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Many data teams fall into a common trap: they become a reactive service desk, fulfilling requests instead of driving strategic business value. This tactical focus often leads to ballooning infrastructure costs, an unclear return on investment, and ultimately, vulnerability during budget cuts. Data leaders and executives need to shift their teams from a cost center mentality to a genuine strategic partnership that directly impacts business outcomes.
In this episode, James Winegar speaks with Lindsay Murphy, Director of Data at Hiive. With 13 years in data across diverse industries—from marketing tech to healthcare to pre-IPO stock trading—Lindsay offers direct experience building data functions from the ground up, navigating growth, and fostering effective teams. She also hosts the podcast “Women Lead Data,” giving her a unique lens on leadership development and diversity in the field.
They discuss Lindsay’s journey into data leadership, the evolving landscape of data community building, and concrete strategies for advancing data professionals. The conversation provides practical advice on how data leaders can ensure their teams remain strategically aligned, demonstrate measurable impact, and avoid becoming just another technical support function, especially when hiring and building out a modern data stack.
Key Takeaways
Data Teams Must Anchor to Business Outcomes to Prove Value
Many data functions operate as cost centers, building pipelines and dashboards without clear strategic alignment. Lindsay Murphy stresses that without a vision for how data directly supports company objectives and measures progress, teams risk ballooning infrastructure and roles. This lack of discernible ROI makes them prime targets for cuts, a trend seen in recent economic shifts.
Data Leadership Demands Business Context and Relationships
Moving from an individual contributor role to data leadership isn’t about deepening technical expertise alone. It requires developing a robust understanding of the business’s core mechanics and building strong relationships with other leaders. Leaders must proactively identify key strategic questions, use data to construct ‘metrics trees’ that map to revenue, and avoid getting trapped in a task-taker mentality.
Effective Hiring and Career Advancement Now Hinge on Genuine Networking
The influx of AI-generated applications has significantly diluted the quality of resume piles, making it harder for qualified candidates to stand out and for hiring managers to identify talent. Lindsay highlights that in this environment, direct networking—through meetups, conferences, and even low-effort one-on-one virtual coffee chats—provides a critical edge. These authentic connections offer a path to roles and talent that traditional application processes now obscure.
Related: CorrDyn helps organizations build strategic data capabilities and develop high-impact data teams. Learn more about the three drivers of data-savvy organizations and how our expertise in data cost optimization ensures data investments deliver clear ROI.
Full Transcript
Jason: Welcome to Data BS, the show dedicated to tackling the big questions impacting the world of data and ML AI without any of the BS. My name is James Winegar. Each week I sit down with guests from across the data ecosystem to unpack how they’re shaping their businesses or the businesses of others through their real-world application of data engineering, ML AI, infrastructure, analytics, and more. No fluff, unfiltered, but slightly edited to remove noise, this is Data BS, let’s get into it.
James Winegar: Nice to have you on the show Lindsay, appreciate you coming on very, very short notice.
Lindsay Murphy: Yeah, thanks. Sounds good. I’m Lindsay Murphy. I’ve been working in data for 13, 14 years now, I don’t know, the year just changed, so I always have to count up. I work at a company called Hiive, the director of data there, and I lead a team of analytics engineers right now and we’re looking at growing our team. Excited to be on the show today.
James Winegar: Okay so everybody make sure you apply to the job on Hiive because we’ll talk more about that. We met at the Small Data Conference, we were hanging out at the speaker get-together and you were like, oh, we should — you do a thing, I do a thing, we should come do something. You do this podcast for Women in Data, so that’ll get sprinkled in as we talk through stuff. Could you share a little bit about how your career developed into data leadership and what initially drew you to the data field itself?
