Prioritizing Problem-Solving Skills in Coding Interviews

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

I interview 2 Senior / Staff Engineers at Meta every week for E5 and above roles. If they asked me how to perform better in coding interviews, I would start with these 3 things. These are people with 8, 10, 15 years of experience. 1. Treat the interview like a joint problem-solving session When I ask an algo question, I want to hear your thought process from the first minute. Start wide, then narrow: - First: state the brute force   If I do this in the simplest way, I would:   a) scan all elements,   b) try all pairs or all paths,   c) this gives time O(N²) and space O(N²). - Second: show that you respect constraints   Now, if N is 10⁵, O(N²) will not work. So I will look for something closer to O(N log N) or O(N). - Third: propose concrete options   Option 1: sort and scan with two pointers, complexity X.   Option 2: use a HashMap and one pass, complexity Y. Then ask: Given these options, which path would you prefer I implement first This does a few important things: - It shows you understand trade-offs, not only syntax. - It gives me a chance to correct your direction early. -  It proves you are not guessing. If your first solution is not optimal and I ask for a better one, keep the same structure: offer two ways to improve, sketch pseudocode, confirm the choice, then code. 2. Enunciate while you code One of the hardest interviews to conduct is when the candidate goes silent and types for 20 minutes. As an interviewer, I am sitting there thinking: - Are they stuck - Are they copying a pattern from memory - Do they even know what this line is doing You can fix this just by speaking while you code. Example of what I want to hear: - I am creating this array to store prefix sums. - This loop walks the list once and builds the map from value to index. - This condition picks the earliest valid index for the answer. Now I do not have to reverse engineer your solution. I can follow your logic in real time. I know you understand your code. You also get an extra benefit: if you say something and your code does something else, you will catch that mismatch yourself. Strong communication here is not about fancy English. You can speak in simple language as long as the mental model is clear. 3. Dry run your code Too many candidates write the final line and look up, waiting for a verdict. A much better move is to verify your own work: - Take the example input from the problem. - Feed it through your code step by step. - Say out loud what each variable holds after each key step. For example: - After the first iteration, the stack contains these indices. - Here we update the answer from 3 to 5 because the condition is true. - At the end of the loop, we return 5, which matches the expected output. Two good things happen: - You often find your own typos, off by one error or edge cases. - You show that testing and verification are part of how you think, not an afterthought.

After giving & conducting 100+ coding interviews over the past 12+ years at startups and MAANG+ companies, I’ve realized one thing: —Interviews aren’t about who writes the most code. —they’re about who thinks in the most structured way. Here are 30 key insights I’ve learned from sitting on both sides of the table - as an interviewee striving to prove my skills and as an interviewer evaluating candidates:   - Communicate trade-offs clearly - Test your code with multiple cases - Start with a brute force, then improve - Clarify edge cases & constraints early - Know when to trade-off time vs. space - Explain time & space complexity upfront - Know when to use BFS vs. DFS in graphs - Optimize only after correctness is ensured - Don’t overcomplicate: simple solutions win - Think out loud & show your thought process - Handle errors & unexpected inputs gracefully - Use stack for problems with nested structures - Understand system design even at junior levels - Recognize patterns (many problems are variations) - Cache results for repeated calculations (Memoization) - Understand when to use heaps & priority queues - Confidence matters(believe in your approach) - Master sliding window for subarray questions - Use binary search for optimization problems - Use modular functions for better readability - Know when recursion vs. iteration is better - Use meaningful variable & function names - Write clean, readable, and modular code - Divide & conquer for large problem sets - Refactor before finalizing your solution Understand the problem before coding - Use two pointers for array problems - Use hash maps for quick lookups - Know how to debug efficiently At the end of the day, coding interviews aren’t about memorization, they’re about structured thinking.  Which lesson do you wish you knew earlier?

