Diksha Baruah

One thing your manager will never tell you Your promotion depends less on your performance and more on whether your team is actually growing. When I joined Amazon as an SDE-1, my team had 25 people. By the time I became Senior, it had 120+. Looking back, my “fast promotion” wasn’t magic. The org was expanding like crazy and growth creates opportunity. Here is the part people don’t like hearing: ❌ If your team hasn’t grown in years, your career won’t either. ❌ It doesn’t matter how good you are. There is nowhere to go. ❌ The only real path up becomes waiting for someone above you to leave. So if you want to grow faster: Choose teams that are growing. Not teams that are comfortable. Skills matter. Environment matters more.

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I am a Senior Data Engineer at Amazon with more than 12 years of experience. Here is the #1 advice I give to anyone wanting to break into Data Engineering in 2025, from my own journey and what I see in the industry. [1] don’t wait for the “perfect” data engineering job, start from wherever you are ○ Most people think you need to land a pure data engineering role to start your journey. That’s not true. ○ My own path began as an analyst. I got thrown into ETL tools, enterprise data warehouses, and learned by building real pipelines for real business problems. ○ Don’t wait for someone to hand you a data engineering title. Start using data skills in your current job, even if it’s not your official title. ○ Look for chances to automate reports, build small data flows, or help your team answer questions with data. It all counts. ○ The best way to learn is by working with real, messy, business-critical data. [2] learn on the job ○ Formal education helped me, but most of my actual skills came from building things at work. ○ Theory is helpful, but the day-to-day challenges, managing ambiguity, scaling systems, and handling constant change, only show up in projects. ○ Don’t get stuck in “prep” mode. The sooner you start solving actual problems, the faster you’ll grow. ○ Every job switch, every new project, every failed deployment teaches you something you can’t learn from a book. [3] say yes to tough projects and new tech ○ I didn’t plan my path. I said yes to working with new tools: Teradata, Informatica, then Spark and Scala when the opportunity came. ○ When my manager asked me to build distributed pipelines, I had just 15 days to figure it out. I jumped in, learned fast, and shipped to production. ○ Every tough assignment (even when I doubted myself) became the thing that leveled up my skills and confidence. ○ Don’t shy away from new frameworks or bigger challenges. Dive in and figure it out along the way. [4] use AI, don’t fear it ○ The role is changing. AI is now a core part of our workflows, writing docs, reviewing code, speeding up development, and democratizing access to data. ○ I use AI for everything from formatting documents to writing better emails and even debugging. ○ Stay current by using AI in your day-to-day work. Experiment, adjust, and keep learning as the tools improve. ○ AI is not a threat, it’s a tool that lets you move faster and focus on the logic and architecture that matter most. [5] don’t focus on brands, focus on learning and growth ○ My best learning came from hands-on work, not company logos. ○ Take the job that gets you closest to working with data, even if it’s not your dream company. ○ The experience you gain will open better doors as you go. ○ Career progress in data engineering is practical: build, learn, adapt, and the opportunities will follow.

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I am not at Amazon working as a Sr. Data Engineer just because I know data engineering. Plenty of people do. I was hired because I know how to drive outcomes using data. And that is what any business wants out of their data team. Let me be clear: I’m not hired to build pipelines. ╪ I’m hired to get the right data to the right people at the right time, So business decisions move faster. I’m not hired to model data. ╪ I’m hired to extract meaning from it and turn it into action. I’m not hired to optimize Spark jobs. ╪ I’m hired to reduce processing time from 3 hours to 10 minutes, so teams can iterate quicker. Your job is not to do tasks. Your job is to create outcomes that matter to the business. And the sooner you internalize that, the faster you grow. → Not "I built a dashboard" → But "I gave the sales team visibility that unlocked X amount in revenue" → Not "I cleaned the data" → But "I fixed the pipeline that unblocked 3 product teams" This is how top-tier data engineers think. They don’t get lost in the tools. They don’t chase complexity. They chase impact. That’s the real job. And that’s where your leverage begins. Agree?