Lindsay Murphy: That’s a great question. Maybe I’ll start there and tell the story forward. I don’t actually come from a technical background. I’ve been in data for a long time — one of those career paths where I did something totally different and then fell into data. I actually took psychology in undergrad, was where I was focusing a lot of my time, and I spent a lot of time doing statistics. I was always really interested in math and physics and those types of problems, so always using data. In stats, or in physics, we had to do a lot of behavioral statistics. So it was relatively small sample sizes, trying to understand what people are doing and why, and I was always really interested in that side of things. At the time I worked for a development lab studying pro-social behavior in toddlers. Really interesting something that I was doing in university, and one of the challenges that we had was recruiting children for studies. It’s very hard to get people to come in. I was in a smaller city in Canada and to get a study done you needed to have at least 50 kids in your sample size, which meant you would probably have to do studies with 200 kids because kids are chaos and you have to throw out half your studies. Recruiting was a really big problem. What we used to do at the time when I joined was just cold call parents. You would buy a list and cold call parents. You have kids, how weird would that be — some random girl calls you up and is like, hey, do you want to bring your kid in for a study at the university? So that was not working super well, and this was right around the time when Facebook had launched outside of the universities. I was obviously on Facebook, I was a kid in university. I created a Facebook page for the lab and started putting posts out for moms and being like, hey, you can come and bring your kid in and they get to do a cool study and you get to help research. We were inadvertently doing social media marketing. As part of that, I started to see the analytics on the backend of Facebook. You could see what posts were doing the best and how many people signed up for your page that week. I was like, oh my god, look at all this data that you just get for free, and we’re doing all this stuff to try to get data about humans to be able to study and do our statistics on that. That was the catalyst of me getting into the digital tech world, and then I ended up not pursuing a graduate degree in psychology and I ended up starting at a startup which was doing social media marketing and I joined as a data analyst, an entry-level role. Learned SQL on the job, was working with an Oracle database at the time, learned a bunch from the other people who were on my team, so I probably was a little gratuitous with my resume. I was like I know how to use Excel — I barely could use Excel at the time. I learned everything on the job and then the rest was history. I moved around to a lot of analyst roles, different analyst roles at the time. Over the last five or six years I’ve moved more into leadership roles, started to manage data teams. I was the first data hire at a startup called BenchSci a couple years ago. That was really how I kind of got in. I was an analytics manager and then I started to build out the team and then I got promoted into a director role and I started to build out the different capabilities and build a modern data stack. At the time I didn’t know what I was doing. A couple other experiences past that gave me more experience in using tools like dbt, lots of different data warehouses, learning more about ETL and pipelines and orchestration, all that kind of stuff.
James Winegar: It’s a pretty traditional analyst path, right? Were the businesses very different from each other? Social media marketing in the first one, and then I think I heard a science one in there, and Hiive does — I don’t remember what Hiive is off the top of my head, but Hiive does some stock RSU movement or something.
Lindsay Murphy: BenchSci, yeah. The industries — I did SaaS startups for quite a few years in different industries in the healthcare space. I actually was the analytics manager at a big pharmaceutical company for two years — that was an interesting experience. I thought, oh big company, they’re going to know everything about their data and they’re going to have everything figured out. It was the exact opposite. It was a huge dinosaur of a company where everything was so slow. They were on on-premise databases, crazy stuff like that.
James Winegar: Going between different companies as a data person is really important because you learn how other people think about their data or how they think about what do they even know. A lot of times you’ll see companies where they say they use data and literally there’s nothing in place. There’s really just gut feeling that’s happening, the use of high-level metrics, but can you trust those metrics? Blah, blah, blah. We’ve both probably been at places where there’s not a dbt pipeline or something like that. There’s no data testing, there’s no anything of these things to make sure that you’re good.
Lindsay Murphy: 100 percent.
James Winegar: Let’s talk about Hiive. What is Hiive? What do you do within Hiive as a data leader, and how are you trying to build out and scale your team as Hiive grows, because you’re a startup in a growth phase.
Lindsay Murphy: Yeah, for sure. I joined Hiive in August, so I’m still relatively new to the company. Hiive is a private marketplace for pre-IPO stock trading. Essentially if you are an accredited investor, you can come onto Hiive and if someone has listed stocks that you want to buy, you can try to buy those. You can bid on their listing and try to go through a transaction process with those people. Or if you’re someone more like myself — I’m not an accredited investor, but I have stocks in a company that I used to work for, I bought those shares when I left. I could potentially go on Hiive and sell those. If somebody’s interested in buying them, if I have enough to sell and somebody wants to buy them, you could do that transaction through Hiive. Really interesting — I had no idea that this existed when Hiive reached out to me about the opportunity. I’ve learned a lot about the industry, I’m still learning a ton, but it’s a really interesting way for people to get liquidity from traditionally incentives that you get as an employee at a startup. Most people don’t stay at a startup for 10 years and the average time for a company to IPO has increased significantly over the last 10 or 15 years. It used to be a few years, now some companies never IPO, we know how many don’t make it. Most of the ones that do are taking 10 or 15 years to do it. Nobody in this day and age is staying at a company that long. To live through all of the different phases of a startup for that long is a pretty amazing feat. A lot of people are looking for ways to get liquidity from these stocks, and when you leave a company rather than having to wait 10 years potentially to get value from your stock — maybe you want to buy a house, you want to buy a car — you may be able to turn those stocks into liquidity in the short term.
