Snowflake’s Chief People Officer: The High-Performance Culture Secret

0:00If you just try something and fail without really measuring it or without really understanding what happened, then you're in the darkness. because then you could fail over and over again, but you don't learn from them. Right. So I love the idea of failing, but also measure what you're doing so you can then tweak it in the right way.

0:19What would happen if we just told the truth? Welcome to TruthWorks, where we dig into the nitty gritty of leadership and work. And what needs to change. I'm Jessica Neal. And I'm Patty McCord. Our journey together started in HR, but trust us, it's evolved into something wild, honest, and well, a bit rebellious. So throw out the handbook. We're here to redefine rules to work for us, not against us. Let's dive into another episode.

0:57Hi, everyone. Welcome to another episode of TruthWorks. I am your host, Jessica Neal. And today I have a guest who I've known for a really long time. We haven't talked in eons, but I don't even know what year I met you, but it was way back in the day and you were at Tesla. And my guest today, his name is Arnon. But Arnon is one of the most influential HR people operations leaders, I think ever. So to say that. And you have worked at Google, Tesla, and now you are at Snowflake. And having been in a similar role to you, I know how challenging these roles are. And I know how complex the whole situation is.

1:48And I'm not going to complain here. This isn't me complaining. But I think that what we do is the most challenging executive role, especially at the C-level. There's so many things on our plate, and a lot of times we are not prepared for them. So I am very excited for our listeners to learn from you and how to think about what our mission really is, and also to hear all the exciting things that you are doing at Snowflake. So welcome to the show, and good to see you, friend. It's been too long. I'm so glad to be here. Thank you for inviting me to share this time with you. Yeah. Yeah. So let's tell everybody, I think it would be great for people to get to know your history kind of a little bit as I do, but you have been at some just incredible category defining companies from Google to Tesla, and now you're at Snowflake.

2:45And you've been in the people world for a very long time. So tell people a little bit about your story, how you got started and how your whole career happened. Let's hear it. Yeah, I would love to. Thank you. Yeah. So I really looked at how I could go from different industries to see if HR worked, how HR works, and how different methodologies could actually make the HR team better in different sectors. So I started early days in semiconductor at Applied Materials and really learned the craft, went to E-Trade Financial. And that was a time where stock brokerage was going online. Yeah, that was like the big time for E-Trade, right?

3:29Yeah, big time for E-Trade. And it was a great time for the financial sector to, it's a whole, you know, the FinTech 1.0, right? Yeah. So really helped to build that organization up. and then from there went on to Google with a little stop of my own startup that was a great experience as well. And then Google, which really changed my perspective of how the people team can work. And it was such explosive growth. And we were building the organization from the ground up. Really helped to shape my understanding. and then I went to Tesla and I got there. It was 400 people when I got there. Yeah, and then how big was it when you left?

4:14And it was almost 40 ,000 people. Jeez. Yeah, and it was a scrappy startup with a great mission and I learned a ton of lessons, got into the mix and many things we can explore a little bit. And then from there I went into healthcare and did another startup in digital health that got acquired by Tel Dock Health eventually. And then from there, I made the jump to Snowflake, and I just love it. It's been an amazing journey here. Did you always, like, know you wanted to be in HR? Because I certainly didn't. I didn't even know it was, like, a thing. You know, it's really funny, actually. When I was in high school, I don't know if they do this anymore, Jessica.

4:57I don't know if they do this anymore. But I took this – you take this personality exam or work style exam, and it tells you what kind of trade you need to go into when you grow up. And then you could – and I took it, and it said to be a business psychologist. That was the outcome of my little evaluation. Okay. Something all the high school kids took just to help them think about what careers they want to take. Yeah. So not that that helped me span or direct my career, but it really solidified in me. Like I like the people aspect and I like the business aspect and I like the intersection between those, the interaction of those and how that can really shape companies.