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I am a Principal Data Engineer at Amazon with more than 12 years of experience. Here is the #1 advice I give to anyone wanting to break into Data Engineering in 2025, from my own journey and what I see in the industry. [1] don’t wait for the “perfect” data engineering job, start from wherever you are ○ Most people think you need to land a pure data engineering role to start your journey. That’s not true. ○ My own path began as an analyst. I got thrown into ETL tools, enterprise data warehouses, and learned by building real pipelines for real business problems. ○ Don’t wait for someone to hand you a data engineering title. Start using data skills in your current job, even if it’s not your official title. ○ Look for chances to automate reports, build small data flows, or help your team answer questions with data. It all counts. ○ The best way to learn is by working with real, messy, business-critical data. [2] learn on the job ○ Formal education helped me, but most of my actual skills came from building things at work. ○ Theory is helpful, but the day-to-day challenges, managing ambiguity, scaling systems, and handling constant change, only show up in projects. ○ Don’t get stuck in “prep” mode. The sooner you start solving actual problems, the faster you’ll grow. ○ Every job switch, every new project, every failed deployment teaches you something you can’t learn from a book. [3] say yes to tough projects and new tech ○ I didn’t plan my path. I said yes to working with new tools: Teradata, Informatica, then Spark and Scala when the opportunity came. ○ When my manager asked me to build distributed pipelines, I had just 15 days to figure it out. I jumped in, learned fast, and shipped to production. ○ Every tough assignment (even when I doubted myself) became the thing that leveled up my skills and confidence. ○ Don’t shy away from new frameworks or bigger challenges. Dive in and figure it out along the way. [4] use AI, don’t fear it ○ The role is changing. AI is now a core part of our workflows, writing docs, reviewing code, speeding up development, and democratizing access to data. ○ I use AI for everything from formatting documents to writing better emails and even debugging. ○ Stay current by using AI in your day-to-day work. Experiment, adjust, and keep learning as the tools improve. ○ AI is not a threat, it’s a tool that lets you move faster and focus on the logic and architecture that matter most. [5] don’t focus on brands, focus on learning and growth ○ My best learning came from hands-on work, not company logos. ○ Take the job that gets you closest to working with data, even if it’s not your dream company. ○ The experience you gain will open better doors as you go. ○ Career progress in data engineering is practical: build, learn, adapt, and the opportunities will follow.

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Despite having 2 years of experience, I failed my interview at Google. Despite years of ML experience, I faced rejections at Google and Roku due to their DSA-heavy interviews. Here are two tough questions I encountered that cost the opportunity 1️⃣ Google ML Interview: Given a massive, infinite data stream of user events, implement an efficient algorithm to identify the top-k most frequent items in real time. 🔹 Challenge: Handling memory constraints and optimizing for speed. 2️⃣ Roku ML Interview: Design a data structure to efficiently support insertions, deletions, and retrieving the median of a dynamically growing dataset. 🔹 Challenge: Achieving O(log N) operations for real-time median calculations. While these questions tested my algorithmic skills, they didn’t evaluate my ability to build, scale, and deploy ML systems which is what truly matters in production. Later, companies like Microsoft and Prezent focused on ML deployment, feature engineering, and system design, leading to my success. If you're aiming for companies like Google and Microsoft, upskill strategically. Don’t just grind LeetCode, focus on both DSA and real-world ML skills. ✅ Master DSA for coding rounds, as some companies heavily emphasize it. ✅ Build, scale, and deploy ML systems to showcase practical expertise in production-ready AI. A balanced approach is key—strong algorithms get you through the door, but real-world ML skills help you thrive. Many ML engineers assume that just knowing ML models is enough but companies want engineers who can deploy and maintain AI at scale. Every company has different expectations, so you need to be prepared for both. Experience alone won’t get you into top tech companies but your skills and domain knowledge will. So, Instead of chasing quick tips, focus on building in-depth knowledge from the basics to advanced levels. One platform I would suggest checking out is Bosscoder Academy. They provide 1-on-1 mentorship from top tech companies which can help you ease your journey. Check their complete program here: https://bit.ly/3FzsmyE They have helped 800+ professionals get placed in top companies. #SQL #DataScience #MachineLearning #collab

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I’ve sat in interviews at 3 MAANGM companies (Google, Microsoft, Amazon) …and here are 3 things you NEED to know before your next interview 👇 (Esp. if you’re preparing for mid-to-senior-level SDE roles at top PBCs, here’s what I’ve learned along the way ✅) 💡 TIP #1: Give Data Structures & Algorithms (DSA) fundamentals PRIORITY. → Most tech giants, esp. Google, expect you to be strong in Arrays, Strings, Trees, Graphs, and DP. → Don’t just solve random problems. Instead, focus on mastering core patterns, Sliding Window, Binary Search, DFS/BFS, Trie, DP state transitions. 💡 TIP #2: Don’t think Behavioral Rounds are “OPTIONAL” At PBCs like Amazon, your real contributions and leadership will matter. So always quantify your impact → “Led a team of 4 engineers to reduce API latency by 30%” is far better than “Worked on APIs”. 💡 TIP #3: System Design will become UNAVOIDABLE (even mid-level) Interviewers will expect you to design scalable, reliable systems, and explain tradeoffs clearly, especially at Microsoft. Practice structuring your thought process ⬇️ → Start with requirements → High-level architecture → Data modeling → API contracts → Scalability challenges. These actionable tips can help you switch to MAANG-level PBCs, so if you’re working a 9-5 and struggling to prep for your next big role… I highly recommend giving Bosscoder Academy a try. 🔗 Explore their programs here: https://bit.ly/4gq7uIp They’ve already helped 2,200+ engineers land roles in top PBCs with structured prep in DSA, System Design, and Full Stack, 1:1 mentorship, mock interviews, resume support, and 100% job switch guidance. #sdeprep #systemdesign #interviewtips #tech #jobswitch #maang #pbcs #collab #interviews #jobs #dsa #resume

