About this episode

Quick, what’s the difference between a data engineer and a data analyst? One preps the data, the other analyzes the data, right? A data scientist, meanwhile, analyzes more complex and disorganized data. The truth is all three of these roles perform overlapping functions leading to an incredible amount of confusion in the job market.    

Join Tim, Juan and special guest Danielle Oberdier, founder of DiKayo Data and host of the popular DataFemme podcast, to talk about the state of data jobs. How do we make sense of the roles that are out there today, who should companies be looking for in their hiring process, and what new and exciting data positions are starting to emerge?

Special Guests:

Danielle Oberdier

Danielle Oberdier

Founder, Dikayo Data

This episode features
  • Certifications that make you more marketable
  • How to stand among your peers in a crowded job market
  • What was the very first job you ever had?
Key takeaways
  • Data scientist vs. data analyst vs. data engineer
  • Google is your friend — always be learning, grab those libraries, follow the patterns, etc.
  • Certifications and networking will set you apart in the job market.

Transcript

Tim Gasper:
Juan, it’s Wednesday once again, and it’s time for Catalog and Cocktails. It’s an honest, no BS, non salesy conversation about enterprise data management. I’m Tim Gasper, product guy and data nerd, and joined by Juan Sequeda. Hey, Juan?

Juan Sequeda:
Hey Tim, how are you doing? I’m Juan Sequeda. I’m the principal scientist here at Data World and as always Wednesday, middle of the week, end of the day, and start to go celebrate talking about data because we’ve made it to the middle of the week. Today, we’re going to have a fantastic conversation about jobs and data science and all this stuff. That’s so many jobs around there. Our guest today is Daniel Oberdier. She’s the founder of DiKayo Data and the host of the popular podcast DataFemme. Danielle, how are you doing?

Danielle Oberdier:
I’m doing great. I love Wednesdays. It’s like you said, making it to the middle of the week. Very big accomplishment.

Juan Sequeda:
Okay, great. So let’s start off. What are we drinking and what are we toasting for today?

Danielle Oberdier:
Well, I know I told you I would be drinking wine and it’s not just because it’s wine Wednesday, but I am the biggest wino in the world, but I have to disappoint, I’m drinking green tea with oat milk in it. I’m not disappointed. I love it. But that is a change to my normal MO, I guess.

Tim Gasper:
That’s totally cool. That’s totally a tasty beverage [crosstalk 00:01:23].

Danielle Oberdier:
What are y’all drinking or do I say the toast first?

Juan Sequeda:
Well, we can share what we’re toasting… What we’re drinking first and then we’ll toast. How about you, Tim, what are you drinking?

Tim Gasper:
I made a Man o’ War, which I guess is named after a famous race horse. So that’s what I’m drinking right now.

Danielle Oberdier:
Well, what’s in it though?

Tim Gasper:
It’s bourbon, triples sec, lemon and vermouth.

Danielle Oberdier:
Okay. Nice. Kind of like a Manhattan a little bit.

Tim Gasper:
Yeah, it’s like kind of like [crosstalk 00:01:54] a whiskey sour meets Manhattan kind of with a triple sec. It’s interesting.

Danielle Oberdier:
Got you.

Juan Sequeda:
I wanted to make an old fashion, which I haven’t had one in a while, but I didn’t have any oranges. I just had, I said it’s a bourbon, I had agave and I had a lot of bitters, so I put a lot of different bitters in here and this is really good. I don’t know,

Tim Gasper:
[crosstalk 00:02:16] a bitter blend.

Juan Sequeda:
There we go. So toasty. What are we toasting for?

Danielle Oberdier:
Let’s toast towards fall 2021 and all the data we’re going to process before the end of the year.

Juan Sequeda:
There’s a lot of data to be processed by the end of the year, that’s for sure. Well, I’ll toast to that. Cheers. Cheers.

Tim Gasper:
Cheers.

Juan Sequeda:
Cheers.

Tim Gasper:
Welcome to the show.

Juan Sequeda:
We have our warmup question of the day, which is, what was the very first job you ever had?

Danielle Oberdier:
Well, so DiKayo Data is actually my second business that I’ve owned started. My first one is a fashion business called AK Kiran, where I would knit hand knit and crochet just like fashion items or household items and I would sell them. But then there was always the social mission behind it, of crafting is therapeutic for mental health and anxiety. That was my first job just because that’s the first thing that actually paid me in like-

Tim Gasper:
That’s so cool.

Danielle Oberdier:
I mean, it’s a privilege I know, because growing up, most people have to work and I was doing all my hobbies, doing music, still knitting but not selling yet. So, yeah, that was my first job, and I learned a lot that I can apply now to my second business.

Juan Sequeda:
Well, that’s cool. How about you, Tim, what was your first job?

Tim Gasper:
I wish that my first job was that entrepreneurial and for that good of a cause. I was washing dishes at an Asian restaurant and I tell you what I got really good at scrubbing off that wasabi. It would get really crusted on there and I figured it out.

Danielle Oberdier:
Did you get free sushi from it at least?

Tim Gasper:
I did. I did. I did get a lot of free sushi and Asian food. So that was one of the benefits.

Juan Sequeda:
Well, my first job was a summer camp counselor. [inaudible 00:04:22] worked a whole entire summer working with kids between the ages of, I don’t know, five and 15. That was an interesting summer for say, but I really enjoyed it. I actually went back to the following summer and did… I mean, guess my second job was the same was my first job, so really enjoyed it.

