https://anchor.fm/census/episodes/Data-Careers-Building-and-Leading-Modern-Data-Teams---Operational-Analytics-Conference-2021-ev8ecj

Jeff Sloan (00:00:01): I'm Jeff Sloan. Most recently I was a data product manager at Treatwell, but actually I am seen to be a fresh young face on the Census team as a customer data architect. And I'm here to moderate this session. This session is of course, part of a larger grouping of sessions as part of the operational analytics conference. I mean, as no surprise to many of the data practitioners in the room, more and more emphasis is being placed on data. And there are massive shifts in how companies and teams are starting to use that data. This transformation has given rise to a whole new set of conversations in the data ecosystem. When you start using that data from your warehouse and throwing it into Salesforce, what do you need to think about? Who do you need to think about hiring? Yada, yada, yada, but even outside of the operational analytics case, there's tons and tons of women in the space that we want to explore.

Jeff Sloan (00:00:59): So to that end, since this is hosting an operational analytics conference to create space for these conversations, with data pioneers and data practitioners about lessons learned the evolution of data across teams, company, culture, all the good stuff that we're going to talk about today. And as a tribute to what we were just talking about, make sure to like, subscribe to the DATA CLUB and you'll be kept abreast of all of the other sessions that we'll do after this. You can do that by clicking on the little green house icon above the title for this. So if you see DATA CLUB, in all caps and you see the little greenhouse, you click on that you can follow and then be kept up to date, when we do more sessions. In the case of this session here this is the data careers session that we have listed on the conference site.

Jeff Sloan (00:01:58): And what we'll be talking about today is hiring. We'll be talking about career progressions, we'll be talking about data org design, how leading companies are building casts of great talent and developing that talent. And I guess how they're leveraging all the talent and all the tools in the space. So without further ado it would be great to take the tension of the attention away from myself as much as I love it and redirect it to our panelists to intro themselves in a couple of sentences. So I'll just pick at random for somebody to kick us off. Maybe Rachel, you can start us off here.

Rachel Bradley-Haas (00:02:47): Yeah. Hi, my name is Rachel Bradley-Haas, and a little bit of background on myself. I did not study computer science in undergrad. I studied industrial operations. Graduated, worked at Cisco for a little bit, then moved over to Heroku, which was part of Salesforce. And then moved over to Mattermost. Both at Salesforce and at Mattermost, I was a director of go-to-market operations and analytics, which also involved a lot of data engineering work. And then most recently I left Mattermost and started my own consulting company called Big Time Data with one of my data architects from both Heroku and Mattermost and have been helping different companies realize the data value in their company and how to put action to it.

Joey Freund (00:03:36): Aren't you supposed to drop out, Go Blue or whatever in there, Rachel?

Rachel Bradley-Haas (00:03:40): Okay. Go Blue. I went to University of Michigan.

Jeff Sloan (00:03:49): Whoever would like to jump in next, feel free to just step in. Otherwise I will just pick somebody at random again.

Emilie Schario (00:03:58): I'll hop in there. So my name is Emilie Schario. I'm a senior engineering manager for data and business intelligence at Netlify, which has a lot of words to say. I lead our data function. Prior to this, I was the first data analyst at GitLab. And was there when the company grew from about 250 people to over 1300. I was the first data hire at Doist, which makes Todoist, the most popular to do apps in the app store. And I was the first data person at a company called Smile Direct Club, which is now a public company. Netlify I lead a team of eight of a company of about 120. And we are part of the engineering organization. How about Sarah?

Sarah Vigrass (00:04:48): Sure. Thanks, Emilie. Hi everyone. Nice to be here. My name's Sarah Vigrass. I'm currently a senior insights manager at Spotify. I actually run two kinds of types of teams there. One is a more traditional insights team with data scientists and also user researchers, and then separately, a more centralized data and machine learning team in another area. My background is more in the, I'd say commercial and analyst side, and then slightly delved into the technical world a bit more in the last few years and had the pleasure of working at Treatwell with Jeff before this and a few other startups as well before that. I'm super excited to talk about particularly... I think everywhere I've been, we've been in a growth phase, particularly with startups I've been in so lots of hiring, lots of reorgs, lots of team changing structures. So I'm excited to discuss that with everyone today. And maybe I will pass over to Joey.

Joey Freund (00:05:53): Thank you. So hi everyone. I'm Joey Freund, I'm an engineering manager at Shopify. So I've been with Shopify for close to four years now. And I actually joined as an engineer to the data team, worked on our web analytics platform, then did a little detour and outside of the data org worked on search and collaborated with the data teams on recommendations, then came back and most recently led the machine learning platform team. Before the Shopify adventure, I was a lecturer at the University of Toronto. And before that I had a few fun failing startup attempts and worked in the various research labs and small startups. I think that's roughly it. I think I will pass the mic to Garegin, and sorry if I butchered your name.