James Winegar: For people who were in the series A for Databricks and they’re in their series Z or something now, a bit of liquidity for those people because when is the exit of that happening? They’re gonna try and hold it out as long as possible for more favorable conditions for an IPO than right now. I’m guessing right now, you’re the data person, but I’m guessing right now Hiive’s got a pretty big increase in transaction events that are happening.
Lindsay Murphy: A lot of the AI stocks as you can imagine, or AI companies, there’s a lot of buzz around a lot of AI companies. There’s always interest in SpaceX, things like that, but one of the things about Hiive is essentially a marketplace is that you have to balance supply and demand. That’s a lot of the things that we’re interested in learning with data — how do you figure out, we have all these companies that people are listing, are there buyers, or the other way around, are there people who are looking to buy things that we can go find supply for.
James Winegar: Then you have to also find sellers and find investors who are willing to purchase these pre-IPO RSUs or whatever the object of trade is. That’s actually really interesting. I could ask you a lot more questions but let’s — Let’s talk about community building and things like that. We both started working pre-COVID, it’s a post-COVID world, has changed the dynamics a little bit. How do you feel networking and community building in data in particular has changed since COVID? What are these events and meetups, how has that helped with connections, and I really would like to hear how you feel about it as compared to pre-2020 as well.
Lindsay Murphy: It’s so interesting because I feel like where I was in my career when I think of pre-COVID was very different than where I am now. It’s a factor of where I was personally growing at the time. I don’t know if this is just a factor of COVID — it could also be a factor of me growing in my own development. When I think of pre-COVID, the only type of networking that I really did was here in Toronto. I would sometimes go to events and I would look up meetups and see who was doing stuff and I would go, and sometimes they were okay and sometimes they weren’t. Half the time I would go to these data meetups and I would have no idea what people were talking about. Data roles are so broad that they might be talking about something super deep in the weeds on ML or something and I don’t know all that stuff — I’m happy to go and listen. What I found was I just liked to go and talk to other data people. Post-COVID, conferences like dbt Coalesce happened online and it was fully virtual. It was a way to go to the conference without going to the conference. That was the first time where I was like, oh my god, look at all these things online that I can get more involved in. I used to do a lot of watching webinars and reading people’s blogs, but I didn’t really do things where it was engaging with other people. Around that same time I found the dbt Slack community as well. Talking to people online and just having more of these easy connections with people, it’s more personable than this formal networking thing that you have to do, like mentoring or something. As that grew after COVID, I started to be interested in going and speaking at conferences. I spoke at my first conference which was also dbt Coalesce in 2022 in New Orleans. That was my first experience going, being a speaker, meeting all the other speakers. That started to open up the world a little bit of, oh, this networking thing is actually pretty cool, you can make some friends from it, you get to go to conferences, you can travel, if you take advantage of it. Maybe you’re not able to travel for various reasons — you can do it online, go to online conferences and meet people on different Slack communities. That blew the doors off of networking for me. The company that I was at when I spoke at Coalesce the first time was a company called Maple, and then I left to go to a company called Secoda and I was actually in the data vendor side of things, so I was going to conferences all the time. That was part of the role — speaking, doing webinars, meeting with different data people. That was another level of that as well where I got to meet more people and just form relationships.
James Winegar: So did your first presentation at dbt Coalesce help you get the role at Secoda?
Lindsay Murphy: I would say maybe, yeah. The other piece that was happening was part of the dbt Slack — I did meet someone. We were talking in the Toronto channel about hey, let’s do a meetup in Toronto. Nobody’s done a meetup since COVID. It was actually 2021 that we did our first Toronto modern data stack meetup, and we were doing them monthly for a while. It’s become a little bit more difficult to keep up that cadence. But we have I think 2,800 people in the group or something. Oh man. Not that many people show up every month obviously, it’s maybe 150 people but…
James Winegar: Still a good group of people to show up.
Lindsay Murphy: There’s a big community in Toronto. That’s been really cool too, because then you’re meeting people locally. Because I didn’t love the meetups I was going to, it’s like let’s just make our own meetup.