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Here’s exactly how I would crack a $150K+ SWE job at Microsoft in 6 months. (A real strategy my client used to land interviews at Microsoft, Meta, and Stripe) Too many engineers prepare hard to crack MAANG. But they lack a solid strategy. Here’s the exact roadmap I'll follow if I wanted to land a SWE job at Microsoft. Step 1: Resume + Role Clarity → Reverse-engineer the JD Study at least 10+ Microsoft SWE job listings. Highlight recurring keywords, must-haves, and preferred tools. → Rewrite your resume like a product pitch Show measurable impact: Good: “Improved load time by 43%” Bad: “Worked on performance optimization.” → Make it keyword-optimized for ATS without sounding robotic. Step 2: Master DSA the Microsoft Way → Focus on patterns, not problems Microsoft LOVES: Sliding window Trees (esp. DFS/BFS) Graphs Dynamic programming → Suggested Platforms: Leetcode (Microsoft tag), Neetcode(dot)io roadmap, Grokking series on Educative → 2 problems/day + 1 mock interview/week = compounding prep Step 3: System Design (yes, even for junior roles) → Start with High-Level Design (HLD): Learn how to design APIs, caching, rate limiting, and DB scaling. → Build a project where you actually implement what you learn. Step 4: Microsoft-Specific Behavioral Prep → Microsoft uses structured behavioral interviews Focus on “3As” framework: Action → Approach → Aftermath → Use real projects to show: Collaboration Adaptability Customer focus Engineering rigor → Prep 8–10 STAR stories mapped to their values Step 5: Mock + Real-World Practice → Do 4–5 peer or mentor-led mock interviews (especially for behavioral + design) → Record yourself. Watch for filler words, unstructured answers, lack of metrics. → Apply for 5 roles/week—Microsoft + similar-sized companies (to get into interview flow) Step 6: Apply Strategically + Use Referrals → Connect with 3–5 engineers/recruiters/week on LinkedIn. Don’t ask for a referral right away - engage first. → Reach out using tailored messages like: “Hi [Name], I’ve been preparing for an SWE role at Microsoft. Your journey from [X] to [Microsoft] really stood out. If you're open, I’d love to learn more about your experience.” → Submit applications using referrals whenever possible + 1-click apply where relevant The client who followed this playbook now works at Microsoft Azure and had 3 competing offers before accepting. It’s not about doing everything. It’s about doing the right things in the right order. Give yourself 6 months and follow this roadmap to make it a reality. Share this with someone who dreams of working at Microsoft. P.S. Follow me if you are a tech job seeker in the U.S. I share practical advice that gets you hired. — Additional resources: https://lnkd.in/eKxQmYtP. https://lnkd.in/gwRWpXR9. https://lnkd.in/g94_Cziv

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I am a Software Engineer at Meta with almost 2 decades of experience. Here’re the 5 learnings if you want to grow faster as a SWE: 1. Know the next level before promotion After you are performing well at your current role expectations, start approaching the next level. - Learn from seniors, understand their perspective - Look for areas you can help & can get noticed In short, prove that you deserve the promotion before you ask for it. 2. Become your own user - go on reddit or twitter threads - read through the users feedbacks and conversations - be empathetic & maybe have a conversation with few users This shift helped me move beyond just shipping features to actually improving the experience. 3. Data is your saviour Start diving into logs, events, and exceptions more deeply. - Where are users spending the most time?  - What’s making them drop off? - What are the most common exceptions?  - Which workflows are unintuitive? You can make better engineering decisions if you spend some time consuming the data 4. Every problem is not yours to solve Ask yourself: - Is this issue truly high-impact, or am I just trying to get it off my plate? - Is this a problem I should solve, or is it better to guide someone else to take it on? - Delegating was tough at first. But it’s crucial to identify your problems and delegate rest. The best engineers I’ve worked with aren’t the ones who take on the most work—they’re the ones who ensure the right work gets done by the right people. 5. Be a force multiplier - document more to save others time - build tools and scripts that automate tedious tasks - Guide others to help achieve their goals   Your team will move faster, and you will become more valuable—not just as an engineer but as a force multiplier. => Final Thought: The biggest shifts in my career didn’t come from just writing better code—they came from these small shifts. If you’re looking to grow as an engineer, try incorporating some of these steps into your own journey. Raman Walia