Tim Gasper:
I bet you were one of the cool counselors. The kids liked you.

Juan Sequeda:
I would hope so maybe. I don’t know. We’ll see.

Danielle Oberdier:
That was my being a camp counselor was like my fourth job or something maybe.

Tim Gasper:
Oh, nice.

Danielle Oberdier:
It’s I love it.

Tim Gasper:
I was a camp counselor too, at one point, so we’ve got a whole team of counselors here.

Juan Sequeda:
Well, I guess in some way we always have to be counselors for our jobs here with data. So let’s-

Tim Gasper:
It’s true.

Juan Sequeda:
…. dive into this honest, no BS discussion. Danielle, we’ve been a decade now into all data science and we look at the job descriptions for data scientists and data engineers and data analysts and they’re all over the place. So honest, no BS, how the heck are we supposed to sift through all this confusion in the job market?

Danielle Oberdier:
Carefully. It’s really difficult because the problem is oftentimes the person in charge of putting these job positions up is an HR professional that just isn’t trained in the ways of data. So it’s very hard to describe what a data scientist will be doing if you don’t know, and it’s not their fault. It’s just that companies need to make sure that the person in charge of posting is also a data scientist. I’ve written about this a lot of my DiKayodata.com blog. I’ve talked about it with people on DataFemme. I think, sometimes people say the solution is to have a person, like the superior, the person who’s going to be your manager, write the job position, but I mean, come on, like everybody’s busy. So it is kind of hard to reach a consensus on what these positions are when you’re too busy, just working.

Danielle Oberdier:
That is where I come in because I’m someone who consolidates everything happening in the data science industry and trying to make everybody connected at least in certain ways. Basically, and we can dive into this more with whatever your questions are, but basically what I see as the difference between data analysts, data scientists, data engineer, business analyst is just different levels of responsibility and innovation. I haven’t seen it phrased that way before, but an analyst works with existing data. If I’m an analyst, I go into work and I just kind of type around and do calculations that everybody knows how to do. That’s a very, very vitals service, but it’s not innovative usually. A data scientist is going to basically take more responsibility for, are we processing the data, right? Can we make this easier for the analysts? Here’s the type of approach we should take.

Danielle Oberdier:
Also visualization is a big deal for data scientists. I don’t see analysts doing as much of the whole process that way, like an analyst will kind of clean up the data, but then the data scientist comes in and is like, here, we got to get you ready for your annual report. There’s just like levels of responsibility difference in that. Data engineer, when I think of that, I think of really hardcore machine learning and AI putting projects up that didn’t exist before really, really getting into the thick of machine learning innovation. That can be through natural language processing, that can be through logistic regressions that you do in are by importing Python packages in. I mean, everybody has a lot of experience with that kind of craft if you’re a data engineer.

Juan Sequeda:
Well, what, I’m confused, why are you putting that last part bucket into the data engineers? I mean, that’s what traditionally hear more as a data scientist.

Danielle Oberdier:
I’m not totally clear on how a data engineer differs completely from a data scientist, but what I would tell you and I’m sure a lot of people in my field will disagree, which is the point we should be arguing nicely about this because there is no consensus right now. What I would say about a data scientist is it seems like a data scientist is more forward facing than an engineer usually is. An engineer does a lot of back end programming, but might not be the one who’s presenting. I feel like the data scientists role requires more communication with analysts, more communication with business executives. The data engineer will report to somebody, but I feel like they are behind the scenes a little more. Does that make sense?

Juan Sequeda:
No, I agree with that, the data scientists kind of being more… Well, I don’t know if they’re going to be always forward facing like talking to people because not all, I mean, you have to have people skills and to go do that and they may not all have that, but they’re definitely, I would argue that they are done definitely doing more things that are kind of more… The front office is going to be seeing more of that impact while the data engineers they’re kind of in more in the back office and they don’t… You don’t even like, well, what are they actually doing? Well, they’re making sure that the machines are running in a way, but they might not be that evident of what’s going on the front office.

Danielle Oberdier:
Also, there projects are more long term. Like a data engineer’s project could be a three year saga before it ever hits the light of day. I feel like the data scientists workflow is a little bit more in and out. Also, it’s the languages too, that they use. When I hear data scientists, I can be pretty sure that Tableau that you’ve at least tried Power BI, that you have a working understanding of Python or are, and you are kind of trained in some kind of database thing like SQL. With a data engineer, I don’t… All my data engineer friends don’t really use Tableau. They’re like, they know what it is. They know it’s a big company, but they’re not the people doing that. Also, I’ll say like, just the uses of these languages, like Python is so fricking robust are not as much, but it can do a lot.

Danielle Oberdier:
I feel like both data scientists and engineers do have to have a working understanding of how statistics fits into the programming that you’re doing. But just the types of things you’re doing will be different. A data scientist is probably using Python packages to visualize, or to sort a lot of like name matching stuff like that, and then I feel like shiny apps. It could go either way, but it really is… It really comes back to different levels of responsibility and innovation, even if you are using the same tools. What we have to do is make it clear because I’m willing to say that a data engineer at one company is going to be doing the same things as a data scientist is at another company. That’s okay. The job position just needs to be really clear. Over time, people will start to talk about their experiences more and it’ll be more clear, I hope. But to do that, we have to have some organization and some communicate around it that’s not just a Google search. So this conversation is like-

Tim Gasper:
Right. That’s super interesting. I like that you’re approaching this in a slightly different way, or with some more nuanced opinion here, because first of all, it seems like one of your statements, one of your conclusions here is that the titles aren’t very good descriptors these days. You need to look at the job description itself and look at what are the responsibilities that they’re listing out here and what are the experiences that they’re looking for? Because those first of all are going to be a bigger hint as to what this company is probably looking for. Then for employers, that is probably where you need to focus your intention, in terms of communicating to the market, what you’re looking for, right?