5:44And so I pursued that in college and went into human resources right out of college and it just felt so right to me. Yeah. Yeah. Well, that's more calculated a little bit than I was. I grew up in Kentucky. I went to art school. I was going to be an artist. I wanted to paint, but fell into the people thing. And really, because I view myself as a creative person, but I didn't know that business was so creative. And I found that creative outlet through solving business problems. And And yeah, and didn't look back, you know, and then got to meet people like you. So through your career, you've, I'm sure, worked at these amazing companies, but you've had your challenges, right?

6:36And this, you know, as I was kind of setting this up, this role is really challenging. What do you think about the role is the hardest? And what do you think has allowed you to navigate through all of the headwinds and the challenges that we face? So there's three principles that I realized were common themes in all of my experiences. Maybe we'll start there. The first is I realized how important, especially working for engineering organizations, how important the people analytics function is. So when I got to Tesla, for example, the team didn't really exist. So we built out a really smart data analytics, people analytics function so I could measure everything.

7:25So I could look at all the programs that I would launch and make sure I understood if they worked, they didn't work, and I would have clear understanding about it. The second theme that I've learned is around unfiltered communication, that you just need to provide open feedback along the way to course correct immediately. Don't guess around. Don't beat around the bush. Just really give great feedback with each other and then go to who you need to go to to get the answer. Like don't worry about hierarchy. Right. Right. And the next really important is that the HR team, the people team, needs to be just as innovative, just as creative as the rest of the organization.

8:08So it should go toe-to-toe with engineering, toe-to-toe with product to deliver amazing products and amazing information to the organization. So you have to have an organization that is curious and vivacious and innovative and tries things and experiments. So I realized that combination seemed to be the right set of ingredients to make me successful and make the team successful in each of those companies. Yeah. No, I talk a lot about the fact that you have to kind of think like a product innovator because that's what you're doing. And I think a lot of teams, and there's nothing wrong with looking and learning at what other companies have done.

8:53but there tends to be a little bit more copy and pasting instead of creativity and thinking a little bit more uniquely about your organization and your people and what's going to work for you because what worked for you at Tesla wasn't what would work for me at Netflix, right? But I think a lot of people don't experiment enough and don't test enough and maybe try to apply the playbook a little too often. Yeah, I agree with you that there's a lot of cut and pasting or I'll just use whatever I did in my last company and do it here. Right, right. And it doesn't work, right? It has to be different flavors.

9:37It has to be different combinations or you won't deliver the same products to the organization because that's not what's needed to make it grow. So you do have to be flexible, have to be curious, you have to experiment. But it's good to have those experiences because you can draw from them. But you definitely have to modify and customize per organization. Yeah. And you have to be willing to fail, right? And throw out the things that aren't working. And I think a lot of teams stick with things a little bit too long. And they're not really moving the needle for the business. and you got to be willing to admit when you're wrong.

10:19Yeah, and I totally agree. And that's why the magic of the people analytics function is so important because you can measure why it failed. You can look at the variables and say, well, it failed. Let me see what type of customization I need to do now to make it work better. And you can report that out. But if you just try something and fail without really measuring it or without really understanding what happened, then you're in the darkness. And you're not really making, because then you could fail over and over again, but you don't learn from them. Right. So I love the idea of failing, but also measure what you're doing so you can then tweak it in the right way next time.

10:58And you can show and demonstrate how you've customized it. And that is the language that engineering likes to see because they also do the same type of methodology. That's right. So as long as I can use that common language and show, all right, it failed, but here's, I know why I did it, and we're going to try it this way next time. Usually organizations accept that as, yes, that makes total sense, and thanks for iterating. Yeah. Well, let's talk about that. How do you set up a good people analytics team? How do you do that? What do you need? So that's a really good question. It depends on what's needed.