James Winegar: What has your experience been as a woman in a data leadership role, and how do you think the industry can better support diversity at the higher level?
Lindsay Murphy: Obviously this is a topic I could talk about all day because I have my own podcast where I talk about this. I have a podcast called Women Lead Data where we dive into this topic with different women leaders. It’s been an interesting challenge in a lot of ways. I grew up with a single mother, and in my own background and how I grew up, I’ve just never really considered that a woman can’t do something because my mom was always the strong mom that I looked up to and she did everything. It was a way for me to not really compare myself to men in a lot of ways. Being naive to a lot of things was probably helpful in the beginning. Now that I’ve gotten to higher levels, I do start to see things where you see bias and you see how people talk to women differently or treatment that I’ve gotten from different leaders that I’ve worked with. It is definitely something to keep in mind, but I would say encouraging people when they’re early in their career — if you can, don’t let that be the thing that you focus on, that you’re different, that you’re a woman and that you have to do things differently, because sometimes that can actually create more issues for you. Some things that I’ve dealt with is this concept of oh, she’s a woman so she’s not technical. One of the earlier roles that I applied for — the analytics manager role where I was the first data hire — they really wanted to hire me because I did a great job on a case study, I convinced them of here’s all the things that we could do. But their VP of Engineering was like, but she’s not technical, so how is she going to be able to do any of the execution? It was like, well, why did you assume that? Where did that come from and what was the assumption behind that? That’s always going to be something — there is bias there that not being technical. I know many women in my network who have PhDs and backgrounds that would impress just about anyone and they still get those types of questions or people think that they’re not capable just because they’re a woman. Those types of things are always things that you’re going to run into. The reason I started the podcast was because I went to a conference and I was at an evening event and I was looking around the room, and it was actually my colleague who is a man who pointed out to me, he’s like, there’s a lot of dudes here. I was looking around, I’m like, there is a lot of dudes here, why are there so many dudes? Then I started talking to people and I realized…
James Winegar: Were you the only woman there?
Lindsay Murphy: I wasn’t, but there was about 200 people at the event, maybe more, and I could count on my hands the number of women that were in the room. There was actually a woman that I went over to and I started chatting with her and I was like, oh, where do you work, what do you do? And she’s like, oh, I’m actually so-and-so’s wife, I’m just here to hang out and come to the conference. I was like, perfect, so you don’t actually work in data. She was lovely, but it was just one of those moments. I started to realize as I talked to more people at this event in particular, a lot of people were co-founders, CEOs, executive level people. That was when I started to dive into it a little bit more. I always knew that there were less women in data than men, but if you actually look at stats — I don’t know if you do show notes, but I’ll look for this link afterwards — I think it’s called Harnham Data Study or something like that. They do a study of all the people who work in data roles every year and they look at gender diversity and all these different things, and they look at the leveling that people are at. As you go from individual contributor roles all the way up to executives, you can literally just see the drop-off at each level of women as the proportion of people in those roles.
James Winegar: We’ll definitely get that in the show notes. You can’t have a data podcast and not provide the data to back it up.
Lindsay Murphy: It’s interesting, right? Even for myself, I’m at the director level now and there’s plenty of stats to say that very few women make it past director into VP into executive level roles, and those are the goals that I have for myself. It’s challenging, it’s something that you have to figure out ways to put yourself out there and make sure that you’re being the best that you can in your field, and just knowing that if you can give back to the community or inspire other women to show them that it can also be done. That’s the idea behind the Women Lead Data podcast.
James Winegar: I think it’s awesome — most of our team leads are women because they’re doing good work. That’s what our criteria is for people to go up. I think it’s a problem when people are like, oh, you talk more, talk less, talk more, talk less, very conflicting feedback.
Lindsay Murphy: Yes. I have a lot of experience of that.
James Winegar: Be more assertive, all those types of things. Those are not actual tangible measurable things honestly. What is the outcome that that person has driven? That’s how we should be measuring people, not on some soft measure, especially when we’re talking about technical people. Your analytics engineers on the team — there’s really no point to measure that unless they’re successfully engaging with stakeholders. That’s really the measure.