Danielle Oberdier:
There’s a lot of problems too, that come up just because people don’t know what they need, but they hear, they want to hire a data scientist to make it and use the beer tap at sad coworking space. Like I-

Juan Sequeda:
That can’t get more honest, no BS than that, right?

Danielle Oberdier:
Yeah.

Tim Gasper:
I love that.

Danielle Oberdier:
That I just watched the WeWork documentary, so that’s fresh in my mind about, that’s why I said, sad co-working space. New Orleans never got a WeWork, but we do have like beautiful co-working spaces. Anyway, some of them have beer taps, some of them don’t. I mean, it’s like when you hire a data scientist, just because everybody’s doing it, you have to have business goals, you have to have at least like a prospectus of what you want this person to accomplish, and the thing is like business people don’t always know. That’s why we talk about data literacy.

Danielle Oberdier:
There’s like a bare minimum that everybody should understand data because data is the language that people already speak. If you don’t kind of know the basics you’re behind. And so I do a lot of career consulting and every time I come across somebody who like is an analyst or is a journalist, or is any kind of profession, and hasn’t thought about how to integrate data or take like a little course, Sarah Python class, do it because you’re going to fall behind and then no one can save you because it’s too late.

Tim Gasper:
I love that. Let’s dig in a little bit on some of these responsibilities that some of these people have to have and have to do. So you’ve got sort of these data roles, right? You might have data analysts, data scientists, data engineers, and then various permutations of those terms, and add some adjectives to the front. What are some of the things that you’re seeing are more like shared responsibilities? I think you had mentioned like statistics, pretty much all these roles need some level of statistical understanding, obviously like engineers, maybe more data scientists, maybe more… What do you see as like shared responsibilities versus more like specialized responsibilities?

Danielle Oberdier:
That’s such a good question. I came into the world of data science through statistics. I fell in love with statistics. Everything about statistics just opened a whole new world for me. I am glad that I don’t have to learn the statistics after the fact, after I get really, really good at Tableau. I’m like, well, I don’t know anything about stats. I think that-

Tim Gasper:
As an economics major, I very much agree with you on that point.

Danielle Oberdier:
There are certain things that are harder to learn later. Basically what everybody should have in common though, because like we’re not talking a lot of the analysts and the analysts are very important. The analysts are there to do what the data science team directs really I think. I love when people are arguing with me because like we have to figure this out and I’m not the V all end. I just observe a lot of different… I observe from a bird side view on the industry. So that’s why I can speak to it. I think like the analysts probably need to have a more day to day understanding of SQL and your company’s SQL because that’s what you’re going to be using to sift around data. Data scientists need to know it enough to access the system and point the analyst in the right direction.

Danielle Oberdier:
But I’m not sure it’s as essential and for a data engineer, I don’t think they’ll be using SQL that much as much as an analyst. But it’s still a good foundation because why not learn it. Why not learn it and then be able to apply it to your work in ways that nobody has yet, like maybe there’s some SQL capabilities that could lead to really great projects and innovations for an engineer. I for everyone like to plug SQL into R and play around, but sometimes I have more time on my hands than others. I think Python and, or R are common for both. Excel is important, and you should… When I’ve done classes or teaching people about econometrics in R versus Eviews, remember Eviews. I always say like sometimes, you don’t have to like show off and do everything in R or Python. If the quickest fix is in Excel, close out, open your CSV and change it. Do like a V look up, like delete a column.

Tim Gasper:
I mean, that’s still for the job, right.

Danielle Oberdier:
Time is of the essence. Don’t let your pride get in the way. That’s what I would say. Definitely, I feel like statistics is easier to swallow when you’re doing it hand by hand on Excel or even math calculations on paper. That’s what I did in business school. That’s when I started all of this when I would have to do the calculations without a computer and then you really have to understand. I know that a lot of data analysts, data scientists, not so much data engineers, because they really have had to go through so much training, but I feel like a lot of analysts and like data scientists and business analysts, they don’t really get that foundation. I’m lucky that I do because it really does open up all the capabilities of my favorite language, which it’s R and connecting the math to it. The best data scientists that I know are very rooted in the math.

Juan Sequeda:
One of the things that you’ve mentioned a couple times that it’s interesting is that the analysts are doing more the day to day. Again, I guess a lot of the issues here is these titles. We’re calling analyst and data scientists, and actually there’s a comment here from Jeff saying that, “These words what do they actually mean?” Let’s take the word data out of them. I like how we’re describing this right here that let’s not put labels on them. So there’s folks who deal with the day to day. You’ve actually mentioned that they’re like, not that innovative, they’re just just shifting to, yeah, we got to go answer this question today. Let’s go do that. Then you have another group-

Danielle Oberdier:
They may be innovative as people just.

