108: Data Analyst Mock Interview

[00:00:00] If you're just trying to see how the heck do you think? Like what is your thought process?

[00:00:03] I love that you like pulled out a calculator and I love the beginning how you like asked for clarity.

[00:00:07] I think that's really important. Welcome to the Data Career Podcast, the podcast that helps

[00:00:12] aspiring data professionals land their next data job. Here's your host Avery Smith. In this

[00:00:17] episode, you're going to see me interview two random strangers and ask them data analyst

[00:00:22] interview questions in hopes of preparing them for their upcoming interviews. If you guys

[00:00:26] like this episode, you are going to love this tool called interview simulator. I recently released

[00:00:31] it interview simulator.io that's interview simulator.io. It's called interview simulator because it

[00:00:37] simulates an interview where you basically love this exact same scenario with me where I ask

[00:00:41] you a question, you respond with video or audio how you get to see how I would answer the question

[00:00:46] and then we also have an AI interview wizard that will give you critique it'll score your

[00:00:50] answer on one to 10 and also give you a list of pros things that you did well and some areas

[00:00:55] where you could maybe improve. So if you guys want to check that out interviewsimulator.io let's

[00:01:00] get into this episode. You prepared for an interview question? Yeah, I'm in a graduate school and I'm

[00:01:06] just looking for kind of like internship and a full-time job within a data analytics side. So

[00:01:11] I think this is good chance to practice. Well first off congrats on grad school and

[00:01:16] for being here and being brave to do this the questions that we're going to be pulling from

[00:01:20] today are going to be straight from interview simulator. So you can answer them today and then

[00:01:26] you can always go back and practice them here. We'll start off with a behavioral question

[00:01:31] and then we'll move to a technical question. So let's go ahead and hop into it Richard. Okay,

[00:01:38] so we're gonna start off pretty simple and because I don't know anything about you

[00:01:42] we're gonna start off with the question that's probably going to be asked in every interview

[00:01:46] and that is tell me about yourself. So Richard, tell me about yourself.

[00:01:51] Yeah sure yeah my name is Richard and I'm graduate school from the U of U MSBA program. I'm on the

[00:01:59] second semester and I'm very interested in to utilizing the data and AI in my field. For

[00:02:07] my work experience I will work for the data analysis data engineer and BI engineer for three

[00:02:13] years and during that time I will utilize over 10 products and built it for the data pipeline for

[00:02:21] the various companies including ATAC, PropTac and the financial Vintag area. That was a great answer

[00:02:29] and it's always scary giving those answers always especially on a live call like this so

[00:02:35] that was a great answer. I like to kind of identified what you're currently doing right

[00:02:40] you said you're a graduate student that's always key to make sure that like they know what

[00:02:43] you're actually doing right now but I also like that you said what you've done in the past so yeah

[00:02:48] you said you had been working like as a data analyst for three years and it seemed like you

[00:02:53] had done it in a couple different industries. I didn't catch any of the company names so what

[00:02:57] companies did you work for? Yeah I work for the BIU pathway and the other is sorry I forgot

[00:03:05] the names of the the PropTac companies it was our located in Arizona. Okay cool one of the other

[00:03:12] things I thought it sound like at those companies that you'd worked for you have like you developed

[00:03:17] some experience and you said you would use multiple tools what were some of those tools?

[00:03:21] I would like to know in that answer like what tools so what tools did you did you use? Yeah

[00:03:26] for the dashboard building I've used a Tableau Power BI and the quick side and for the data

[00:03:32] pipeline I most likely used the AWS stack which is the glue and the Athena for the data

[00:03:39] wrinkling and the cleansing. Oh see that's super awesome and very impressive yeah so that I think

[00:03:46] that would be my my critique is mentioning the companies and the tools by name because like AWS

[00:03:51] is no joke it is not the easiest tool stack to use so that's like oh I'm already thinking

[00:03:55] Richard knows knows some pretty good stuff so that's great I think that was really important

[00:03:59] to add those things to your answer but I thought I thought you did a fairly good job.

