Navigating Elimination Rounds in Tech Interviews

There are so many people who asked me about my Goldman Sachs internship—how did I apply, what's the procedure… so here’s my journey! 🚀 I secured my internship through Goldman Sachs' Engineering Campus Hiring Program (ECHP), and here’s a breakdown of my experience: 📌 1. Application Process ✅ Applied via the official portal after participating in an off-campus drive. ✅ Prioritize filling out the form accurately before proceeding to the first online aptitude test. 📝 2. Aptitude Round ✅ Conducted on Hackerrank with 7 sections and 70 questions. ✅ Sections: Numerical Computations, Reasoning (Numerical, Abstract, Diagrammatic, Logical), and Comprehension. ✅ Marking: +5 for correct answers, -2 for incorrect ones. ✅ Attempted 50-55 questions correctly—a crucial elimination round. 🎓 3. Coursework ✅ Completed 3 Coursera specialization courses that helped in the upcoming rounds. 💻 4. Technical Test (Coding + MCQ) ✅ Another Hackerrank test, 1-hour time limit. ✅ 11 questions (MCQs + Coding) from Core CS Fundamentals and DSA. ✅ Marking: +5 for correct, -2 for incorrect answers. ✅ Divided into: 🔹 Programming questions 🔹 MCQs (OS, DBMS, OOPs, Networks) 🔹 Personal Introduction Section (2 theoretical questions) ✅ Proctored test with strict plagiarism checks. 🔥 TIP: Solve the easy coding question completely first, and avoid guessing MCQs. 🗣️ 5. Interview Rounds 📍 Round 1 (Technical Interview) ✅ Zoom interview, focused on DSA & basic OS questions. ✅ Solved 3 DSA questions (1 Array, 2 Binary Trees). ✅ Explained time & space complexity for each solution. 📍 Round 2 (Technical Interview) ✅ Covered DSA & DBMS concepts along with SQL questions. ✅ Solved one puzzle question correctly. 📍 Round 3 (Technical Interview) ✅ Started with introduction, then moved to one hard DSA question. ✅ Wrote working, optimized code with proper time & space complexity analysis. ⏳ 6. Post-Interview Experience ✅ Waited for the results, which were communicated via email within 20 days. 🚀 7. Life-Changing Tips ✔️ First two online rounds had major eliminations—prepare well! ✔️ Have faith in your practice and skills—this is key. ✔️ Solve previous Goldman Sachs DSA questions. ✔️ Read interview experiences of others and reach out for help. ✔️ Strong fundamentals in DBMS, OOPs, OS are a must. Hope this helps! Feel free to connect if you have any questions. 🚀 #GoldmanSachs #hiring #HiringProcess #internship #tech

Most candidates treat interview questions like trivia. The interviewer asks, they answer, hopefully they sound good. That is not what is happening on the other side of the table. Every common question is a probe for a specific signal. The question is the wrapper. The signal is what gets scored. Signal is the evidence the interviewer is actually looking for underneath the question. Not your words. The thing your words prove about you. When you answer the wrapper instead of the signal, you lose points you did not know were on the board. Three examples. 1) "Tell me about yourself." This sounds like a warm-up. It is not. It is a positioning test. The interviewer is checking whether you can pick the parts of your story that matter for this role and company. Most people list their CV chronologically and sound like every other candidate. The strong answers pick one thread and point it straight at this role. Answer the signal: what you do now, the relevant thread running through your past roles, and why this one is the next step. Three parts. 90 seconds. Done. 2) "Why do you want to work here?" This is not about flattery. It is an effort check. The interviewer is trying to work out if you actually know what this company does and why you would be good here, or if you are spraying applications at anything that moves. Generic answers about culture and mission score zero. They sound like every other candidate. Specific answers about the team, the stage the company is in, or a recent product decision score high. It shows you did the work before you walked in. Answer the signal: one thing you like about this company that is not true of their main competitor. 3) "What is your greatest weakness?" This is the most misunderstood question in tech interviews. It is not a trap. It is a self-awareness test. The interviewer is not trying to catch you out. They are looking for whether you can name one honestly, show you know where it surfaces, and explain what you do about it. Candidates who deflect with fake weaknesses like "I care too much" fail it instantly. Candidates who name a real one and describe their system for managing it pass it instantly. Answer the signal: a real limitation, the context where it shows up, and what you do about it. Every common interview question is an iceberg. The tip is the question itself. The real scoring happens underneath. Most candidates answer the tip. The ones who pass the interview answer what is below the waterline. Once you learn to see what is underneath, the same question stops being cliché and starts being useful. Which one trips you up the most?