Juan Sequeda:
Just that the work you isn’t that innovative, but it needs to get done, right? Then there’s another group, a cohort, which is kind of the data scientist here, but let’s put the title aside and there seems to be doing more of that innovative, and they’re more forward thinking about what else can be done with the data. Then there’s other cohort who’s in the backend and they’re making sure that things are going to be running. Another interesting aspect I’m getting out of here is that the projects that the folks in the backend, like we’ve been calling them the data engineers, they have more long term projects while the the front end folks, or the ones who are dealing with the day to day, they have more shorter products. We need to go get some stuff quickly.

Juan Sequeda:
That’s kind of summarizing what we’ve talked up to now, but one of the things that’s still stuck is there’s this whole cohort that needs to go do the day to day work that isn’t that innovative, but needs to get done. That’s not exciting. How do you hire people to go work on that area, or is that just going to get… Or is that I’ll get automated by tools, or is the data scientist going to go take that on later on? I mean-

Tim Gasper:
Is it a bad time to be a basic analyst, like is at the boring job in this trio?

Danielle Oberdier:
Oh man, I’m going to have a lot of [inaudible 00:23:16].But that’s the point I know, no BS. I think that, and this is going to be the best answer. I think that, that really depends what you want your job to be, and nobody should be judged for that. For me, I want to always be working. When I wake up at four in the morning, I want to be working, but then at the same time, I want to never be working. That is the entrepreneur lifestyle, and I am good at that. However, a lot of people just want to do their nine to five, get happy hour with their friends after work and go home. That is a valid lifestyle.

Juan Sequeda:
I love that answer. I was thinking right now, well, yeah, everybody wants to be innovative. I guess being innovative or saying that you’re being innovative or not, like, I don’t think we should describe it that way. It’s like, yeah, there’s just work that needs to get done and it’s very well defined, and you can have that lifestyle, you just said, just work your nine to five and you get your shit done basically, and it’s providing value. Perfect.

Danielle Oberdier:
Well, one of my [crosstalk 00:24:29]

Tim Gasper:
Is this a statement though about the analysts role? Because I know plenty of analysts who have very poor work life balance. I wonder if there’s something else going on here.

Danielle Oberdier:
Well, I talk to… One of my best friends, I guess I could call her an analyst. She works at a university and we talk about this all the time, because she is a very creative person. She has a lot of hobbies outside of her work, and that’s where she chooses to put her creativity into. I choose to channel everything I have really, and to work. If it didn’t make me happy, then I would have to adjust that, but it does. And so it’s like, we’re very different people. I still meet her for happy hour, but in my head, I’m like, oh, did this person respond to this? When am I going to get a check for this? When is season three starting, what… I don’t mind that, but I do because I have worked a more analyst type nine to five job in the past, it was kind of nice to leave your work at work.

Danielle Oberdier:
I think like I don’t, and also the difference, I’m thinking how to phrase this. The difference is in how much, and we kind of touched on this earlier, but how much people interaction is involved. I think that if you’re a senior analyst, you’re definitely managing analysts who’ve just started, you have that responsibility. As a data engineer, I’m pretty sure that most companies just want you and your computer to come up with something incredible, and I don’t think you’re doing as much managing as part of your role. Data science team leads still I feel like there’s less hands on managing and training than if you are an analyst. So being a data analyst is probably the closest we have to other industries that have existed for all of time. I don’t mean all of time, but you know.

Tim Gasper:
Like an era.

Danielle Oberdier:
Yeah, like no dinosaurs and computers. That would be cool, but nah,

Tim Gasper:
No, this is fun.

Danielle Oberdier:
I think the structure of an analyst’s work is probably more similar to the things we understand and have seen, but going up from there, it gets a little dicey kind of figuring out what the responsibilities are.

Tim Gasper:
It’s definitely the role that’s been around the longest too, right? Not to say that those are the dinosaurs to go back to that. But I think what’s interesting, I’ll make this comment, and then I know we want to transition to another topic, which is around actually certifications and some of the things that people can do there. But my last comment here is that I honestly like over the last few years, I used to be in love with the data science title, but I find myself falling out of love with it. I know we even had like DJ Patil on our show who is actually one of the pioneers of the term around data scientists and the field around that. But I feel like a lot of the data scientist role came out of trying to be unique and different and being like, this is a different skill set and a different focus, and you’re going to be focused on this different area. And it was a way to kind of be like, hey, we want the data scientists, not the analysts,

Tim Gasper:
But it feels like the analyst markets actually kind of catching up now. You’ve got a lot of modern tools, some code oriented tools, some are no code oriented tools that are aimed at analysts. I don’t know if you two, both agree with this or not, but like I find myself feeling like the distinction between a data scientist and an advanced analyst, like somebody who’s more of a senior analyst or very capable analyst is starting to go away. I kind of like, we’ve got analysts, we’ve got in engineers, data engineers. We’ve got hybrids that are sort of these list engineers. Then we have data product managers who are leading and organizing these teams. Perhaps they’re a little bit more the sort of the front office person. I don’t know, am I crazy in thinking this, or is this a [crosstalk 00:28:59] valid perception?

Danielle Oberdier:
No, that’s just very valid. It made me think of a new kind of way to talk about the structure, which is that data analysts exist to basically carry out and try out what the engineers and the scientists come up with. Basically, the engineer says, “Okay, this is how we’re going to… This is my module. This is what it does, go to town. Let’s see if this makes us more money.” The analysts are kind of the test subjects in a way. I mean, of course the consumer is, but like the analysts and how well they can like…. Well, meaning efficiently and with the least errors, like the most accuracy, how can they carry out basically the larger mission, which is directed by the business, but also determined by what the data engineers and scientists can come up with.

