out of scope (worth mentioning to show product sense) - Multiple car types (UberXL, Black, etc.) , we'll assume one tier - Ratings and reviews for drivers and riders - Scheduled rides in advance - Surge pricing algorithms - Payment processing Non-functional requirements - Low-latency matching: under 1 minute from ride request to driver match (or a failure message) - Consistency of matching: a ride is matched to exactly one driver, and a driver is never double-booked across rides - High availability outside of matching: the rest of the system (fare estimates, location updates, navigation) should be up at all times - High throughput in surges: the system must handle hundreds of thousands of requests within a region during festivals or special events Rest of the breakdown: https://lnkd.in/gieR-EqR

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Amazon announces major workforce reduction Amazon has confirmed plans to eliminate around 14,000 corporate roles, marking one of the largest layoffs in its history. According to Beth Galetti, SVP of People Experience and Technology, AI-driven efficiencies are one of the key factors behind the move — enabling the company to innovate faster and operate with “fewer layers and more ownership.” While these changes aim to make Amazon leaner and more agile, it’s still a difficult moment for many talented employees affected by this decision. How do you think AI-driven restructuring will reshape the corporate workforce over the next few years?

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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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I recently had a conversation with a junior engineer that got me excited about the next generation of amazon leaders. He mentioned, “I wanted to fix problem X in my team so rather than fixing it myself, I spent some time looking for other people who had fixed it. I noticed that a Principal Engineer had created a wiki about it, so I scheduled some time with him to learn about his solution.” Two things I loved about this that I want to see other junior engineers emulate. (1) He exhibited Learn and Be Curious as well as Respect What Came Before (the first one is an Amazon Leadership Principle, the second one is an Amazon Principal Engineering Tenet). Chances are others have solved problems similar to your problems. Even if you don’t exactly use their solution, you should at least understand what pitfalls they ran into and learn from their journey. There’s way too much duplication and wasted efforts. No matter what problem I'm trying to solve, I always look for others who have been on similar journeys. (2) In his quest to Dive Deep (another Amazon Leadership Principle), he personally reached out to a Principal Engineer. In tech, way too many junior engineers do not feel comfortable reaching out to a PE for a 1:1. When I was a more junior engineer, watching and learning from the PEs around me made a massive difference. PEs had to exhibit a high degree of judgement to get to where they are; they also had to exhibit a willingness to educate, so I bet the vast majority of them would be happy to talk to you. I have office hours open to anybody in the entire company, and I always learn a thing or two from every single person I talk to. These are both great behaviors we should all follow!

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Current Hiring Trends and Challenges for H-1B Visa Holders In today’s job market, many companies in the U.S. have increasingly narrowed their hiring focus to prioritize U.S. citizens and green card holders. This shift is especially noticeable in mid-to-large enterprises. Additionally, economic uncertainty, budget constraints, and evolving immigration policies have made many employers hesitant to take on the perceived administrative burden or risk associated with sponsoring foreign workers. As a result, qualified professionals on H-1B visas are frequently overlooked, with their resumes not even being considered despite strong skills and experience. What Can H-1B Professionals Do?

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Amazon layoffs again — due to AI-driven restructuring. Big reminder: no company is permanent, skills are. Roles change. Automation grows. Only constant? 👉 Learning and adapting. Thoughts with everyone impacted. 💙 #Amazon #Layoffs #AI #TechReality #FutureOfWork #Upskilling

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Utilizing advanced machine learning techniques, I analyzed data from Visa applicants to develop a predictive model aimed at streamlining the visa approval process. This model identifies key factors that significantly influence Visa status, allowing for tailored recommendations on which applicants should be certified or denied. The integration of data-driven insights enhances decision-making efficiency in visa approvals.