[00:04:04] One of the cool things is you can go back and go to interview simulator.io

[00:04:08] and practice the question here you know with either video or with audio and it does a pretty

[00:04:14] good job you'll get a response from our interview wizard that basically looks at your answer

[00:04:20] analyzes it gives it a score one out of ten and lists the pros and the cons as well and then

[00:04:26] also lets you watch the replay as well because even when you were answering the question me as a human

[00:04:31] I was like trying to remember every single thing that you said that's the nice thing about the interview

[00:04:35] is it's not human so it doesn't have to try to remember so anyways I think your answer I'd probably

[00:04:40] get your answer probably around a seven or an eight out of ten I think you did a good job

[00:04:46] identifying of what you've done you maybe feel confident that you I was like oh Richard does have

[00:04:50] you know experience in the past and I think to make it like a nine or a 10 out of 10

[00:04:55] adding a little bit more specificity around the tech stack and the tools would probably be

[00:05:02] the key there does that make sense Richard yeah it makes sense thank you okay cool you

[00:05:08] got to do a technical question yeah I'm ready to do that I think okay let's try a technical

[00:05:12] question I'll actually let you choose out of these questions you see over here on the screen

[00:05:18] which technical question because technical questions are a little bit scary in my

[00:05:21] opinion so unless you choose since you've been brave which technical question would you like to

[00:05:25] answer today I think the sequel window function from amazon would be interesting for me okay let's

[00:05:31] go ahead and do it so I'll pull this up once again I'm not going to play the video of me

[00:05:37] asking the question like you normally would in an interview simulator because I'm here live with

[00:05:41] you right obviously so it's probably not necessary but basically in this question

[00:05:46] which is taken from in amazon I think this is actually taken from a business intelligence

[00:05:50] engineer position at amazon and let's go ahead and I'll ask you that question the question is

[00:05:57] what is a sequel window function and when would you use one so I'll open it up to you Richard go

[00:06:02] ahead yeah for the window function I think it is very similar for the group by or aggregated

[00:06:09] function but you can do without just aggregating for that one or or transforming the original

[00:06:17] or low data set so for example like if you want to partitions by one of the data or each of the

[00:06:25] users for example then you can just like doing group by and just like aggregating some of the

[00:06:32] numbers or counting the numbers for examples but window functions allow you to just like

[00:06:39] without group by you are just like maintaining some of the original data set and you are just

[00:06:45] like adding some of the aggregated numbers onto the data set so like for example count of the how many

[00:06:54] users are existing per each of the men's or women then if you get to being seven in total

[00:07:01] then you will get the number of each low as a total number of seven by using the window function

[00:07:07] so wait clap it up for for Richard you guys that was a great answer and he did a good

[00:07:13] job that is not an easy question the sequel window function at all Richard I think you did a great job

[00:07:20] on this question I think I loved the thing I really once again so just just to kind of highlight

[00:07:25] you can always go back to interview simulator and ask you know go through the exact same question

[00:07:30] again and you would actually be able to watch your answer back you would actually get to see me

[00:07:36] answer the question and then of course you'd get your feedback from the interview with an

[00:07:40] interview simulator as well if I had to guess what interview is would say about that question

[00:07:45] I honestly think that was probably an eight or a nine out of ten you pretty much nailed everything

[00:07:50] so things I'm looking for when when I ask this question is yeah saying it's like a group by

[00:07:56] without aggregations I think is probably the easiest way to describe it like in simple terms

[00:08:01] I think that's really good and then you said for example twice which I think is always good

[00:08:06] trying to give like a tangible example of when it's useful so I think that's really good and

[00:08:11] it's like way easier to show like a table of it being used versus explaining it via word so I

[00:08:16] think you did a good job with that my only critique would probably be to talk I mean you did kind of

[00:08:21] talk about it with that girls and boys example I guess I was kind of looking for like some some

[00:08:27] common use cases like some of the and you kind of gave it I think you were what you were

[00:08:31] what you were talking about the boys and girls was like a running count I was looking just for

[00:08:35] that word like a running sum or like a running count or like a running a rolling average or something

[00:08:40] like that so I think that that would have the only thing that could have made it a little bit better

[00:08:44] but overall I think that was a pretty good answer to a pretty tough sequel question what do you think

[00:08:47] does that make sense yeah I think it'll be better to if I just give the length example so that