After taking 75 Software Engineer interviews at Google in < 7 months, I’ve seen a range of mistakes all of us make in coding interviews. Here’s a compiled list to help you (and me) avoid these pitfalls in our future interviews! 1️⃣ Not Clarifying Requirements > Many candidates jump straight into coding. Often without fully understanding the problem. This can waste time and lead to errors. Tip: Always ask clarifying questions. To ensure you get the requirements. Confirm edge cases and input constraints early on. 2️⃣ Overcomplicating Solutions > In the heat of the moment, it is easy to overthink a problem. And this complicates the solution, both for you and your interviewer. Tip: Start with a brute-force approach (just explain it), then iterate towards optimization (code it up). Easy-to-understand solutions get bonus points. 3️⃣ Under-Communication > Interviews are not just about coding. They’re also about conveying your thought process. Silence takes away the only help you have during the interview—your interviewer. Tip: Think out loud! Explain your reasoning and approach as you code. This helps the interviewers understand you and even guide you if needed. 4️⃣ Ignoring Edge Cases > Many candidates create a working solution. But fail to consider edge cases. This can lead to catastrophic failures. Tip: After arriving at a solution, always discuss potential edge cases. Explain how your code handles them. This shows your thoroughness. 5️⃣ Neglecting to Optimize > Even if your solution works, failing to consider optimization can cost you points. Tip: After solving the problem, re-read your solution and discuss ways to improve time and space complexity. No micro-optimizations. Interviewers appreciate candidates who think about efficiency in big-oh notation. 6️⃣ Skipping Dry Runs > 80%+ candidates skip the dry run of their code, leading to overlooked mistakes. Tip: Walk through your code with sample inputs. This helps catch errors early and makes you look proactive. 7️⃣ Getting Flustered > Interviews are stressful. And it is easy to panic if you hit a roadblock. Tip: If you’re stuck, ask for a minute or 2 to gather your thoughts. Ask for hints if necessary—interviewers appreciate candidates who are willing to seek help. Those were my 2 cents on how to tackle coding interviews. But believe it or not, the best way to realize your interview mistakes would be to start taking interviews (even mock ones). After conducting so many interviews at Google, I realized how I often fell into the same traps as everyone. Like going completely silent or forgetting to do a dry run for the interviewer. Taking interviews altered my perspective, and now I advise everyone preparing for interviews to take a couple of them first. Total game changer! #codingInterviews #jobPrep #softwareEngineering #Google #interviewTips

10 pieces of advice that helped me get good at solving problems and cracking coding interviews, I wish someone had told me when I was 20 and grinding at BITS Pilani.... 1. How do I pick which problems to solve? - Start with easy problems in each topic (arrays, strings, etc.) - Use curated lists like “Top 150 Interview Questions” - Don’t waste time on random, obscure problems - Repeat the basics till they’re muscle memory 2. What to do when you’re stuck on a problem for hours? - Spend 30–60 minutes thinking hard - If still stuck, check hints or partial solutions, not full code - Try writing the solution from scratch after reading it - Make a note to revisit after a week 3. How to avoid feeling overwhelmed by 3000+ LeetCode problems? - Ignore the total count - Focus on a shortlist of 250–300 core problems - One topic at a time, one pattern at a time - Track your progress on a sheet 4. What’s the best way to learn a new data structure or algorithm? - Watch a trusted YouTube video (not 10!) - Implement it from scratch - Solve 3–5 related easy/medium problems - Teach it to a friend or write a summary note 5. How do I actually retain what I learn? - Make a habit of revisiting solved problems - Explain your solution out loud or to someone else - Maintain a notebook with your ‘aha’ moments - Practice old topics every 2 weeks 6. How to get faster at solving problems? - Set a timer: 15 mins for easy, 30 for medium, 1 hour for hard - Focus on pattern recognition, not brute force - Analyze time/space complexity for every problem - Practice under interview conditions (mock tests, contests) 7. What language should I use? - Stick to what you’re comfortable with (Python/Java/C++) - Learn built-in libraries for arrays, maps, queues, etc. - Don’t switch languages unless you’re stuck 8. How do I stop memorizing solutions and actually understand them? - Re-write solutions from memory after learning - Break down why each step works - Relate the approach to real-world problems - Never copy-paste, always type it out 9. How do I tackle hard problems or topics? - Break into smaller subproblems - Use pen & paper to draw out tricky inputs - Start with a brute-force solution, then optimize - Look for similar solved patterns 10. How to not lose motivation when it gets tough? - Celebrate small wins (accepted solution, a new pattern cracked) - Take breaks and walk away when you’re frustrated - Join a group or community for accountability - Remember: Growth compounds, it gets easier with time

