187: Would You Survive This Data Analyst Interview?

Real-World Data Analyst Interview Prep

[00:00:00]

Avery: Would you survive a real world data analyst interview today?

Let's find out.

I'm gonna hit you with three realistic questions that you need to absolutely master for the data analyst interview.

For each question, I want you to pause the video and say your answer out loud. Then press play,

and I'll go ahead and break down what a great answer looks like and why it works after.

By the way, this mock interview is brought to you by Replit, the tool I personally use to build software quickly, but more on them later.

Question 1

Avery: Ready to go? Let's go ahead and dive into the interview.

Avery: All right. Thanks for interviewing with us today. Can you go ahead and start off by telling us a little bit about yourself?

So go ahead and press pause on the episode and try to answer.

Okay. Did you give your answer? Let's break down what a good answer and a bad answer would look like.

A bad answer might sound like this.

Well, my name's Avery Smith and I'm applying for your data analyst job. Um, I was born in Utah in 1995. Uh, I worked as a product manager at [00:01:00] IBM and then I left there to go be a product special quality analyst, uh, for Google. Uh, and then I am now applying for your job because, uh, it's time to, to move on.

Yeah, basically it's like way too vague, uh, not relevant enough, not specific enough, not very interesting. , And so instead you wanna try to give enough details, but you're just kind of connecting the dots of your career and showing it why it's relevant for the hiring manager or the interviewer. Like, why should they be interested in you specifically?

A good answer might look something like this.

Well, yes, my name's Avery Smith. I majored in chemical engineering and I have a master's degree in data analytics. I used to be a chemical lab technician and I absolutely fell in love with crunching numbers, uh, while I was a chemical lab technician.

And ultimately I actually pivoted internally to become a data scientist. Of that small biotech startup. After that, I wanted to try solving, uh, some energy problems. And so I went to ExxonMobil where I did data science for them, mostly machine learning and Python, to try to predict, uh, what [00:02:00] oil we should buy around the world based off current prices and properties of the oil, as well as what the demands in different parts of the world for gasoline might be.

So, uh, hopefully I was able to save them lots of money with those machine learning models. And then finally, I now. You know, run my own business today where I teach people data analytics and make YouTube videos, and I'm applying for this job because I really want to have a bigger impact. I want to solve more problems with data.

Uh, I really believe in the company's mission and, uh, I'm excited to be here.

Now that answer wasn't necessarily perfect, but you get the idea. You want to be specific. You wanna connect the dots of your career, and you want to tell them that you have the relevant skills that the job description is looking for.

Now, I know this is a basic question, but you're going to be asked that in a hundred percent of your interviews, and fortunately for us, it's one of these questions that actually get easier with practice . And later in this episode, I'll actually share my favorite way to practice these types of questions. Once again, all thanks to our sponsor, Replit.

Question 2

Avery: All right. Our next question is actually going to be a technical one. Specifically, it's going to be [00:03:00] sql. So an interviewer needs to know that you can actually handle the technical side of a job. So they might ask a sequel question like this.

Avery: So what's the difference between a WHERE clause and a having clause in sql? And when would you use one over the other?

So go ahead, hit pause, practice your own answer out loud. All right. Here's how I'd personally answer this one. I would say,

well a WHERE clause and a having clause are somewhat similar because they're both used to filter results in sql.

The difference is basically when those filterings happen, are you filtering before an aggregation or after an aggregation a WHERE clause is where you filter data in SQL before an aggregation happens.

This means initially you're filtering out your database by some sort of conditional statement. A having clause does that filtering with the results of an aggregation. So there's always going to be a GROUP BY when you're using the having clause and that filterings not happening and before the GROUP BY, it's happening [00:04:00] after the GROUP BY.

So you're filtering the results of the aggregation. And then I would try to show them a time that I've used it in the past. So for example, when I was at ExxonMobil, I might have had a database where each row represents a different oil and its current price. I could use a a WHERE clause to only return the rows where the price was above a hundred dollars per barrel.

Let's say I had a country column as well. I could group the data by the country and that would use a GROUP BYing sql. And now I'm looking at the aggregate results. So like for example, maybe I'm finding the average price per barrel of different oils from Saudi Arabia or from the us. If I then wanted to filter the aggregate results, so like the countries that have a barrel per oil average above a hundred dollars, that's when I'd use a having clause.

So obviously you wanna make sure you get this right, but you'll get bonus points if you're able to point back when you've used it in the past with a realistic example.

Question 3

Avery: All right. Let's move on to question three. And question three is going to be more of a show your thinking type question. So here we go.

[00:05:00]

Avery: As you know, we're a software company and let's say that we noticed our user signups dropped by 20% yesterday. Walk me through how you'd investigate this.

Pause the video, think about what you're gonna answer. Try to answer. Okay.

This type of question is basically show me how you think and so there's kind of two main ways to answer this. One would be like through the star method. This is a behavioral question of like, Hey. If this pops up in the future, how would you handle it? And one really good way is to talk about how you've handled it in the past.