[00:08:53] like people can easily understand like how the window function can be applied to just make the

[00:08:59] length of each of the loss yeah 100% but overall I feel like that was pretty solid and I think you

[00:09:05] did a great job everyone clap it up for Richard that is not easy to do and come up here on this stage

[00:09:12] speaking of which we have someone new to the stage joey welcome to our show today thank you so much

[00:09:18] for having me hey no problem thanks for being brave I know this is a hard thing to to do

[00:09:24] where are you calling from today calling in from Houston Texas so I've been following you

[00:09:29] you know LinkedIn you're one of the main reasons why I should be a lot of my success I recently

[00:09:34] accepted an offer with Compass and BC Universal as a senior business intelligence analyst but always

[00:09:39] constantly on look out to improve my interview strategies I have an interview coming up tomorrow

[00:09:43] a presentation on how to deliver the best interview tips so also you know I'm looking forward to

[00:09:48] learn about yourself and you know this new platform that you have sweet that is awesome and congrats on

[00:09:54] the new job I'm going to challenge you if you don't mind let's do one of these like logic

[00:09:59] thinking question this one is from Airbnb all of you guys watching you guys can try it at

[00:10:04] interview simulator dot io but the question is how many meeting rooms so Airbnb once again I'm not

[00:10:10] going to press play on the question that's how you do it in real life I need an interview

[00:10:14] simulator but basically Airbnb is looking to expand and they're building a new headquarters

[00:10:19] and they're trying to think through how many meeting rooms should they put in this new

[00:10:24] head they're asking you as you know a data analyst to try to solve this problem so

[00:10:30] Joey go ahead how many meeting room should we build as Airbnb sure absolutely first I would

[00:10:35] have to start off with how many people are we intending to to relocate to this office

[00:10:41] straight sample I would also want data on sort of the department titles you know I would imagine

[00:10:47] meeting rooms would be mostly taken up by folks in the upper you know level executive right I also

[00:10:54] would ask about you know it's the square footage of data on the square footage and as well as the

[00:11:00] number of meeting rooms are well that's the question right so I would say the square

[00:11:05] footage I would also ask data on you know the number of hours you know are there like peak times

[00:11:11] that you know certain executives or teams meet most regularly I would imagine that you know

[00:11:18] traffic you know a lot of meetings take place in the morning with those sporadic ones coming

[00:11:23] up any afternoon but if you can guide me more through this I'd really appreciate it okay great so

[00:11:29] yeah let's say we're going to we anticipate around 3000 people being at this building there is a little

[00:11:36] bit of a hybrid schedule however so you know it might not be 3000 every single day let's say 20,000

[00:11:43] square feet so 20,000 square feet absolutely so now that we have sort of you know I think also

[00:11:50] information of the meeting culture so sometimes employees I you know take on virtual meetings

[00:11:56] right let's assume that 25% of those employees are in meetings at any given time and so we would say

[00:12:04] the average meeting size can be anywhere from I would say five to ten people of course there

[00:12:11] can be more people in the meetings there can be less and so with that being insane I would say

[00:12:16] each room can be used around you know 60 to 70% of the time with 30% is where like you know just

[00:12:23] being used like by people that just come into the meeting groups to just to get work so I would say

[00:12:29] based on the assumptions so the total number of people in these meetings simultaneously can be

[00:12:35] 20,000 right every other ones that you just mentioned I would multiply that by 25% of those

[00:12:41] employees that are in meetings every time so 20,000 times 25% I would be around you know I

[00:12:49] would say let me do the math thousand or that 25% 5000 people right and so the number of meeting

[00:12:58] rooms that are needed based on the average size I'd say an average meeting size is four to six

[00:13:04] people so I would go around with five people I would divide them by five and so that would be

[00:13:09] that's that's not that doesn't make sense so a thousand I think I think I I confuse you

[00:13:14] because I think I said I accidentally said 20,000 employees but I really meant 20,000 feet or square

[00:13:21] feet but regardless I think this was a pretty good answer I think you did really well because

[00:13:26] like basically these questions if you're just trying to see how the heck do you think

[00:13:30] like what is your thought process and you did a really good job of basically