Danielle Oberdier:
It’s like, you filtered down like they may have less responsibility the analysts, but at the same time, they are the ones who are trying out the company’s new way of doing things, new systems. If they’re not happy with how efficient it is or there’s errors between analysts. Analysts are a big workforce and if different analysts are coming up with different results, that’s important. Then that information travels back up the pipeline, food chain, whatever you want to call it.

Tim Gasper:
So, we got data scientists are chopping down the trees. We’ve got data engineers laying down the road, and then we got analysts driving on the road.

Danielle Oberdier:
Or coming up with like a new electric saw that just like cuts through the whole forest. Then obviously there are risks there, and unfortunately the analysts are going to be the ones that experience those risks first, and data scientists would be the people kind of stepping back one level, but still really witnessing it on the scene. And the engineer goes to sleep and wakes up to total destruction if they didn’t do well.

Juan Sequeda:
Kind of getting into the takeaways already, because you’ve made me think about this on the front end and the back end. We make these analogies all the time with software. In software, you talk about front end engineers and then backend engineers. You think about full stack engineers. Again, let’s just put these titles away, a data scientist and engineer and all that stuff is, I think you’re going to have people dealing with data on the front end, the front office, you have people dealing with data in the back office, and this is like, let the infrastructure go run. Then on the front office, you have to go deal with being clever, cool new things doing with the day to stuff. There’s different types of front office types of approaches.

Juan Sequeda:
That’s how I’m realizing that we should be looking at these roles. And as you mentioned, right, and one place, a data engineer for some, another place is going to be the data scientist. At the end, it’s really think about, is this more front office work or is this more back office work? Then you can have folks who are the full stack. I can do from the back to the front and all over. That’s kind of, I think I’ve already given my takeaways here. I’ll repeat that at the end, but that’s awesome.

Danielle Oberdier:
No, I have something to say about that for sure. I’m glad you brought up the whole full stack engineer, more of the coding terms, because I mean, as I’ve been watching the new Gossip Girl and I can quote it almost as well as the old one, “Google is your friend.” Somebody says that. So, if Google is your friend, what that means is I don’t like JavaScript. I don’t know anything about it. I don’t know like how to use D3 libraries. However, I can use those plugins in R, I can geocode, transfer it from R into a JSON file and then upload it to a site that might be outdated, but like gaffy or something. Sometimes it’s like I’m comfortable in R but sometimes I’m going to have to look something up to incorporate some kind of front end programming into data projects.

Juan Sequeda:
I think this is a common theme that we’ve always seen Tim here is that we just do all these analogies of data with software, and then this is just another one too. I wanted to get into something we talked about before is on the certifications and stuff. Right now, what are the opportunities that you receive for people to go take certification? How do they become more marketable in this job market right now?

Danielle Oberdier:
I’ve always been a huge fan of certifications and it’s an ongoing discussion of which ones are the most important, but moving to Louisiana, I found that having a trade is really, really a cool thing. You gain a reputation in your career by having that trade. And so that’s kind of how I view data science in this way. It’s very hands on. The thing is, as somebody who has multiple degrees, I can’t tell you that what I learned at Tulane is the same as what my friend is learning at Wharton. That’s not Tulane and Penn, I said. That analogy didn’t fly as well as I wanted it to. But you can’t look at a degree employers, can’t look at a degree and see what you know whereas a Tableau certification, yeah, okay. you’re competent. We know what you’ve had to know.

Danielle Oberdier:
It’s easy for employers to kind of vet what the certifications test and then really assume that you have that skillset, your degrees, your jobs, that’s awesome, but there’s too much variation there. There’s like just too much variability in all the degrees and jobs that you might have had that, that’s not the most efficient way for an employer to be like, okay, they know their way around SQL. They know their way around Python, and this is what they done in Python. Because Python is like such a robust language that you can like divide it up into different things. What do you use it for?

Danielle Oberdier:
I know for a fact that like two years ago, the Tableau conference was here in New Orleans. They transformed the Ernest Memorial convention center from a graveyard to like this one plush heaven, then they took it all away. But while we were there, we, as in my class, we were given just these cards for free vouchers to get certified in Tableau because that’s what it really matters to these companies is to get people certified. I mean, I’ve seen a lot of people be certified in like CMA or you take your financial certifications at three levels and Salesforce, huge one. I mean, there’s just so many for… There is a certification for any trade you’re in. I know that I was… I don’t really like accounting, but I was in an accounting class where I got a free voucher to take the managerial accounting test. I didn’t end up getting around to it because other things were more important, but it is nice to know that that exists.

Danielle Oberdier:
You can always find something that will take you to the next level, and that means more money. Like somebody with a [crosstalk 00:37:41] series seven, somebody with a series 65 who works at Goldman Sachs as an investment banker is more worth more money than somebody who doesn’t have it. It reminds me of one of the actors on the TV show Lost was saying like, “The more you can do, the more you don’t need a stunt double, the more you’re going to get paid.” So like he threw his own knives because he wanted-

Juan Sequeda:
This is-

Danielle Oberdier:
You know.

Juan Sequeda:
Okay. This is very strong statement than, yeah, go get more certifications, more certifications makes you more mark and opportunity to go get a better job and get paid more.