I failed many coding interviews, but finally, 🚀 I’ve cracked them at Meta, Amazon, Netflix, Snap Inc., and Intuit. Here are lessons I wish I knew earlier 👇 🔹 Understand the problem first Don’t jump into coding. Clarify requirements, constraints, and edge cases. Sometimes it’s okay to defer handling complex edge cases and call them out as follow-ups. 🔹 Think in trade-offs, not just solutions If multiple approaches exist, explain each and discuss pros & cons. Talk about data structures and why you’d pick one over another. Recursive vs iterative: recursion may look elegant but comes with stack depth & memory costs. 🔹 Code quality matters Readable, modular code beats a clever one-liner. Use meaningful variable names (not just i/j). Add error handling when appropriate. 🔹 Testing mindset Don’t stop at “happy path.” Think about edge cases and how unit tests would catch real-world issues. 🔹 Communicate constantly Explain your thought process, decisions, and trade-offs as you go. This shows how you solve problems in real-world scenarios. 🔹 Stay calm under pressure Interviewers are testing problem-solving under pressure, not just correctness. 👉 For Senior/Staff+ SWE roles, interviews measure engineering judgment + clarity of thought more than speed. What’s one tip you wish you knew earlier in coding interviews? #InterviewTips #SoftwareEngineering #MAANG #EngineeringExcellence

You finish 100 LeetCode questions… And yet, when it’s time for the real interview — you freeze, you stumble, you feel unprepared. I’ve been there. Solving problems is only half the game. The real challenge? Solving them out loud — while thinking on your feet, explaining every thought clearly, and staying calm under pressure. Nobody really teaches us that part, right? Here’s what actually helped me (beyond just solving questions): 1. Mock Interviews Even the awkward, nerve-wracking ones taught me where I struggled. Practicing with friends, mentors, or even strangers online — it pushed me beyond my comfort zone. 2. Teaching What I Solved If I couldn’t explain my solution in simple words, I didn’t know it well enough. Explaining things to others forced me to really understand the logic. 3. Embracing Rejections I won’t lie — getting a “No” hurts. I’ve faced it too. But every rejection was a mirror. It showed me where I needed to grow — whether it was coding depth, system design, or just confidence. If you’re prepping now — don’t just count problems. Reflect. Speak your thoughts out loud. Simulate the real thing. It’s not just about cracking the interview — It’s about becoming a stronger problem solver. A better communicator. A more resilient you. That’s the real win. #InterviewPrep #SoftwareEngineering #TechInterviews #GrowthMindset #LevelUp #KeepGoing

💡 In coding interviews, the expectation is not flawless code. What interviewers really look for is clarity of thought, problem-solving approach, and communication. Think about it — no one expects you to craft a production-ready, fully optimized solution in just 45 minutes. Instead, they want to see: > How do you understand the problem? > How do you break it down into smaller steps? > How do you recover when things don’t go as planned? It’s perfectly fine if you pause to think, take a wrong turn, or even forget syntax. What matters is your ability to adapt, correct yourself, and keep moving with confidence. So, when you prepare, don’t aim for perfection. > Aim for structured thinking. > Aim for communicating your approach. > Aim for calmness under pressure. That’s what separates a good candidate from a great one. And honestly, that’s what real-world engineering is all about. Good luck to everyone on this journey 🚀 #CodingInterviews #InterviewPreparation #ProblemSolving #SoftwareEngineering #CareerGrowth #TechCareers #DSA #SystemDesign

