Data Analyst interviews are changing with AI (and you better be ready!) | by Amney Mounir | Jun, 2026 | MediumSitemapOpen in appSign up<br>Sign in

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Data Analyst interviews are changing with AI (and you better be ready!)

Amney Mounir

3 min read·<br>1 hour ago

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A friend reached out to me last week.<br>He had a Data Analyst interview coming up, and the recruiter told him something he’d never heard before: “You’ll have access to AI during the SQL round.”<br>His first reaction was relief. “Oh nice, this is going to be easy.”<br>His second reaction, about ten minutes later, was “oh no! how can I differentiate myself?”. Because if everyone has AI, then writing the query isn’t the test anymore.<br>So what is? That’s the question.<br>So let me break down what’s actually changing, because this will matter a lot for your upcoming interviews.<br>SQL is now a conversation with AI, not a memory test<br>Press enter or click to view image in full size

For years, the SQL round was about one thing. Can you write a correct query from scratch, under pressure, without Googling (remembering all these functions that you always need to Google or ask ChatGPT for)<br>That era is ending.<br>The new SQL round looks more like this. The AI writes the first draft. Your job is to read it, understand exactly what it’s doing, explain it out loud, and then catch the part that’s wrong (if any!)<br>And one thing isn’t changing. Don’t rush into writing the prompt. Ask the interviewer if that’s actually what they’re looking for first. Never start writing before you’re sure you understand the request. This could be a BIG red flag!<br>The people who understand SQL deeply will continue having a massive advantage, because they can babysit the AI, explain the output and make adjustments (instead of blindly trusting it).<br>The weight is shifting to Product and Business Sense<br>When the technical part of the job gets faster, the human part gets more weight. The interviewer will make more emphasis on how you solve ambiguous problem and how creative you can be coming up with feature ideas.<br>You’d get more questions like “engagement dropped 5% last week, what do you do.” We want to improve X, what are some ideas you have in mind? And the AI is not doing a great job at it (right now!) so this is YOUR advantage.<br>So if you’ve been spending 90% of your prep on SQL, change that ASAP.<br>Spend more time on:<br>Investigation type questions<br>Product Ideation<br>Success Metrics<br>Storytelling will also matter a lot with AI<br>Last one, and it’s the one people underestimate the most.<br>Being able to talk through a project you led end to end. Projects you’re actually proud of. How you handled the uncertainty, how you prioritized when you had to deal with a tight deadline. That’s the stuff that you need to prepare for as well.<br>I recommend practicing 2–3 stories for each scenario. I also recommend using the STAR method (Situation, Task, Action, Result) to give each story a clean structure, and practicing them out loud until they feel natural. You can record yourself and ask AI for feedback or just practice with a friend.<br>So here’s what I’d do if I were prepping today<br>If I were you, I will still practice SQL (but not as the main thing)<br>Put most of your time into Product sense, business sense, and talking about your achievements.<br>If you want to practice this the right way, with company-specific questions and full interview guides built for how interviews actually work in 2026, Dataford is a good place to do it.<br>Check out their interview guides →<br>Hope this was helpful!

AI

Sql

Data Analysis

Written by Amney Mounir<br>2 followers<br>·5 following

Product Growth @Meta , passionate about generating growth and making an impact in challenging markets

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