Lindsay Murphy: It’s about giving people opportunities. A lot of times if you don’t give someone an opportunity or you don’t take a bet on someone, then they’re not going to be able to show their level of experience or their ability to grow into something. I’ve thought a lot about allies — what makes a good ally and how do you know someone’s a good ally, because somebody can tell you to your face, oh, we believe in women in data, we believe in supporting women. I’ve literally gone through this experience…
James Winegar: But they don’t do anything to support it.
Lindsay Murphy: They don’t do anything, or they end up being someone that really doesn’t support you, and I’ve actually had that happen firsthand where I’ve thought that I’ve joined a company and joined people that were going to support me and they really didn’t when they went the opposite way. I can remember specific people in my career who’ve given me opportunities, like my manager who promoted me from an analytics manager to a director role — I had never had that experience before and they took a bet on me. Another manager who encouraged me to go speak at Coalesce conference — that was the first time I’d ever spoken, and he actually got up on stage with me and did the talk with me, which was a super helpful way to support. Those are the types of things, those are the behaviors that show you’re there to support someone the same as you would support a man. At the end of the day, it’s just giving someone the same opportunities that you would give to anyone on your team.
James Winegar: I think it’s funny because I was just thinking about the same situation with men, right? Not everybody’s had a director level role. You have to have your first director level role at some point, or manager level. You don’t come out of high school or undergrad and you’re a director level person, you have to build up the skill and somebody has to be willing to take the risk to put you into the position, or you’re at a smaller organization where you wear all the hats and then you grow with the organization. That leads me into a different question, which is — as a woman in data, do you feel like the opportunities to be that first or second data hire are more limited as compared to men?
Lindsay Murphy: That’s a good question. It hasn’t been for me, because in that particular case somebody actually reached out to me, they found me on LinkedIn and it worked out. But I think the first data hire roles can sometimes be difficult to find. They’re not necessarily…
James Winegar: If you don’t have that network already established within the hiring industry.
Lindsay Murphy: Yeah. I do think that those come from networking more than anything. For any women listening, it is a great role, it’s definitely nerve-wracking, and I do say you want to feel like you’re at the top of your IC game when you’re going to step into that kind of a role, and also that someone who is managing you is going to support you. At the time I reported to a CMO, chief marketing officer, so he knew some data stuff but he didn’t really know the breadth of everything. It was helpful but it made it challenging in a lot of ways because I couldn’t really lean on him to get support for different things. Whereas in the role after that I was reporting to a VP of Data and data security, and he knew the field, he knew dbt, so it was very helpful to have someone who had that technical background that you could bounce ideas off of. Stepping into those first data hire roles, you want to assess the company. How much are they actually going to invest in data? Are they actually going to give you any budget to do anything, because if they’re not, you’re going to have a hard time. And how are you going to be supported — how is data going to be used at the company, is it just going to be you become a support function to everyone in the business, or are you going to have a seat at the table to talk data strategy?
James Winegar: Are you a cost center? Are you a dashboard monkey? And there’s no strategy around what are you trying to do? Do they want to treat you like that or are they trying to do more than that? Trying to move towards making sure that you’re at the table, you’re able to even listen to the conversation just so you can know what are people thinking about. As a person doing data, I get more insight from listening to people talk than talking myself typically, because it’s like, oh, that’s what you’re thinking about. What if we provide that information, because we already have that but you don’t know that because you’re not in the database or whatever. In the next monthly meeting or weekly or whatever the structure is, you come with that thing and you’re like, hey, okay, let’s talk through this, does that change anything? If you see change in response to that, you know that the organization is supporting the data function or at least values it in some meaningful way.
Lindsay Murphy: That they’re actually able to use it. A lot of times it’s just that people have been hacking things together with data for so long, by the time they hire that first data person, when you get in there it’s like a fire.
James Winegar: Yes.
Lindsay Murphy: It’s like a pack of wolves on top of you, it’s a little bit overwhelming. You do have to be ready to push back and say no to people and just do it in a way that you are focusing on the leaders of the business and you’re servicing those people, because you’ll learn very quickly a data team gets cut quickly if they don’t feel like there’s impact.
James Winegar: My recommendation to people who are listening is focus on what’s the most valuable thing based on your understanding of the business, and I think we’re going to talk more about that type of stuff.
Lindsay Murphy: That’s the important thing too — to understand the business you have to be able to speak to the leaders and you have to learn the domain. Those are soft skills, those aren’t technical skills. I think they’re the most important skills. SQL is not rocket science, you can do SQL.
