Tech Interview Preparation

I can tell in 5 minutes during an interview if someone understands data (based on how they describe past projects they worked on) Most data candidates that I've interviewed focus on the wrong details entirely during this interview stage and this is definitvely and area that is easy to prepare for. They spend 10 minutes walking through their tech stack: "We used Snowflake with dbt, Fivetran for ingestion, and Airflow for orchestration." But they never explain why those choices mattered, what trade-offs they considered, or what business problem they actually solved. The strongest candidates I interview do the opposite. They start with the business context: "Our sales team was making decisions based on 3-day-old data because our reporting pipeline took 72 hours to run." Then they explain their approach: "We had to balance speed versus cost, so we implemented incremental loading but kept full refreshes for critical accuracy checks." What I'm actually listening for: Do you understand the business impact of your technical decisions? Can you articulate trade-offs you made and why? Do you think about data quality, reliability, and maintainability, not just getting it to work? Can you explain complex systems in simple terms? The candidates who get hired don't have the most impressive projects - they have the clearest thinking about data problems. They talk about monitoring, error handling, and edge cases. They mention stakeholder feedback and iteration cycles. They understand that great data engineering is invisible to end users. Next time you're asked about a past project, spend less time on the tech stack and more time on the problems you solved, decisions you made, and lessons you learned. That's what reveals your actual engineering judgment. What's your experience with this? Share in the comments, follow for more insights on data engineering careers, and ♻️ repost if your network could benefit! #DataEngineering #Interviews #CareerAdvice #TechCareers #JobSearch

When people ask me, “How did you get into Google” ? — they often expect a shortcut or some secret trick. Here’s the truth: there is no shortcut. But there is a strategy. 💪 If you're preparing for big tech interviews (Google, Meta etc.), here’s what I’ve learned first-hand: ✅ 1. Master fundamentals, not just patterns. Instead of memorizing 100+ Leetcode solutions, deeply understand how and why data structures work (e.g., why a trie is used for prefix matching, why dynamic programming optimizes overlapping subproblems). ✅ 2. Solve problems consistently. Quality beats quantity. Solving 2 problems deeply every day > solving 10 problems quickly without understanding. ✅ 3. Think out loud. In interviews, your approach matters more than your final answer. Interviewers want to know how you think, debug, and improve. ✅ 4. Mock interviews are game-changers. Simulate the real interview environment with friends or mentors. You’ll build confidence and identify blind spots. ✅ 5. Embrace feedback and failure. I’ve faced rejections too. Instead of feeling defeated, I treated each one as a free lesson to level up. --- Today, as a Software Engineer at Google, I still use these principles daily — solving real-world problems at scale. ✨ To anyone preparing: You don’t have to be a genius. You just have to keep showing up, learning, and believing in yourself. If you'd like, I can share a detailed roadmap or my personal prep strategy in a future post — just comment “Interested” below! ⬇️ For 1:1 conversations please connect here: https://lnkd.in/ga_5bi57 #Google #SoftwareEngineering #InterviewPreparation #DSA #WomenInTech #CareerAdvice

REEL vs REAL : Data Analyst In REELs and online posts, it looks like: ✔️ Learn SQL ✔️ Learn Python ✔️ Master Excel ✔️ Create dashboards in Power BI / Tableau …and you're set to land your first job! But in REAL life: Project requirements change. Tech stacks are different in every company. Suddenly it’s not just about SQL and Python - it’s also Snowflake, Databricks, AWS, Airflow, Git, scripting, and whatever new tool the team uses. Sometimes it’s internal tools nobody outside the company even knows about. And no matter how many courses you finish, real-world problems will always throw something new your way. The expectation isn’t that you know everything from day one. It’s that you stay curious enough to figure things out. Foundations like SQL, Python, Excel, and Power BI are important - they give you the confidence to start. But building a real career in data goes way beyond ticking off a list of skills. It's about how quickly you can adapt when a tool you’ve never heard of becomes critical to your project. It’s about staying calm when you don’t have all the answers, Googling like a pro, asking good questions, and learning from every messy situation. In real-world data teams, things rarely go by the book. New tech keeps coming in, project needs evolve, and every organization has its own way of doing things. The people who thrive aren’t the ones who knew everything beforehand - they’re the ones who learned how to learn, again and again. ♻️ Repost : If you found this helpful, to reach others who might need it. ✳️ Follow Mariya Joseph for more daily content!

As a hiring Team Lead at Google, I’ve sat on hundreds of interviews. I can tell you the two biggest signals we look for that are 𝗡𝗘𝗩𝗘𝗥 mentioned in the official prep docs. Candidates often ace the coding challenge but still get a "No Hire" recommendation because they miss these two subtle, high-leverage signals: 1️⃣ 𝗧𝗵𝗲 "𝗧-𝗦𝗵𝗶𝗿𝘁 𝗦𝗶𝘇𝗶𝗻𝗴" 𝗦𝗶𝗴𝗻𝗮𝗹 (𝗘𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗼𝗻 𝗦𝗸𝗶𝗹𝗹): We aren't just looking for the right algorithm; we want to see how you handle 𝑎𝑚𝑏𝑖𝑔𝑢𝑖𝑡𝑦. When we ask, "How would you design X?" we listen for the ability to break the problem into clear, quantifiable phases (like T-shirt sizes: S, M, L, XL). A great candidate can estimate complexity, identify bottlenecks early, and justify trade-offs 𝑏𝑒𝑓𝑜𝑟𝑒 coding. 2️⃣ 𝗧𝗵𝗲 "𝗣𝗼𝘀𝘁-𝗠𝗼𝗿𝘁𝗲𝗺 𝗠𝗶𝗻𝗱𝘀𝗲𝘁" 𝗦𝗶𝗴𝗻𝗮𝗹 (𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆): If you hit a roadblock or make a mistake during the interview, we look closely at your 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆 𝘀𝗽𝗲𝗲𝗱. Do you panic and shut down? Or do you treat the error like a post-mortem: immediately identifying the root cause, documenting the flaw, and proposing a fix? This shows us you're a stable operator under pressure—the most valuable trait in production. If you are interviewing for any senior role(team lead, staff +), spend less time memorizing LeetCode patterns and more time practicing 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗴𝗿𝗮𝗰𝗲𝗳𝘂𝗹 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆. #hiring #CareerAdvice #SoftwareEngineering #TechInterviews #Recruiting