I recently made the switch to 𝐆𝐨𝐨𝐠𝐥𝐞 as a 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫! 🚀 But along the way, I also received an offer from 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 for a 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 role. Here is my Microsoft Interview Experience: The interview journey was insightful, and I wanted to share my experience, hoping it might help others navigating similar paths! 𝐑𝐨𝐮𝐧𝐝 𝟏: 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐃𝐞𝐞𝐩 𝐃𝐢𝐯𝐞 The first round tested my core Data Engineering knowledge: • 𝐄𝐓𝐋, 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠: We kicked things off with a discussion on the fundamental building blocks of data pipelines, exploring which data sources I’ve worked with and how I’ve modeled data in the past. • 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: They asked me to break down an ML problem I had worked on (algorithm and pseudo-code), focusing on the practical impact of the model in production environments. • 𝐒𝐐𝐋 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: A SQL problem requiring knowledge of Window Functions and CTEs (think “rank employees by department” or “calculate rolling averages” kind of questions). 𝐑𝐨𝐮𝐧𝐝 𝟐: 𝐀𝐳𝐮𝐫𝐞, 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐌𝐨𝐫𝐞 This round went deeper into the cloud stack: • 𝐀𝐳𝐮𝐫𝐞 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧: Since I had experience with Azure, we discussed various Azure resources I had worked with (like Data Factory, Synapse, and Cosmos DB) and how I used them in real-world scenarios. • 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬: One standout area was few real-life scenario where I had to design a data model. • 𝐒𝐐𝐋 𝐑𝐞𝐯𝐢𝐬𝐢𝐭𝐞𝐝: Another SQL problem popped up, again featuring Window Functions — a reminder that these are absolute essentials for data engineers! 𝐑𝐨𝐮𝐧𝐝 𝟑: 𝐓𝐡𝐞 𝐇𝐢𝐫𝐢𝐧𝐠 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 In this final round, I had a candid discussion with the hiring manager: • 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐖𝐚𝐥𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐬: We dug into my past projects, including how I solved scalability issues and optimized system performance (perfect time to highlight the work I did at my previous companies). • 𝐃𝐞𝐬𝐢𝐠𝐧 & 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬: Questions shifted towards design choices—what would I prioritize in different situations? How would I handle conflicting deadlines? Plus, some behavioral questions to assess cultural fit. • 𝐓𝐞𝐚𝐦 𝐃𝐲𝐧𝐚𝐦𝐢𝐜𝐬: Since I was interviewing for a specific team, the hiring manager gave me insights into the team structure and their expectations for the role. Switching to Google as a Data Engineer was a rewarding decision, but I’ll always value the learning experiences from interviewing at Microsoft. Interviews are not just about answering questions—they’re a great opportunity to learn, reflect on your strengths, and grow!💪🏻 Hope my experience helps anyone preparing for their next big opportunity. Feel free to reach out if you want to discuss more! 😄 #careerjourney #interviewexperience #google #dataengineering #microsoft #techcareers