Danielle Oberdier:
I mean, you have to… What I would say. Okay. So me, I’m a free agent. If I got a certain certification, I would pay for it myself or get somebody to sponsor me maybe. But the thing is, if you’re at a firm, if you’re at a big company and a lot of my people that I talk to on DataFemme or come across on social media in the industry, a lot of them are. They’re at big companies doing data science. You just need to kind of like when you’re in college and you don’t go to the guidance counselor, you don’t go to career services. I’m not saying those things will change your life, but why not use them? Why not find out what certifications you can get for free at your job? That’s just like, that’s just normal. I mean, maybe it’s not normal, because we’re all overwhelmed, but it should be like when you [crosstalk 00:39:21]

Juan Sequeda:
Our jobs, the company should be offering these certifications now for all their employees, right? Because you want them to be more… Learn things and they’re basically investing in their employee. So I think that’s something we need to start seeing.

Danielle Oberdier:
Well, I think they already do. It’s just that people don’t take advantage of it and that’s okay. But if we’re talking about certifications as the next new proof that you can do something and then your company is going to offer you a free opportunity to do that, maybe even training sessions after work. I mean, there’s so many resources for free at your company that you can take advantage of. It’s important to do that if you’re looking to advance because it’s a competitive market out there.

Tim Gasper:
I know a lot of people who are… I’ll just call them like certification skeptical. I can remember a conversation I had recently where I was encouraging somebody to get a certification in a BI tool. They were like, “No, no, I can figure it out. I’m going to learn it on the job and that kind of thing.” I think in general, we all try to be very self-sufficient and we’re like, oh no, I don’t need this certification to do that. But it seems like there’s a lot of benefit to that. We should try to change our minds around that. Especially if your employer is going to be encouraging of you doing that on the company’s dime and on company time, right?

Danielle Oberdier:
Well, it’s like, why does it hurt? It’s funny when you said certification… What did you say skeptical? I was like-

Tim Gasper:
Yeah, skepticism.

Danielle Oberdier:
I thought about pronouns. I was like, I identify as certified certifications. That’s why you see me laugh-

Tim Gasper:
Put that in your Twitter profile.

Danielle Oberdier:
And your email signature. No, I think that, it’s just a way to prove to… It’s like global entry. Why wait in line, when you can have global entry and bypass the line. Certifications will have you bypass the line because they’re like, oh, they have this certification.

Juan Sequeda:
Hold on. Is that true? Well, so is that a given or is that kind of very employer specific? Well, employers like, oh, I’m going to put on the top of the pile of folks who have certifications, I’m looking for an analyst and [inaudible 00:41:52] Tableau. So, if they have at Tableau, they go to the top of the pile or people are like, “I don’t believe that. It’s just, I’m not giving too much [crosstalk 00:41:59].

Tim Gasper:
I like the certifications. I think it’s a good thing.

Danielle Oberdier:
I mean, I can’t speak for every employer, but I will say that people are lazy and want to do things with the least amount of effort. So like, if I have to call up my friend at IBM say and be like, “Okay, what did this analyst do exactly? What were her major tasks?” What does this mean versus just see, oh, are certified. I’m going to do that. I’m not saying I won’t be thorough, but it’s just like, you put yourself at the front, because you’ve already proven on a standard system that you know this stuff, that’s the thing.

Tim Gasper:
How do you make it easier on employers and things like that? I know personally looking at resumes for data roles, scrolling for like, do they use the word Python? Did they use Python anything? And if you see like, oh, certified, took Coursera course on Python, or went to Python bootcamp, or something like that. I mean, they may not have the ceiling that you’re looking for, but you now you know, they have the floor. You’re like, oh, okay, good. They at least know Python and we can grow them from there. So, I mean, I think it can be really helpful in that way.

Danielle Oberdier:
Yeah. I think it just standard, like now I’m going to be talking about stats and their application to real life, which is my favorite thing in the world. It’s like normalizing your data. That’s what the certifications do. You are not comparing apples to oranges anymore. You are saying that this one experience that can be quantified very easily is something that this person and these people have. Maybe it is entirely, entirely probable that there are so many people in your employee pool that are way more qualified than the person who has the certification. However, that person you have… There’s just less proof and people like proof. The thing is like, yes, maybe you are the biggest rockstar of RGIS or Salesforce or whatever. Maybe you are so good at it, then why not get the certification? Why not just do it?

Danielle Oberdier:
Because if you’re that good, prove it, don’t rely on employers to go do the dirty work for you of researching what you might or might have done. Save that for your interview, which you will probably be more likely to get, if you have a certification. I’m actually… I’m really, really, really on the investigative team with this. I would love the statistic of people with certifications are X percent more likely to get hired in data science. I will find that statistic. [crosstalk 00:44:51].

Tim Gasper:
I would love to see that.

Juan Sequeda:
That’s a fascinating [crosstalk 00:44:54] question right there.

Tim Gasper:
I bet some of the HR tools like Breezy and things like that have some of this data but I’d love to see that.

Juan Sequeda:
All right. That’s a call out if anybody from Breezy or these HR tool are listening out.

Tim Gasper:
Calling all. Does anybody have a good data set? We’re looking for a data set here. We talked a lot about certifications just now. Danielle, you’re very involved in the data community online, as well as in person. What else really helps people to stand out other than, certifications in this market. Also, how does a community play into this? Is that also a big factor here?