James Winegar: Don’t overcomplicate things and you’ll be able to do most of the stuff. Half the time 80/20 is better than 100/0. For those looking to advance into a leadership role in data, what’s the primary advice you would give to them?
Lindsay Murphy: That’s a great question. Definitely you want to make sure that you’re learning the business and that you’re developing relationships with the other leaders in the business. The worst thing that can happen to you as a data leader or someone who wants to be a leader is you’re getting caught in the tactical stuff that’s happening on the ground. A lot of data people end up falling into this task-taking mentality of, oh, someone asked me to do this thing, I have to do it — we were just talking about stack-ranking all your priorities. Maybe you just don’t do 50 percent, 80 percent of the things that people are asking you, because if everybody’s coming to you with all these requests, maybe building them a data model and giving them access to it and teaching them how to fish might be a better solution to that problem. It’s really working on your skill sets of higher-order thinking. Don’t just focus on somebody asked me for this thing, I need to do that. That’s part of IC — being an IC is someone set a task for you, you get it done, you do a really great job of it. You need to step into the next piece, which is not necessarily just questioning what people are giving you, but focusing more on the business. Think of yourself as a shareholder in the business — you want to make sure there’s alignment and that you’re rowing the boat in the right direction, you’re aligned with the leaders of the business. Focusing on your strategic thinking, your organizational understanding, and developing relationships with people probably is the easiest way to do that. If you’re having coffee chats with different people and you’re understanding what are their objectives, why is this their priority, how does that align to the company strategy, those are going to be the things that help you focus on the right areas.
James Winegar: Bigger companies tend to have some processes to support this as well. There’ll be standard leadership training programs and events.
Lindsay Murphy: Startups not so much.
James Winegar: Bigger companies tend to have some way to get executive level discussion — lunch meetings and things like that, right? If you can really take advantage of that meeting to understand what they think about, how they think about it, what’s important to them, then when you go back to doing your IC role or your manager level role you can say, hey, I understand what’s important based off of the person several levels up from me. Usually there’s skip-level meetings and things like that, but every organization is different, you have to learn how to navigate within the organization and there’s always ambiguity. One of the other things, especially when it gets softer — you’re not just IC — is being able to handle the ambiguity, try to make structure out of ambiguity. If you can make structure out of ambiguity, you have something to aim at.
Lindsay Murphy: For sure. That immediately makes me think of metric trees — are you familiar with that whole discussion that goes on? It’s really, how does your business make money? What is the North Star of your business? For most companies, it’s going to be revenue or profit or something like that. How does your company do that? Use a metrics tree to break that down, and from a data perspective you can become someone who can then teach others in the business — these are how these levers are all related and you can provide data to measure those things. That conversation becomes a lot more, I don’t want to say scientific, but a little bit more of, here’s the formula on paper of how this business works. It’s pretty cut and dry. It’s maybe complicated in some cases, but at the end of the day, that’s how the function works of the business.
James Winegar: So how do you go about hiring typically? Are you doing in-person networking? Are you doing LinkedIn? Sounds like Slack’s kind of a big thing for you too. Dig into it.
Lindsay Murphy: It’s definitely been a little trickier because Hiive is located in Vancouver, actually. I’m in Toronto but Hiive is based in Vancouver and we like to have as many people there in person as we can. Hiring geographically is a different challenge that I hadn’t really had, because a lot of hiring I’ve done has been over COVID, so it’s either been hybrid or remote. Being that my network is a little bit more broad now — if I go to the dbt Slack community, majority of people in there are probably in the US, there are not that many people in Canada, there are some for sure, but that’s the challenge — my network has to be geographically centered. It does require you to be creative in how you’re reaching out to different folks. We obviously get a ton of applications. AI has really made this hiring process much more painful for the hiring manager as well as people applying. When I first started hiring five years ago before all this AI stuff came out really to the public, we would get maybe a couple hundred resumes to one role. The quality would be probably about 15 to 20 percent of those would be good resumes that you’d want to talk to someone on the phone and eventually get through the funnel. Now, I don’t know if it’s the amount of effort or just this easy apply button on LinkedIn, but the quality of resumes that we get has gone down significantly and we’ve gotten way more. There’s thousands of resumes that you get now. You can tell that people aren’t really looking at the job, or people are using ChatGPT to write their application and their response. It’s really difficult because it makes it difficult for people who are qualified for the role because their resume might get lost in the pile, and then it’s also difficult for hiring managers because it’s just exhausting to try to sift through all of that application. We’re very lucky at Hiive, we have an amazing talent team that help with some of that part of it. But it definitely makes it tough. The assessment part of things is challenging too — a lot of companies you want to do some kind of a technical assessment and people are using ChatGPT to do that now too, so it’s an interesting time to be hiring. And an interesting time for folks who are applying. I encourage you, if you are applying to jobs, it’s okay to use ChatGPT to do stuff, but don’t try to get it to do everything for you. Get it to help you, get it to support you, but at the end of the day, if you’re applying to a job, we’re probably going to ask you why do you want to work at this company, what do you know about Hiive? If you can’t answer those questions, that just tells me that you’re just spraying and praying your resume everywhere and that you haven’t really thought about the role of the opportunity specifically. I could rant about AI and hiring for a long time but we won’t go down that path.