How to stop failing data interviews (even when your resume is perfect) Most data professionals I talk to have strong resumes. Solid projects. Relevant experience. The right tools. And they still don't get the offer. Not because they aren't good enough. Because they never learned how to interview. Here's what's actually costing you the job and how to fix it: 1. You answer questions. You don't tell stories. Hiring managers don't remember answers. They remember impact. When they ask about a project, they don't want a technical walkthrough. They want to feel what changed because of you. 👉 Fix: Use this structure for every answer, what was broken, what you did, what the business result was. Three sentences. Lead with the outcome. 2. You optimize for correctness. Not for clarity. Data people are wired to be precise. That's a strength in the role. It's a weakness in the interview room. 👉 Fix: Business outcome first. Methodology second. If they want to go deeper, they'll ask. 3. You wing the behavioral rounds. The technical round isn't where most data candidates lose the offer. It's the behavioral. "Tell me about a time you disagreed with a stakeholder." These feel uncomfortable so you improvise. And it shows. 👉 Fix: Build a bank of 10 stories from your experience. Map them to the most common behavioral questions. Practice them out loud at least once before every interview. 4. You treat the Q&A as a formality. "Do you have any questions?" is not a formality. It's your last impression. Generic questions signal a generic candidate. 👉 Fix: Prepare 3 questions that show you've thought about the role, the team, and the problem they're hiring to solve. Make them think "this person already gets it." 5. You have no feedback loop. Interview ends. You wait. You get rejected. No idea why. So you walk into the next one and repeat the exact same mistake in a different room. 👉 Fix: After every interview, score yourself on three things, how clear was your storytelling, how well you read the room, and where you lost energy. Fix one thing before the next interview. Your resume got you in the door. Your interview skills decide if you walk out with the offer. Follow me for real, uncomfortable, and practical data job search advice.

Cracking the Coding Interview: The first round of most tech interviews is a coding challenge, and clearing it is crucial. Here’s a step-by-step guide to help you ace it: 1. Understand the Problem Rephrase the question to ensure clarity and note key requirements. 2. Ask About Edge Cases Clarify potential tricky inputs like empty arrays or negative numbers. 3. Discuss the Brute Force Solution Briefly mention it, but focus on optimizing. 4. Explain the Optimal Solution Discuss the time and space complexity of your solution. 5. Check if the Solution is Feasible Confirm with the interviewer if they’re happy with your approach. 6.Write Clear, Simple Code Use a language you’re comfortable with and keep the code clean. 7. Dry Run Your Code Step through the code to catch errors and edge cases. 8. Adapt as Needed Refactor if needed after the dry run, especially for edge cases. 9. Review Complexity Double-check the time/space complexity and ask if more tests are needed. 10. Seek Feedback Always ask for feedback on your approach. Pro Tip: Focus on Patterns, Not Problems Understanding problem-solving patterns (like sliding windows or dynamic programming) will help you solve a wide range of coding challenges. #CodingInterviewTips #TechInterviews #InterviewPrep

I interviewed at Apple, Google, and Amazon. Here’s what most people don’t tell you about how interviews really work at these tech giants👇 Apple: Apple interviews are intense, especially for full-time roles. For Internships- → 3–4 technical rounds + 1 HR round. Full-time: 1/ Starts with a phone screening 2/ If you do well, you move to a loop of 8 interviews (split into two sessions of 4 each) ✅ Rounds 1–4: Focus on technical/coding and system design ✅ Rounds 5–8: Dive deeper into problem-solving, team fit, and one final round with a VP or Director Important Tips: → Apple interviews often include “stress testing.” Interviewers may interrupt your answers, throw back-to-back questions, or challenge your assumptions. →They may even ask: “Are you sure you learned this in college?” Not to humiliate you, but to see how you handle pressure. Tip: Stay calm and try navigating the interview. Note- Apple typically has a waiting period of 3-6 months before you can reapply for the same or similar roles. Google: Google’s process is structured and heavy on problem-solving. ✅ Stage 1: Online Assessment (OA) ↳ This is a filtering test that includes behavioral questions to test your decision making especially in ambiguous situations and logic-based problem-solving scenarios (even for non-tech roles). ✅ Stage 2: Coding Round ↳ Whether you’re applying for SWE or a test role, you’ll be expected to clear a coding challenge (python is basic) Tip: Be prepared with DSA (Data Structures & Algorithms). Use Leetcode, especially Google-tagged questions. ✅ Stage 3: Interview Loop (4–5 Rounds) ↳ Technical interviews ↳ Role-specific problem-solving ↳ Googleyness & behavioral interviews Note: If you're rejected after an interview, Google generally requires a 6 to 12-month waiting period before reapplying for the same or similar roles. Amazon: Amazon’s process is heavily based on its leadership principles, customer obsession, and delivering results. And they expect you to use those principles while answering questions. ✅ Stage 1: Phone Screening ↳1 or 2 calls covering technical basics and behavioral questions ✅ Stage 2: Onsite/Virtual Loop (4–5 Rounds) ↳ 2–3 technical rounds ↳ 1–2 behavioral/leadership rounds Tip: Keep real stories ready- projects where you failed, resolved conflicts, or made data-driven decisions. Note: Reapplication policies change and may depend on individual circumstances, but the general waiting period is 3-6 months. MAANG companies test your thinking, your grit, your ability to learn fast and your fit in their culture. So, ↳ Prepare beyond Leetcode. ↳ Master behavioral storytelling. ↳ Learn to “actively listen” under pressure. Comment “guide” if you want a prep guide for any of these companies So tell me about your own interview experience, would love to learn from you :) #FAANG #interview