Garegin (00:06:54): Nope, that's perfect. My name is Garegin. I'm the senior director of analytics at Fivetran. I have been there for about maybe over three years now. Prior to that, I was at a boutique consulting firm where I focused on data management, sales and marketing analytics for healthcare companies. And prior to that, I was in school. I studied physics at Berkeley.

Jeff Sloan (00:07:25): Awesome. Is that all the panelists? I think so. Thanks everybody for joining us on the panel today. Thanks a bunch and thanks to everybody who's joined in the room. I guess, I'm going to pick a really specific question to get us started because I think it's a great entry point into thinking about building data teams and where we are in the data space at large. And that particular question is relating to hiring and hiring for junior talents on your data team, whether engineers, analysts somewhere in between. And it's no surprise I think probably to a bunch of us in the room that the demand for data talent far outstrips the supply of data talent. And in some cases, people look to more junior or data adjacent talent. I guess, I was wondering for the panelists and then the room, how do you think about hiring these new graduates into a data team or these junior individuals into a team? Like what do you look for and what gaps do you typically see in their preexisting experience where their academic or professional that you have to overcome once they join that data team?

Rachel Bradley-Haas (00:08:51): I'll jump in here. I think I would actually flip the question on its head a little bit and say that, if we think about what the hardest part of data is, it's not actually writing code. You take anyone and spend a couple of weeks patiently teaching them and you can get someone to a pretty strong place in SQL, they can build DBT models and be impactful. The much harder part of data is dealing with people. And so really looking for those soft skills and then being willing to teach those technical skills makes a big difference. And I think that means that data more than possibly other areas could be a real opportunity for people who are taking a second career because a lot of those soft skills are more transferable than other technical specific skills. The other thing is this idea that you need a college degree to be good at data, I think is very wrong. The best analytics engineer I've ever worked with didn't have a college degree. On the other hand, there's someone on my team who has a PhD. So I don't think there's an academic prerequisite. I think all our teams would be really benefited if we remove that from our list of requirements.

Jeff Sloan (00:10:18): It sounds like you're advocating a little bit more for potentially... that there's a good route for people who have had a couple of years, maybe understand the business function particularly well, and then have gained some of those soft skills and are willing to move laterally, or move it... However, it's seen into the data org and that is maybe the path that you see being really, really valuable for teams.

Rachel Bradley-Haas (00:10:47): Yeah. I think it's the popular route particularly because what I've seen over and over is people learn how to leverage data because they have a problem they're trying to solve. It's not that they just want to learn how to be good with data for the sake of being good at data. And so you develop that muscle in learning how to solve a problem. And that makes you very good at the job, which is leveraging data to do whatever the thing in front of you is.

Emilie Schario (00:11:21): Yeah. I was going to say, I totally agree. I think it's a very common path. And also when I really value people who have experience of being on the other end and almost being that customer or understanding when somebody is asking for something that's seems high priority or urgent or a bit random to be able to delve and ask the right questions to figure out actually what are they really talking about. And that is really, really difficult to get without experience really. And I think you're right, teaching technical skills is much easier to teach than business context and understanding. So I definitely value that. One typical, well, actually no, maybe untypical, but super valuable set I've seen recently is actually, I've heard a few ex-teachers come into teams and they again with that soft skillset have been brilliant. So from communication and explaining and coaching other team members which was a slightly new one for me, but something I was pleasantly surprised with as well.

Sarah Vigrass (00:12:32): And something in there. I think one thing that on top of definitely people skills, I mean, to be on a data team, you have to have a lot of patients, that's... with the data quality we have to deal within the people in general, you have to have a lot of patients. I think the other thing that I love to look for, and it might just be because of my industrial background and interest in supply chain is how does data map to process and what's going on in the business. If someone is unable to understand how raw pieces of data can be enriched and combined together and map to a business process or value or understand what it could mean to the business, then they're not going to do well in a data world. So understanding, "Hey, this data came from this source. When you combine it with these other things, then you can output it and derive value from it." Being able to understand how all those things interconnect and create value is so important. It doesn't mean that you have to be able to do that in SQL, but understanding how everything flows through and what that output is needed is critical in my mind.

Emilie Schario (00:13:36): I guess the other thing is that we talking about data, data is so wide in terms of what roles are we really talking about? I think we're naturally all gravitating towards the data analyst and analytics engineers typically, but actually depending on the business it can include quite a wide range of things. So obviously it gets a bit more nuanced, I think at some stage.