Danielle Oberdier:
I mean, you probably know what I’m going to say. Networking, you have to network. I remember being so annoyed with one of my business school classmates who I would try to bring to networking events with me, and she’s like, “I don’t really need to do that.” I’m like, “When am I going to get this through to you that that is the only thing that really gets you hired. The knowledge isn’t so important. But if you can’t tell people, if you can’t have casual conversations like these, about the things that you know, you’re not going to get anywhere.” I got my company to pretty much like the top of the industry in less than two years because I networked constantly on Twitter.

Danielle Oberdier:
I went to conferences. I kept in touch. I developed relationships for fun, for leisure, because it’s like networking doesn’t work when you’re not genuine about that connection. And so it’s a talent, it’s a motivation, a drive. It’s just not something that everybody is good at, but it is something that you need to at least get better at, because I do believe that most humans want to connect with that at least somebody. And so when you’re networking, when you’re at these like functions with the free cheese and the open bar and… I miss that.

Juan Sequeda:
I love it. So yeah, you got to take advantage of your community and you got to network. I think that’s… Because the [crosstalk 00:47:12] folks are going to be networking are the ones who are going to go head, right? Network and have certifications you’re already getting ahead of the crowd.

Danielle Oberdier:
I think with networking too, I really, really, really want to drive this home to everybody. You will have to be genuine. People can tell when you are networking and just throwing pitches and don’t give a damn about.

Juan Sequeda:
That’s why our… We’re the honest, no BS. That’s sort of tagline here, We take it for… We were so true for that, because I think in this… During the whole pandemic and us being remote, like this is something that we need to go do. I mean, that’s why we started this whole podcast was because we wanted to go network. I mean, honestly it was Tim and I trying to go just… We thought we had good ideas and topics and we talked about it and people were paying attention. Then we wanted to go network with more people. Let’s go invite people. So I’m fully with you that networking is crucial here.

Danielle Oberdier:
[crosstalk 00:48:05].

Tim Gasper:
We can’t just go to conferences and rub shoulders anymore. I mean, obviously we’re starting to get back into that, but like, through this pandemic, it’s been like, you got to find creative methods.

Danielle Oberdier:
Well, and it’s also like, you have to think of it, about it from like a personal perspective too, because you know what if somebody is literally the person who could take you to the next level in your career, but you don’t get along. That’s okay because there is somebody else who’s your fricking soulmate who does all the same things. And so in the industry, it’s really fun to see at conferences like, yes, we all get along, we all have respect for each other, but like who is hanging together, that makes a difference. That’s how I know a lot of my information because I’ve been… I will take the time to sit and actually talk to you about, not just your work, but like your hobbies and like which paintings you like and where you like to do, your exercise and are you an Olympic swimmer? I will talk, I will talk to you about all of those things.

Juan Sequeda:
That’s [crosstalk 00:49:16] the whole thing about being genuine. I think that’s an important thing when you go off to network. Hey, I told you it’s almost 50 minutes in. We could keep talking for hours around this. I love this, but we want to get into-

Danielle Oberdier:
[crosstalk 00:49:28].

Juan Sequeda:
…. our lightning brown session. So we got a couple of questions here going on. I’ll go first. Is the data engineering or let’s call it the back office community growing faster than the data science, the front office community? Yes or no?

Danielle Oberdier:
No.

Juan Sequeda:
You go next.

Tim Gasper:
I like that. An analyst wants to get into like data science and data engineering. Let’s say they’re kind of coming for advice here, is machine learning a key skill that they should learn?

Danielle Oberdier:
I think everybody needs to be familiar with it, but the reason that I’m trying to be quick, because it’s a lightning run. The reason in that the engineer community is not growing faster, it’s simply because you have to be more qualified to be an engineer, and those skills take time. It’s like learning a musical instrument. There’s some perfection and art to it. Not that data scientists can’t have that, but it’s not necessarily required. So they are growing faster than the very, very, very like specifically trained engineers who are the expert of what they do.

Juan Sequeda:
All right. Go to next question. Every data role should be competent in SQL, is that true?

Danielle Oberdier:
I think so at this point because even if you’re not really… Yeah, I mean, of course you’re working with databases at any office. Again, think of the dinosaurs. We’ve always been working with databases. Maybe they were on paper. Maybe they were scratched somewhere like all on a napkin. I don’t care. There’s [inaudible 00:51:09] always been databases. So yeah, SQL is pretty important at this stage.

Juan Sequeda:
Tim, last one.

Tim Gasper:
Everybody [crosstalk 00:51:19] R versus Python and there’s sort of like the silent majority, the SQL like lurking in the background.

Danielle Oberdier:
Well, the thing about SQL is SQL just helps you work on a team. The reason I’m not that good at SQL is because I don’t need it. I don’t have a team yet. I don’t have… I’m working with me and myself and I’m also on media. So I don’t need to really have that knowledge but any job you work at, you’re going to have to learn SQL to communicate with your other team members on the pipeline. Like the engineers communicate with the data science people and you have to have access to that. Most people at these firms are constantly logged into SQL.

Tim Gasper:
It can be kind of the Rosetta Stone sometimes. Last question for you lightning around here, let’s say you’re an employer and you’re looking for somebody to fill a data role. If you can’t really articulate what that role needs to be and what your goals are, are you putting the cart ahead of the horse or should you go ahead and get that data person and fill them in? So are you putting the cart ahead of the horse, if you’re trying to hire that data person you don’t really know what the job description should be and what your goals are?