🚀 𝗠𝘆 𝗔𝗺𝗮𝘇𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 🎉 Since sharing my exciting news about joining Amazon, many of you have reached out asking about my interview experience. Today, I’m excited to break it down and share my learnings, which I hope will help anyone navigating a similar process. ⏳ 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 1️⃣ ͟A͟p͟p͟l͟i͟c͟a͟t͟i͟o͟n͟: I applied through an employee referral and was contacted by a recruiter within a week. This was followed by a few admin questions like location preferences, visa status, and availability. 2️⃣ P͟h͟o͟n͟e͟ ͟I͟n͟t͟e͟r͟v͟i͟e͟w͟: This was a resume-driven conversation with the Hiring Manager, filled with behavioral questions. I used the 𝗦𝗧𝗔𝗥 method to frame my answers and ensured they aligned with the role’s responsibilities. 👉 𝘗𝘳𝘰 𝘛𝘪𝘱: Highlight the IMPACT of your work. 3️⃣ F͟i͟n͟a͟l͟ ͟I͟n͟t͟e͟r͟v͟i͟e͟w͟ ͟L͟o͟o͟p (2 Days, 5 Rounds): 4 Behavioral Rounds focused on Leadership Principles and situational responses. 👉 𝘗𝘳𝘰 𝘛𝘪𝘱: Prepare two examples for each principle. Repeating examples is fine, but having variety strengthens your answers. 1 Technical Round: This round was intense! It started with basic SQL questions (joins, percentage calculations), but the final question escalated quickly to a Leetcode Hard problem that was truly challenging. The problem itself was ambiguous and required a deep understanding of SQL. The solution involved multiple window functions and CTEs, which made it tricky to even know where to begin. 👉 𝘗𝘳𝘰 𝘛𝘪𝘱: Practice Leetcode Medium and Hard problems, and focus on explaining your thought process—the interviewer is as much interested in how you think as in the solution itself. 🎓 𝗠𝘆 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 1️⃣ G͟o͟ ͟B͟e͟y͟o͟n͟d͟ ͟S͟t͟a͟n͟d͟a͟r͟d͟ ͟P͟r͟e͟p͟: I reached out to colleagues who had interviewed for similar roles and got some insights that shaped my strategy. Never underestimate the value of connecting with people who’ve been through the process. 2️⃣ S͟t͟a͟y͟ ͟C͟o͟m͟p͟o͟s͟e͟d͟: Interviewing for tech roles with #FAANGs requires a ton of preparation. Instead of rushing to answer, I took a moment to think and responded with clarity. Remember, interviews aren’t just about showing your skills—they’re about connecting with the interviewer. 3️⃣ L͟e͟a͟d͟e͟r͟s͟h͟i͟p͟ ͟P͟r͟i͟n͟c͟i͟p͟l͟e͟s͟ ͟&͟ ͟S͟T͟A͟R͟ ͟A͟r͟e͟ ͟N͟o͟n͟-͟N͟e͟g͟o͟t͟i͟a͟b͟l͟e͟: Amazon’s #LeadershipPrinciples and the #STAR format are the backbone of the entire interview process. I can’t stress enough how critical these two elements are. Master them, and you’ll have a framework that’ll help you excel in the interview and in your career. 🌟 𝗪𝗿𝗮𝗽𝗽𝗶𝗻𝗴 𝗨𝗽 To everyone working through interviews, remember this: Preparation, patience, and clarity will take you far. The right opportunity is closer than you think! #Amazon #InterviewExperience #BIE #JobSearch #InterviewTips #CareerGrowth #Leadership #JobInterview