11:34So So usually what I've looked at is I grab people with economics background because I found that those people can help me with comp analyses and look at benchmarking data. So it really helps to have people with econ backgrounds. I love statisticians on the team. They help to look at probabilities and help me with thinking about experimentation. uh sometimes i brought in folks that are psychological they're experimental psychology because they know about doing experiments and so i bring those folks in as well and mathematicians so i do a combination of different backgrounds because you can apply them to all the different areas within the pupil function from benefits to compensation they can bring this level of expertise level of depth of analyses so uh so i've done that type of approach where i mix it up a little bit with different types of backgrounds and not necessarily people who have studied human resources or studied you know business but studied those type of analytical disciplines that can provide a fresh perspective on a certain problem and provide uh an objective opinion and i really appreciate that.

12:52So that's, I kind of start combining that and I would always cash in some of my headcount to hire those people on board to start that process. And they also would measure the recruiting function, you know, all the different functions. So it just helped me get better. And it really was a anchor to the people organization in each company. Yeah. And was this something that you learned from Google or is this just something that you kind of learned over time? so google highly highly reinforced this and working with lazio bach and working with the rest of the team out there we how important that people analytics function how influential it was to the to the world right so my time there was really spent solidified i did metrics in the past but they were very rudimentary in prior companies but really the time at google provided this expansive look at what people analytics could do and how it could have a great influence on the direction of the company.

13:49And that really stuck with me. Yeah. Well, let's talk about that. Because I think this is a challenge for many HR leaders is how do they form a people analytics team that's getting them great data and insights. But then how do you package that up and tell the story to the executive team to move the needle in terms of, you know, what, what really needs to happen, what matters and how do you get that alignment and how do you influence them? So maybe tell us the story of like some data that you were getting and you had to really get the organization at the top to get aligned. And, and then what was the strategy that, that you had from that?

14:38Yeah. So the storytelling with data is really important. And what I would try to do is, first of all, make sure all the data is very clean. Because what really is the Achilles heel is when you give data and they say, well, that's not right. The data is not right. So I can't do any analyses on this or any insights. It doesn't make sense. So you have to make sure your data is really clean and spend time doing that. And in multiple companies, I've looked at the data first and did auditing and make sure it was super clean. But then once you have that, then you can agree on, okay, the data is clean.

15:15Everyone agrees on it. Then you create some basic analytic tools that looks at the data. So everyone can see the same data and you report at the same time. So everyone looks at the same set of information. Then you do your insights analysis on it and start to make interpretations. What I usually like to do is if I have clean data and basic analytics, I can actually stop there for a minute and show this to the organization saying, look, we have measured and this is what we're seeing in terms of the data. And then everybody agrees on it. Then here's our interpretation. Here's the storytelling around it.

15:56And so you debate the interpretation. You don't debate the quality of the data, but you shift to how these insights are going to impact the business. A lot of HR teams I found, and I was part of that as well, get caught in trying to prove the data is correct. And you can never get out of that hole until it's super clean and you build credibility around it. But once you have that, then you can shift to analytics, to storytelling, to insights, to business decisions. And that's where the magic of HR is. I love that you said that. And I wholeheartedly agree. And I think when you're in the unproven stage, it's an endless fight and you're never going to win that fight.

16:41To your point, it's got to be clean. And then you're debating the real stuff and the stuff that actually will move the needle. So thank you for saying that. So I want to get to Snowflake in a minute because it's so exciting what you're doing there. But you were on this crazy journey at Tesla from 400 to 40 ,000. What was that like?

17:08So, again, the organization was a highly engineering mindset. Yeah. And kind of going back to what I mentioned, when I got there, I had five people on the people team. That was including all the recruiting benefits. Everybody, all included, it was five people. Yeah. And I said, this is not going to, based on where the company is going, this is not going to work in terms of scaling the company. So I had to really invest first in the team, and I built the analytics team first to make sure, just as I mentioned before, brought in people with econ backgrounds, brought in people with stats backgrounds, and really built that out.