Danielle Oberdier:
Yeah, that is just the waste of money, and everybody’s time. You need to be able because I’ve been that data scientist who comes in and they don’t really know what they want and everybody loses in that case. I mean, at least be able to tell this person. I’ve had both ways. I’ve had a really excellent job where they said, “Look, this is our objective. This is the problems we’re having, and this is what your day to day is going to look like.” And I was very prepared for that job, and I did a lot of good work. But in the other job where I was just kind of there to be there, I didn’t-

Juan Sequeda:
I’ve heard a lot of anecdotes about this. People are data scientists, but they don’t even know what they’re doing or they go do something, and then at the end it is like, well, nobody is using this. Nobody needed it. But-

Danielle Oberdier:
Or like, you don’t have access to some of the databases that [crosstalk 00:53:24]-

Juan Sequeda:
Exactly.

Danielle Oberdier:
…. your senior analyst to help you, but they’re busy. They don’t want to train the new person.

Juan Sequeda:
All of this is not prepared. Well, this has been a fascinating discussion and we got a bunch of notes here and the TTT Tim takes it away with takeaways. Tim.

Tim Gasper:
I am very happy about some of your advice that came out of here, which I think is very helpful to a lot of our, our listeners and folks that they interact with every day. That don’t just do the obvious things like, work on your resume and that kind of thing, but also think about certifications and how that can be something that can really help you to stand out and to be another way that you kind of show off what you’re capable of, and [crosstalk 00:54:10] also learn new skills. Networking, the importance of going out there, getting yourself out there, meeting people and related to networking. I really liked your comment that you said when you said that if you’re dependent on a single person to get that job and you don’t really like them, you don’t trust them, there are other fishes in the sea.

Tim Gasper:
It immediately had almost like a dating reference to me. They’re like, “You don’t have to commit yourself to that person. You can find somebody else.” I think that’s good advice, and if you feel like you’re overly committed to a single person, then that means maybe you’re not networking enough. You need to get out there more, you need to meet more people. Then I liked your comment to the lightning round question around, as an employer, if you don’t know the role, you don’t really know the business goals, then, you need to get your house in order to really bring in a good data people and have them make an impact.

Juan Sequeda:
My takeaways is, I think I said this before, but let me go repeat it is, first of all, there’s no single definition for all these titles. Actually we should put those titles aside and you got to really look at the job description if that’s what you’re looking for. That’s really what it is. So, forget the title on hiring a data scientist, look at the job description. I think the main takeaway for me is this separation. These two cohorts, you have the front office and the back office, right?

Juan Sequeda:
The front office of data there need to be innovative, and they also need to get the day to day work done of doing things with data answering questions. The back office, they’re implementing that infrastructure. They’re creating those multiplier effects and making everything is operationally working. Then, you can ask yourself, am I more of a front office? Am I more of a back office or I am a full stack, or I do everything. So I think that’s the way you should start thinking about things and kind of put the titles aside for now, because you think about what is a scientist, what is engineer and analysts? Semantics in here, but well-

Danielle Oberdier:
[inaudible 00:56:09] the office and ask questions about what you’ll be doing. Like employees have to be clear on what they want to be doing too, to see if it’s a good fit.

Juan Sequeda:
Danielle, let’s throw it back to you. Final two questions. One, what’s your advice broad about data, about life, whatever? Second, who should we invite next?

Danielle Oberdier:
All right. Well, first you don’t want my advice on life. If I give that to you, it probably wouldn’t make sense anyway, but I will say about networking. I’m glad that we got to talk about that because that wasn’t in our plan, but really like, I just need all of you to take home that be authentic, be genuine, share pictures of like your cat. I have a cat. I like be a person first when you network and there’s never going to be a problem for you because people will want to know you, you get their contact naturally, you tell jokes, you have inside jokes and then later they sponsor your podcast. I really think… You can’t be… It’s not going to, if you are authentic, like fakely authentic. Is that a thing?

Danielle Oberdier:
Trying to look authentic when really you just want something out of somebody you have to genuinely really want to talk to this person. That’s why I said, you’re not getting along with this person who could help you to the next stage in your career, well, there’s somebody else with those same skills, who’s going to totally vibe with you. So look for those people. In terms of next guest, I figured it out early in our conversation Mallory O’Brien from Amazon. She is very mathy and she’s used calculus in creating modules for Twitter and social media. She’s one of my recent podcast guests, and she just would be amazing for you guys. I can put you in touch if you need.

Juan Sequeda:
Definitely, would appreciate that.

Tim Gasper:
Thank you.

Juan Sequeda:
Danielle, well, thank you so much. I appreciate it. Have a great Wednesday. This is a fantastic discussion and a lot of great takeaways for people who are listening on what to do next, go get your certifications, go network, and think about it. Think about these jobs, not as the titles, but more about kind of the front office and the back office.

Danielle Oberdier:
Yes. This was amazing. I had a lot of fun. There will be a recording, right?

Juan Sequeda:
They will be a recording.

Danielle Oberdier:
When I’m talking and talking, I don’t always hear my self.

Juan Sequeda:
Next week we have [inaudible 00:58:51] who is the chief data officer of [inaudible 00:58:54], one of the largest data analytics companies in the world. That’s going to be an interesting conversation about… We’re going to be talking about ethics and responsibility of data. So cheers all have a great Wednesday.

Danielle Oberdier:
Thank you.

Tim Gasper:
Cheers, Danielle.

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