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Last year, one of my colleagues made a bold move; he transitioned from an SDE role to Data Science. He wasn’t unhappy as a Software Engineer. But he was deeply interested in solving business problems with data, building ML models, and driving product decisions through insights. The whole transition wasn’t easy. He had to learn statistics, machine learning, SQL, Python for data, and pick up a whole new mindset. But within 8 months, he cracked a Data Scientist role and not just anywhere, but at the big 4. Here’s what helped me: 👉 Following a structured path 👉 Working on both tech & practical aspects, by working on real-world projects. 👉 Focusing on developing his skills, rather than blindly following multiple YouTube videos. I’m sharing this because many people feel stuck in roles they’ve outgrown, even when they have the potential to do more. The truth is: You don’t need to start over. You need to reskill smartly. Learn skills that makes the difference. But learn from resources that are structured & cater to your personal needs. If you’re considering a similar switch, I highly recommend checking out Bosscoder Academy. Explore their programs here: https://bit.ly/4jngz4u They have helped through: ✅Resume building and optimization, LinkedIn optimization, and job switch assistance. ✅ Structured curriculum (ML, NLP, Python, SQL, Power BI) ✅ 1:1 guidance from Data Scientists at Google, Samsung, etc. ✅ Hands-on projects to build a strong portfolio Your career is not a label, it’s a journey. Own the direction. #DataScience #CareerSwitch #MachineLearning #SDEToDS #TechCareers

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Layoffs = Growth? Bumble just proved it. 🚨 Bumble’s stock soared 26% overnight — not because of a new product, not because of a market expansion… …but because they laid off 30% of their workforce. That’s ~240 jobs cut to save $40 million/year. Harsh? Yes. Strategic? The market sure thinks so. 📉 Bumble’s valuation had plummeted from $7.7B in 2021 to just $538M. Now, with founder Whitney Wolfe Herd back in the CEO seat, they’re rewriting the playbook: → Slim down. → Reinvest in product and tech. → Focus on execution, not just expansion. It’s a cold reminder of where public markets place their bets: efficiency and clarity of vision > bloated org charts. 💬 This isn’t just a Bumble story. It’s a broader signal across tech: In 2025, lean may be the new scale.

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H1B changes are out of our control. Job search isn’t. It’s been 2 years in the U.S., and take it from me — this post can help you avoid the mistakes I made. I wish someone had warned me earlier. When I first landed here, I believed my college name would be enough. Recruiters would call, and interviews would flow. But reality hit different, and here is what my 2 years looked like: 1. It was my friends at Columbia who first taught me how to actually do cold outreach. Until then, I had no idea that sending those messages, building those connections, would become a lifeline. 2. Without that push from them, I would have been completely lost. Learning: Make good friends, they are your family here! 3. But even with their support, I slipped. With classes, assignments, exams, and a part-time job, I felt busy — but I wasn’t focused on the right things. By the time I realized my mistake, I had already missed out on a summer internship. 4. That was on me, not the job market. Learning: There is no substitute for applying for jobs; it's a numbers game, and I barely applied to 400 internships. 5. So when full-time recruiting came around, I was determined not to repeat that. I applied to over 1,000 jobs, reached out relentlessly (LinkedIn, Email), used every job portal that you can think of, and got call-backs from Meta, Apple, Bain & Company, Amazon, and many startups. Learning: Keep your head down and just apply, email, and send LinkedIn connects. Now, with the new H1B changes, I know many of you are feeling the same uncertainty that I am feeling. And I want you to know: you’re not alone. A lot is not in our control, so let's not waste time thinking about what will happen and capitalize on what we have right now. This time of hiring won't come back again! I didn't always find people to network with when I needed them most, and I don't want anyone else to feel that way. My inbox is always open, so connect with me and feel free to send a message! You've already shown courage by choosing this path. Don't doubt yourself — your story is still unfolding, and there is a happy ending for all of us. #Careers #Technology #H1B #JobSearch #Hiring

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