17:48Then I built a recruiting organization that these hardcore recruiters were just ready to rock and roll. And I would say, all right, what I'm going to do is I'm going to build out the talent of the organization. We're going to hire lots of amazing people. We're going to measure how we're doing it. And I'm not going to build any part else of the organization. I'll just kind of run it in the background. And so what I tried to do is the whole concept was to build credibility that we could scale. We could scale intelligently. And we know the type of sources of talent that we need to go after, how they're doing in the company, the profiles of people that were working with the company.

18:30So I really doubled down on the whole talent portion of the people function to prove concept because it was all about growing. I mean, going from 400 to 40 ,000 in a few years. Yeah, how many years was that? That was around seven and a half years we got to the 40 ,000 mark. That's crazy. Yeah. It was a big one. And then once I built that function, it proved concept. We were growing the company fast. And then it gave me a little bit of social capital, gave me a little bit of goodwill, and I could build the other functions behind it. So that was just how I had to build the organization. And I had to figure all that out and do it fast.

19:12and make sure that we're delivering high quality people. But I think the point that you're making, and maybe this is something good for people to listen to, you were really building a foundation, right? So that you had a steady place to build from. You weren't just sort of building, right? Like a wobbly building, you were building like a strong foundation that you could build upon. And you focused on sort of the right things first versus trying to boil the ocean. You weren't trying to do everything. You were like, I'm going to do these few things, and we're going to do them really well, and we're going to prove it, and then we're going to build from there.

19:49And I think that sometimes when you're building, it's hard to figure out what not to do, right? And you're trying to do everything. So I think that that's important for people to listen to. Yeah, no, I agree, Jessica. And it goes back to what we mentioned before is, because, again, as an engineering organization, I had to build that people analytics team as a foundation, as you mentioned, so I could show what we were doing. So I could show how we were building the function, how we were, what we're going after, how we were thinking about the organization, all in analytics and insights. And that was, I couldn't come with feelings.

20:31Yeah. Because engineers need to see the data and how you're transforming. So that was really the huge, that was the foundation. Then I could build on top of that. It was really on that people analytics and talent function was the primary foundation to build a company, as you mentioned. It was intentional and it was sequential. Yeah. What for you changed in you as a leader from 400 to 40 ,000? What was different about you as a leader from small to big? Yeah. so you have to transform with the company yeah and there's a balance along the way of what you keep scrappy and innovative and what you build some structure around and you have to be really careful because sometimes structure can stifle creativity sometimes structure can be encumbersome so so you we were so intentional about as we scaled i would make judgment calls Like, I think we need to preserve this as a flexible environment.

21:40Let's not create a policy around it. Let's not create a structure around it. Let's keep it freeform and human-led. Right. And then for stuff that was around how we scale certain functions or how we scale operations, how we look at conversation programs within the people team, how we look at leveling, we had to build some structure around that. So when you hit 10 ,000 people, things change in the company. So we were really intentional about let's not do it for the sake of because we can do it, build a structure around it. Let's actually say let's not build something that just should be human decisions and should be free form and innovative, but structure that's only scaffolding that allows people to have some guidance.

22:25Right. Some parameters because that's what's needed at the time. So for me, the question was, in myself, I had to resist sometimes building those structures, even though sometimes in the playbook it says to do that. I say, you know, for this environment, I don't think it's going to work. I don't think it's going to be accepted. I think it's going to be rejected. And I think we're going to lose credibility. I think we need to keep this and preserve the human aspect here. But here, I think it's fine to build that because that's the backstop of all these other programs. And it's okay to build some scaffolding over here.

22:57So we had to be really careful about it and do a balance. No, and I love the human-led thing. I love that you're saying that because I do think that you do have to resist because it's so chaotic, right? And you're trying to control all this chaos. And I see all these companies, and it's all well-intended. It's all coming from a really good place because you're trying to help. You're trying to be helpful. But to control the chaos, you put in these processes and these policies that end up really stifling creativity and stifling growth and stifling innovation, and you're slowing the whole company down.