[00:13:33] you were almost like streaming your full of consciousness and like thinking through everything

[00:13:37] I love that you like pulled out a calculator I thought that was that was really good

[00:13:41] and you're and I left at the beginning how you like asked for clarity I think that's really

[00:13:44] important and it's something that's that a lot of people probably don't feel comfortable doing

[00:13:49] an interview all the time is like yeah can you can you explain a little bit more so I think I think

[00:13:54] you did a really good job I love what Daniela just said Daniela says OMG these questions made me

[00:13:58] realize I need to do more mock interviews well that's that's good and that's the point of

[00:14:03] interview simulator is to get questions like this and to take a stab at answering them because

[00:14:08] like I mean Joey correct me if I'm wrong but I'm assuming that you don't like spend your time every

[00:14:14] day even as a data analyst even as a senior data analyst like really thinking through how many

[00:14:20] meeting rooms a building should have right that's not something that you do absolutely not but I

[00:14:25] think where the valid comes in is your thought processing and your ability to think critically

[00:14:30] and I think that is what you know when we're talking about the AI era the ability to think

[00:14:36] critically will take us far and make sure that we will not be replaced right because that's

[00:14:40] sort of the trend that we a lot of people say we wouldn't be replaced by AI but I think that

[00:14:45] ability to think critically is what will make us stay yeah 100% and so these types of questions

[00:14:51] I mean of course every day you're thinking critically but it's like you're not thinking

[00:14:55] critically out loud on like this type of a weird question so really practicing like Daniela

[00:15:00] said like doing these mock interviews is really useful but I thought you did a good job

[00:15:04] and like in the end I don't even know I might have cut you off before you actually gave a real number

[00:15:09] but that's not the point of the interview is or the question is like we don't actually care about

[00:15:13] what number you actually end up saying it's more what did you lead up before the number I thought

[00:15:17] you did a good job asking for clarity asking for more data you know going through it almost looks

[00:15:22] like or it almost felt like you had like a google sheet or like excel open and you're like

[00:15:26] putting these numbers in and kind of crunching it and I as the interview were could be like okay yeah

[00:15:31] I could see how Joey could solve these types of hard problems at our company so I thought you did a

[00:15:37] good job I think at an honor rating I would give you probably about an eight or a nine the only thing

[00:15:44] I think that you could approve on is probably just not having to do it in front of people on

[00:15:50] LinkedIn and have like like a piece of paper in front of you just to keep track of it all

[00:15:54] that's being me being nitpicky I thought you did a great job so it would be fun for you to test

[00:15:59] this out on interview simulator and see what interview was things have your answer because I

[00:16:04] thought it was pretty good yeah absolutely thank you so much every so I'll be presenting this I have

[00:16:09] nothing to think of screenshot of this and presenting it to my presentation not tomorrow

[00:16:14] black and technology so you know promoting you helping you promote this hey I I appreciate Joey

[00:16:21] thanks so much for being brave and coming up and let me know if there's anything I can do for

[00:16:25] that as well absolutely thank you so much and we have a good one I recently released a mock

[00:16:29] interview tool it's called interview simulator you guys can check it out at interview simulator.io

[00:16:35] for the next 14 days it is completely free and you guys can just click this button right here

[00:16:40] try interview simulator and just enter your email boom and you will be taken to this page

[00:16:46] right here which is where we have all of our questions from all different companies

[00:16:49] and you can try a question from Apple or a question from Airbnb or a question from chevron

[00:16:54] from the nfl from netflix that good stuff and practice using audio and video you'll basically

[00:17:00] well click on a question you'll hear me ask the question and then you'll be able to respond

[00:17:06] via audio or via video once you give your response you'll actually hear me respond give my like

[00:17:13] example response and then we are going to give you a grade from the interview with the interview

[00:17:18] with is our AI tool and we'll look at your answer give you a score from one to 10 and list all the

[00:17:23] pros and the cons like what you did well and where you could improve and we'll also be adding expert

[00:17:28] examples down here at the bottom as well from other students inside the program as we expand

[00:17:35] you guys can find the link in the comments thank you guys for joining I'm excited to

[00:17:39] be doing this more in the future and we'll talk soon have a good day everyone bye