So you say the situation that you were in, the task you were given, the action you took, and the result that came from it. If you've never experienced something like this before, you're just gonna literally. Talk out loud what's going on in your mind and just kinda speak your, uh, consciousness they're basically like, Hey, show me how you think.

And so just show them how you think. So I would say something like this,

well, I'd first ask you where you saw the signups dropped up by 20%. Was it a specific chart? Was it a dashboard? Did you get a notification alert or an email or something like that? And [00:06:00] that's where I'd personally start. So let's just say for, for kicks and giggles you saw on your like Tableau dashboard, the first thing I wanna do is make sure is this 20% drop actually for real?

So I'd go into the Tableau dashboard and just double check to make sure that the, my numbers say the exact same thing. I also might try to create some sort of a data visualization showing how this number's changed over time. 'cause right now we just have like a KPI like a key performing indicator here.

Just one big metric, 20% down. But how does that actually look like? I'd make a line chart probably to show that, and I'd want to be able to see is it a gradual decrease or is it a sudden drop off? Because that would tell me where to maybe look based off of the answer.

Let's say it was a sudden drop off. I'd want to double check how Tableau's getting this data and what changes are done inside of Tableau and which are done more in the database. So I'd want to go inside of the database and probably do some sort of a SQL query to confirm this drop off.

Maybe I do some sort of a window function or something like that to [00:07:00] basically show the number of signups per time.

If in fact we are really seeing a 20% drop off, I would then explore probably why that's happening. I'd stay in SQL and try to figure out if that 20% that drop off is happening uniformly across all of our platforms or just like one specific demographic. So I might look at different parts in the world, like maybe US versus Europe.

Is there any differences there? I'd check maybe like the page traffic that we're doing through ads versus organic traffic. Is there any differences there?

After that, I'd obviously want to try to go through the process myself of signing up as a user and just see if anything weird's popping up and actually experiencing it at from a user's perspective, not only from a data perspective. I think these steps would ultimately leave me to figure out why that 20% drop off happened, and then be able to report to you what we can do to actually fix it.

All right. Maybe not the best answer on planet Earth, but basically just speak your mind and tell them exactly what you do. Obviously, you want us try to stay analytical and just give your thoughts on how you'd actually solve this problem.

Now, I'm curious, how did [00:08:00] that feel? Let me know in the comments if those questions were hard.

Let me know which one you thought was the hardest, or maybe share a tough question that you've gotten in an interview recently.

How to Practice Interview Questions

Avery: If it felt pretty difficult and pretty awkward, that's totally okay. And that just means you need to practice. That's the key about interviewing practice makes perfect.

And that's actually the exact reason I built a mock interview platform where you can practice these exact questions and a bunch more with me. So here's how it works.

Step number one, you select a question. Step two, I ask you the question just like I did today. Step three, you respond with audio or video. And then step four, we grade your answer using interview best practices.

And then step five, we show you how to properly answer the question from an expert, usually me.

Now this platform, it's called Interview Simulator, and you can try it for absolutely free@interviewsimulator.io. And the more you practice, the more your future self will. Thank you.

And I actually built this platform entirely from scratch using today's sponsor, Replit, to help me program.

Lemme tell you a [00:09:00] quick story. When I was first breaking into data,

I seriously could not land an interview to save my life.

Finally, after like a hundred applications, one company took a chance on me and invited me to an interview.

And you guys, I can't tell you how freaking nervous I was for this interview 'cause it felt like my whole career was literally depending on it.

And like I said earlier, when it comes to interviews, practice makes perfect. And so I actually forced my wife to have these flashcards with different questions on that and to quiz me these different questions that they might ask in this data interview.

I would practice my answer back to her.

And I was like, man, this is great. I love practicing, but my wife doesn't really know if my answers are good or not, and I wish I could be doing this, uh, not with her and with like some sort of an expert. And that's literally where I got the idea for interview simulator.

So for years I've been trying to build this and it hasn't gone super well. I've used like a lot of no code tools to try to duct tape it all together and I've honestly spent hundreds of hours and thousands of dollars trying to bring my idea to life. And we had some success.

Like I won't say it's been a total failure. We've had tools that have been working, but [00:10:00] it's always felt a little bit clunky and like it's barely hanging together.

That's until I built the entire mock interview platform using Replit because I was no longer like reliant on these, like shaky, no code tools that are duct taped together and these crazy

workarounds, I could literally just log into Replit, tell it exactly what I wanted and have an MVP in a few days.

And so I've done that. I've rebuilt interview simulator for all of you guys to try and I'm really proud of it and I think it's gonna be awesome and it's ready for you guys to beta test.

So you can head to interview simulator.io and try it for free today,

and there will always be free questions on Interview Simulator for you to practice always.

But thanks to Replit for sponsoring this episode, I've actually decided to unlock all of the questions.

Literally a hundred percent of the questions are going to be free until January 1st, 2026.

shout out to Replit for helping you guys aspiring data professionals practice your interview questions. That's awesome.

So what are you waiting for? Go to interview simulator.io and try a few questions.

And if you've ever wanted to build your own app tool website, you need to test out [00:11:00] Replit.

It's literally the software I use every day to bring my software ideas to life. Links the description down below.

I'll see you guys in the next episode.