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

I applied to 47 QA jobs in 2 months. Got 8 interview calls. Bombed 7 of them. Not because I didn't know testing. Because I didn't know how to talk about it. Interview 3. They asked me to describe my testing process. I said: "First I write test cases. Then I execute them. Then I log bugs in Jira." The interviewer nodded. Wrote something down. Moved on. I thought I nailed it. Rejection email came 2 days later. Interview 8. Same question. Different company. This time I said: "I start by understanding what the user is trying to do. Then I ask what could go wrong. Last project, I was testing a payment page. I didn't just check if the Pay button worked. I tested what happens when someone clicks Pay twice. When their card declines mid-transaction. When they switch tabs during processing." "Found 3 critical bugs before release. One would've let users get charged twice." Offer letter arrived in 48 hours. Here's what I finally understood: Interviewers don't care about your process. They care about your thinking. They don't want to hear "I write test cases." They want to know how you decide what to test. They don't want "I found bugs." They want to know which bugs matter and why. Even if you're a fresher, you can show this: Talk about apps you use daily. "I tested the forgot password flow like I was analyzing why Zomato makes you verify OTP twice." Use your practice projects as real examples. "When I tested a login page, I didn't just check valid credentials. I tested empty fields, SQL injection attempts, session timeout." Turn every answer into a mini case study. Not "exploratory testing is useful." Instead "I once explored an edit profile feature and found you could upload a 50MB image that crashed the app." The testers who get hired aren't the ones who memorized ISTQB definitions. They're the ones who sound like they've actually broken something. What's one interview question you answered better the second time around? #SoftwareTesting #QAJobs #InterviewTips

Most students approach data science interview prep still like it’s 2021. Brush up Leetcode. Memorize stats & ML theory. Skim a few project slides with visuals. But technical interviews are evolving and so should your prep. In the age of AI, hiring managers are no longer just asking: → “Can you code?” → “Do you know XGBoost?” → “What’s the difference between precision and recall?” They’re asking: → “Can you adapt to new tools quickly?” → “Can you apply statistical thinking to ambiguous business problems?” → “How would you audit the output of an LLM?” → “What processes would you automate and which would you leave manual?” And they want to see more than just clean code. They want: → End-to-end thinking → Business understanding → Opinions on what should be built and not just what can be If you're prepping for interviews today, here’s what I’d focus on: → Know your fundamentals; especially programming logic, stats, SQL, and model development and deployment → Build projects that reflect real business use cases → Practice explaining tradeoffs, assumptions, and limitations → Stay current on how AI tools are changing workflows → Get comfortable thinking like a product owner, not just a data analyzer Because in this new landscape, interviewers are looking for those who know how to make data (and AI) actually useful. #datascience #techinterview #ai #careerstrategy #machinelearning #interviewprep #realworldskills #earlycareer #productthinking

How do you explain your past projects?   I have always found a consistent pattern of struggle in this question. Most of the struggle is not having a structure to present and as a result, the rambling and long winded answers.   Here is an easy framework that you can use and practice if you want to give an impactful reply that showcases your real skill set:   IPR-CTO Framework:   1. Intro (I): Go top down. First give a brief of the product then the particular project you worked upon. 👋 2. Problem (P): Here you describe the feature requirement or pain point that you worked upon. 🐞 3. Role (R): Here you describe what was YOUR role in this project. e.g. front end or back end or full stack engineer or architect or tech lead or manager. 4. Contribution ( C): Here you describe what was YOUR exact contribution to this project. e.g. I wrote a design document, implemented backend APIs and unit tests using Python and Flask. Here you can ask a clarifying question to the interviewer – let me know if you’d like me to dive deep into any particular area. Also, take a pause here to ask if the interviewer has any question(s).   5. Timeline (T): Here you describe how long the project took to complete. ⏳   6. Outcome (O): Here you describe any small or big wins as a result of the delivery of this project.   In the end, you can also share your learnings from the project, as a matter of fact, I’d encourage you to share your learnings even if not asked.     #interview #softwareengineers #interviewprep #interviewskills #jobs #interviewpreparation #teaching #dataanalyst #dataengineer #datascientist

I interviewed over 300 software engineers during my time at Turing, and here's the biggest mistake most of them made in the interview process: A complete lack of understanding of what the hiring manager needed. A business is not hiring for a JavaScript developer to increase their headcount. No. They have a pain, a business need, and they need someone to fix that business need. So don't do this in an interview: - I code with React - I code with JavaScript - I can write Go Do this instead: - Figure out exactly what problem the hiring manager is trying to solve, and who exactly they are looking for to solve that problem. Once you do, your entire manner of communication during the interview process changes. You begin to focus on their pain points. Example: Why are they looking for a backend engineer? From analysing the job description and learning a lot about the company, you learn that they are having a really hard time scaling their PostGRES database. So in the interview, your language changes to: - I helped Company X scale their database from 5,000 queries per minute to 25,000 queries per second. - I helped Company Y migrate their 10 TB database from Amazon to a dedicated server, saving them $90,000 a year. You're not in the business of learning technologies. You're in the business of solving problems.