Preparing for Tech Interviews with Limited Experience

During a recent mock interview with someone preparing for a data engineer role, we ran into a super common challenge job seekers face: The job description mentioned machine learning and stats, but my mentee only had basic experience in those areas. And with a big presentation coming up, we had to figure out the best way to prepare without wasting time or getting overwhelmed. Here’s what we did: 1) Focus where it matters most I told them: Imagine it’s a test. 90% of the questions are on things you know well, 10% on stuff you don’t. Where would you spend your time? Obviously, on the 90%, that’s where you can really shine. 2) Play to your strengths Instead of cramming complex ML topics overnight, we doubled down on what they already knew: cleaning data, building solid pipelines, writing scalable code—all stuff that directly applies to the job. Then we built a strong presentation around that. 3) Address the gaps without pretending We practiced how to talk about the ML/stats side in a smart way: not by faking expertise, but by showing how their data engineering skills help data scientists succeed. It’s not about having every skill; it’s about showing how you add value to the team. 4) Be real, but show you're a problem solver We also talked about how to confidently say: “I don’t know X yet, but here’s how I’d figure it out or approach it with the tools I do have.” This shows honesty and the mindset every team wants: someone who takes ownership and knows how to solve problems. The result? They walked into that interview feeling confident and clear on how to present themselves. Key reminder: You don’t need to know everything. You just need to show how what you do know makes you a great fit. So here's a question for you: If you have ever faced a situation where you were underqualified in one area, tell us in the comments how you used your strengths to stand out. ➕ Follow Jaret André for daily data job search tips 🔔 Hit the bell icon so you never miss a post

Our client pivoted from Sales to Data Analytics. They did it with no formal data experience. Here are 6 strategies they used to make it happen: Context: When our client reached out, they were stuck. They had spent months applying to data analyst roles with no success, despite completing a data analytics course. They had even received a verbal offer that was later rescinded. Frustration was building, and they were considering a return to account management. We teamed up with them, and things started to change: 1. They Clarified Their Target Role Before working with us, their approach was to just apply to any and every data analytics role that popped up. We helped shift that mindset to focus more of our energy on a smaller set of highly-aligned companies. They used this clarity to create a “Match Score” for each opportunity—filtering out roles that didn’t align with their ideal job. 2. They Optimized Their LinkedIn For What Employers Wanted To See Before joining, they weren’t getting any outreach for roles on LinkedIn. We revamped their LinkedIn headline and profile to include keywords specific to the Data Analytics space as well as projects that illustrated their capabilities. Then the inbound messages began to roll in. 3. They Shifted Their Time From Online Apps To Networking Instead of just applying online, they reached out to alumni from an analytics bootcamp they attended. They specifically focused on people who had successfully transitioned into data roles. One alum gave them insider insights into the hiring process at a target company and even suggested key skills to emphasize their application. 4. They Built A Consistent Outreach System They started sending 5 personalized LinkedIn messages per day to data professionals. They focused on asking for advice, then taking action on it to open the door for a follow-up. This helped build rapport and trust, which led to multiple referrals and interviews. 5. They Went Deep On Interview Prep They knew that other candidates would likely have more “traditional” experience to lean on, so they went deep on interview prep. For technical interviews, they built a portfolio project analyzing Airbnb data to showcase SQL skills. For behavioral interviews, they prepared answer examples that tied directly into the company’s biggest needs. 6. They Stayed Persistent & Flexible Originally, the recruiter who reached out was asking about a business analyst role. After pitching their SQL and Python skills, our client convinced the recruiter to get them in the door for a data analytics position. Then they used their networking to gain insider info on goals and challenges which they pitched in their interview. That approach secured the offer. 🔄 Need help making your career change a reality? We help our clients change careers without experience, without taking a pay cut, and without settling. 👉 Book a free call and we'll show you the system: https://lnkd.in/gdysHr-r

This is part 3 of how I spent 8 weeks job searching and landed several interviews and a new data role. I’m sharing exactly what I did in case even one part of it helps someone else. 𝗣𝗮𝗿𝘁 𝟯/𝟯: 𝗛𝗼𝘄 𝗜 𝗣𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗙𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 Interviewing is arguably the most stressful part of the job search (at least for me). I’m no interview expert - I get nervous, I ramble, and sometimes lose my train of thought, which is why preparation was everything. Here’s what helped me feel more confident going in: 📝 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗽𝗿𝗲𝗽: • I picked the most impactful project from each of my roles and wrote it out in the STAR format (Situation, Task, Action, Result). This gave me a baseline story I could reuse across different questions. • I made a list of common behavioral questions (“Tell me about yourself,” “A time I drove business results,” “Why this company?”) and wrote bullet-point responses so I wouldn’t blank. • I asked ChatGPT to generate behavioral interview questions based on the job description, plus tips on how to answer. It gave me specific practice questions that I wouldn’t have thought of myself. • I typed out my answers, then practiced saying them out loud so I wouldn’t stumble as much in the moment. • I also found Madeline Mann's content very insightful! She shares how to interview better and why you may not be getting selected. Definitely worth a follow! 📊 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗽𝗿𝗲𝗽: • I reached out to the recruiter/interviewer when possible and asked for specific topics that would show up on the technical interview. • I asked ChatGPT to generate technical interview questions based on the job description, plus an answer key. • I practiced SQL challenges daily, and my favorite resource was Data Lemur by Nick Singh 📕🐒 🖊️ 𝗙𝗼𝗹𝗹𝗼𝘄-𝘂𝗽: • I always followed up after each interview with a short note to thank them for their time and to express my interest in the role. It’s simple, but it goes a long way in leaving a positive impression. All this didn’t make me a flawless interviewer, but it gave me structure, confidence, and the space to be myself. If you found this series valuable, connect with me! I’ll continue sharing my journey through data and would love to connect with others along the way! 🚀 #DataAnalytics #JobSearch #CareerAdvice #InterviewTips