🎯 400 LeetCode problems solved - but this isn't your typical "grinding problems" post. Like training a machine learning model, I approached algorithmic problem-solving with a focus on data quality and diversity. Just as ML models need varied, high-quality data points to generalize well, I found that solving diverse problems across different patterns and domains builds better problem-solving intuition. My systematic approach: Pre-coding Analysis (Link to sample doc in comments) • Document multiple potential approaches • Analyze time & space complexity for each approach • Think through tradeoffs before writing any code • Consider edge cases and constraints Practice Execution • Used stopwatch to measure performance • Aimed to solve while explaining clearly within: - Easy: 10 minutes - Medium: 15 minutes - Hard: 25 minutes • Focus on thinking aloud - crucial for interviews Deep Dive Process • Rigorous complexity analysis • Explore optimization opportunities • Document learnings and patterns • Regular mock interviews on Pramp The goal wasn't to solve all 3000+ problems, but to build a robust "model" that could generalize to new problems effectively. Each solved problem is like a new training data point, helping my brain recognize patterns and edge cases. Key learning: The magic happens in the pre-coding analysis. Writing down different approaches and analyzing tradeoffs before coding helped me: - Build stronger problem-solving intuition - Communicate my thought process clearly - Make better engineering decisions - Save time during actual coding I'll share a sample doc in the comments. It's been crucial for building a systematic approach to problem-solving. To those on this journey: Keep your head down, document your thinking, and remember - you're not just solving problems, you're building a framework for approaching any technical challenge.

The interviewer asked me to reverse a string in Python. I knew I had 30 seconds to either impress or join the rejection pile. That was interview 47th of my 90 interviews in those 90 days. Sadly, I bombed my first 15 Python interviews because I rushed to code without thinking. When I finally cracked the pattern, I went from getting ghosted to rejecting offers. I'm sharing this because watching talented data professionals fail interviews over basic mistakes makes me sad. Here's my exact 3-step system that helped more than 50 clients of mine to ace interviews. 𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Say the question back in simple words. Make sure you really get what they're asking. Ask questions if the input/output isn't clear. (Interviewers love this. It shows you don't rush to code.) Check edge cases: empty lists, big numbers, or weird inputs that could break your code. 𝟮. 𝗪𝗿𝗶𝘁𝗲 𝘁𝗵𝗲 𝗖𝗼𝗱𝗲 Think before you type. Plan out your approach (loops, if statements, functions, or libraries). Keep it clean and Pythonic. Use clear variable names, list comprehensions, and functions when needed. Use built-ins (sum(), set(), sorted(), zip(), etc.) before making your own. 𝟯. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗮𝗻𝗱 𝗥𝗲𝘃𝗶𝗲𝘄 Test with different inputs, including edge cases. Talk through debugging - it shows how you solve problems. Make it faster when possible. Can your solution run quicker or use less memory? Explain your choices. Why did you pick recursion vs. loops? As a result, when you nail Python interviews, you become the data professional everyone wants to hire. Your manager sees you differently. Your team respects your problem-solving skills. 𝗪𝗮𝗿𝗻𝗶𝗻𝗴: This system requires practice. If you're looking for a magic bullet, keep scrolling. Extra tips from my 150+ mock interviews: • Practice real problems: data structures, file handling, APIs, regex • Learn Python-specific stuff: list comprehensions, decorators, generators • Know your libraries: Pandas, NumPy, Scikit-learn, SQLAlchemy • Practice live coding (most people skip this and fail) 𝗡𝗲𝘅𝘁 𝘀𝘁𝗲𝗽: Connect with me, Jaret André and send me the word "INFO". I'll help you build the system that I used to go from bombing interviews to getting multiple offers. 𝗣.𝗦. I've coached 22 data professionals to ($100K+) offers in 2024 using this exact framework. The ones who practice live coding get offers 3x faster than those who just do LeetCode.