23:34And I love that lens of like, where do we need the humans to continue to lead and to have that freedom? And where do we need scaffolding? And maybe it's guidance. It's just a little bit of structure to help people get to the right place. So that's so important. Yeah. And one thing I'll add is even the nomenclature was really important. I avoided calling things policies and I would call them guidelines. So, or, you know, it really made a difference in terms of how it was received. Sure. Because a policy, oh, here's an HR policy. But here are some guidelines to help us do our job better. Yeah. It just, it's sometimes just the nomenclature actually helps with adoption.

24:21Yeah. It's so true. Yeah. All in the naming. That's right. That's right. Yeah. Cause I mean, mine was like, we're, it's a test we're testing and, and our engineers love testing and they would, they would do it. Okay. So take us to Snowflake. So tell us a little bit about Snowflake? Why were you so intrigued? And then I'm really excited to get into the work that you're doing there. Sure. So it was my dream to go into the whole field of AI to look at data. I just, I love it. And you see that this data theme throughout my life, how important it was. And now married with AI technology, it just seemed like a canning store to me.

25:04And I was so honored to be to be chosen to come and help at this phase of growth at Snowflake. So it is a company that really focuses on data analytics and AI and enabling companies to have this AI capability. And I look at this also from an HR perspective, from a people perspective. We need to be just as innovative, just as creative in this space as our product and engineering teams are putting amazing things out into the world. So I've looked at the last year and so that I've been here, how do we create a whole set of approaches and guidelines and applications and capability to enable the people team to amplify their work that teams have not been able to do before in other companies but that we have access to so many tools.

26:03So my focus has been on enabling the people team to grow and innovate as fast as our engineering organization and really deliver some amazing products. So what are you doing? How did you decide what to focus on, what not to focus on, where to start? I think a lot of companies are struggling with this and a lot of HR leaders are struggling with this because all of a sudden now they have to be the AI strategist, not just the HR strategist, right?

26:34And nobody's figured it out, right? And so what's your advice in how to think about it, where to start, what to try, what not to try? So first we came out with a framework. And the framework, there's three parts of the framework, and this helps to build some guidelines around how we're going to implement different programs. The first one is all the tasks that are repetitive, we're going to automate through AI. Things that require creativity, we're going to augment through AI and have that partnership. And things that need to stay empathetic and have judgment calls and are human-led, we'll preserve for the human.

27:19We won't, we'll avoid using AI. So having that framework, then we're able to say, okay, let's look across all of our programs and all of our tasks that we're doing. Let's look at some small wins we can do and start to look at the repetitive tasks and start there. What's a good example of that for you guys? A great example is our job description generation. so we're scaling to like other companies are and sometimes it takes 45 minutes to an hour to write a job description you look at some ones in the archive you pull it in, you put it in a doc, you're editing it, it just takes time so we said, and all the recruiters are working on it, all the managers have to review it and edit it there's hours and hours and hours of human time applied to that process so I said let's try Let's try to build a job description generator that can get it done in five minutes.

28:19So basically, we build an application on top of our Snowflake capability, and you put in a couple of keywords, and it takes all of the job description in the archive. It looks at benchmarks. It looks at data, all the data. It pulls it all in, and within five minutes, it really takes 10 seconds to do it, but five minutes for you to put your variables in. And then it comes out with a beautiful job description, exactly like 99 % there. Everybody looks at it, blesses it, and we post it within 15 minutes. That's already saved several years of human time. I'm just repressive tasks of doing that over and over again.

29:00I hated writing job descriptions. Recruiters love it. I hated writing job descriptions. It was simple, and it was non-threatening to the team. Right. Well, let's talk about that for a second because, again, as you were talking, is sort of thinking, this isn't taking somebody's job. There's not a professional job description writer. But now you're taking the recruiter's time and the manager's time, and they're actually able to focus on the more important stuff versus spending an hour writing a job description. So it's amplifying the work, not taking a job away. our philosophy is that ai is going to help amplify all of those components that are repetitive tasks really do it really well and leave you time to connect to build connections right to talk to candidates to do the interviews to really engage on the human aspects of it to do much more value added time, value added, you know, work.