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Self-code review checklist I wish I had when I was a Jr. Engineer at Amazon: ○ Before I even run the code: – Do I fully understand the input, output, and constraints? –Did I rephrase the problem in my own words? –Did I list edge cases before touching the keyboard? –Did I pick the simplest working solution first? –Am I optimizing only after correctness is proven? ○ While writing the logic: –Are my variable and function names clear without comments? –Is each function solving only one responsibility? –Did I remove hard coded magic numbers? –Are my conditions readable at first glance? –Am I avoiding deep nesting where early returns work better? ○ After the first successful run: –Did I test empty input, single element, and max constraints? –Did I manually test worst case scenarios? –Did I validate both success and failure paths? –Did I remove all commented dead code? –Did I remove all temporary debug logs? ○ Performance and safety checks: –Is any loop unnecessarily nested? –Am I recalculating something that can be cached? –Is there any chance of null pointer or out of bounds access? –Is any blocking call happening on the main thread? –Are all error cases actually handled? ○ Readability and maintainability: –Can another engineer understand this without my explanation? –Are functions short and intention revealing? –Does the file structure feel obvious? –Are my comments explaining why, not just what? –Did I reduce complexity or just rename it? ○ Production readiness: –Are errors logged with enough context to debug later? –Are retries and fallbacks handled where needed? –Are timeouts set for all external calls? –Am I leaking any sensitive data in logs? –Would this survive real world traffic without babysitting? ○ Final gut check: –Would I confidently approve this in a real PR? –Would I be okay debugging this at 2 AM? –Would I understand this code after 6 months? –Would a senior engineer push back on this? –Did I actually improve the code or just rearrange it?

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Silicon Valley was built on Indian talent 🇮🇳, and now it’s our turn to create that ecosystem here. Talent alone isn’t enough — India needs stronger R&D funding, better AI infrastructure, and the courage to innovate beyond just copying models. If we get the intent right, “Silicon Valley Bharat” won’t just be a dream — it can be our reality. 🚀

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One year ago, I left the Bay Area, wrapped up nearly 8 years on US work visas (TN + H1B), and moved back home to Toronto. Living in the states as a skilled immigrant came with a constant hum of uncertainty around visas, but also lead to incredible opportunities early in my career that I don’t know if I would have found elsewhere. Our motivation for moving was primarily for personal reasons, but we asked ourselves many questions before pulling the trigger: > Can we find fulfillment and growth in our careers beyond the first role when we land? > What will our financial picture look like if we made $X? And spent $Y? > What does life outside of work look like? With family? And friends? > Is {healthcare|rental market|the TTC} really as bad as people say it is? For Canadian tech expats who are asking themselves similar questions especially in light of the recent announcements related to H1B, I’m happy to share my experience navigating the change and maybe some tips I learned from the whole process to make it less daunting 🇨🇦 #H1BNews #TorontoTech #LifeInToronto #Canada #CanadiansAbroad #H1B #TechToronto #Engineering

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Amazon plans to lay off up to 30,000 corporate employees According to Reuters, Amazon is preparing its largest round of corporate layoffs since 2022: potentially affecting up to 30,000 employees. The move comes as part of broader cost-cutting measures and an effort to address over-hiring that occurred during the COVID-19 pandemic. Amazon currently employs around 1.55 million people globally. Over the past two years, Amazon has been gradually reducing headcount across several divisions, including devices, communications, and podcasts. The upcoming layoffs are expected to impact multiple departments such as People Experience & Technology, Amazon Web Services, Devices & Services, and others. Managers of affected teams were reportedly instructed to undergo training on how to communicate layoff notices, which are set to be distributed via email starting October 28. CEO Andy Jassy continues to push initiatives aimed at reducing bureaucracy including trimming layers of management. Earlier this year, Amazon introduced an anonymous feedback line to report inefficiencies, resulting in more than 1,500 submissions and over 450 process improvements. Jassy also noted that broader adoption of AI tools may lead to further job reductions, especially through automation of repetitive and routine tasks. Sources told Reuters that the final number of layoffs could still change depending on Amazon’s evolving financial priorities. Fortune reported that as much as 15% of Amazon’s HR staff may be affected. In early 2025, Amazon reinstated a five-day in-office work policy, but the initiative failed to produce the expected voluntary turnover. As a result, employees who live far from offices — and cannot comply with the return-to-office mandate are reportedly being terminated without severance pay. Resource: https://lnkd.in/d2RD-EDf #Amazon #Layoffs #TechIndustry #BusinessNews #ArtificialIntelligence #FutureOfWork #HR #CorporateRestructuring #BigTech #Leadership

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