James Winegar: I think what this is really illustrating is that networking is very important if you’re interested. Try to go to events or meetups or various things, conferences, etc., get in front of people who would be hiring managers, talk about why you’re interested in what they do and things like that.
Lindsay Murphy: The other thing too is sometimes if you aren’t looking, beefing up your LinkedIn profile is really important, because when I’m dealing with this inbound that’s a bit of a mess, I go out and source people. Especially I will also go and source women into my pipeline, because of the thousands of resumes that I get, 80 to 90 percent of them are from men. For me to ensure that I’m getting balanced teams, I do go out and source — I look to source women into the pipeline as much as I can. I’m using LinkedIn Recruiter for example, and with LinkedIn Recruiter you can get down to specific keywords and past places that people have worked at, and it’s kind of crazy all the stuff you can do in the backend of LinkedIn, but it’s a way for your profile to show up. If you are open to work, make sure that you put that on because that helps recruiters to find you. Your LinkedIn is your social profile, your resume potentially, that recruiters might be able to find you. That’s how I’ve actually been reached out to for several of my jobs that I’ve started at, where people have found me on LinkedIn because of different keywords or things that I had there, so that’s another great way to get found.
James Winegar: What do you think is the best strategy for people who are trying to make those meaningful connections?
Lindsay Murphy: Networking has gotten a lot easier I would say for introverts. I don’t say that as an extrovert to call introverts out, but even for myself, sometimes going out into a room full of people I don’t know, even though I run a meetup, is still difficult for me to do sometimes. I have to take a deep breath before I walk in the room because I’m like, this is going to be exhausting, I’m going to have to talk to a lot of people, I’m going to have to say the same thing over probably about 50 times. That’s just not for everyone and I totally get that. This post-COVID world — I don’t know how many one-on-one coffee chats I’ve had where people reach out on LinkedIn and said, hey, I’ve seen you around on LinkedIn, or I know so-and-so, would you be interested in just setting up a coffee chat? I’ve had much more of these one-on-one connections with people. Those have turned into people that now I reach out to when I have questions or I want to connect again in the future. Those are ways to build relationships that are relatively low effort and low touch, and they can end up being some of the closest people that you rely on. I think that is much more acceptable — in the pre-COVID world just reaching out randomly like that might have been a little bit different. I’m sure people were still doing it, but people are so open now to just hop on a Zoom. Working remotely most of the time, I am in Vancouver once a month for a week just to be in person with my team, and those weeks are so valuable because there are things that you miss. There’s definitely things that you miss working remotely. But there’s other benefits — you get a lot more quiet and focused time that you wouldn’t get otherwise. I do think it’s almost made those in-person interactions so much more valuable, and when you do have them, you want to be there and present. Sometimes we’re on our phones and stuff, right?
James Winegar: Earlier at the very start we were talking about how data is not necessarily a technical function in the sense that all you do is technical work 24/7. Data’s not a technical function, but a strategic one. How do you think organizations should approach data to unlock that strategic value that’s in it?
Lindsay Murphy: For sure. This definitely — my viewpoint comes from my background, that I didn’t come from a technical background. I came from trying to understand people and using data to understand behavior. I also spent a long time at different marketing agencies and doing marketing strategy. That really crystallized this idea of data is something that helps you measure strategy and data should be a strategic part of your business. But I’ve also seen firsthand when it’s not. As much as I believe in that, it’s very easy to just fall into this pattern of let’s build all these pipelines and data stacks and do all this stuff, and then you’ve got all the stuff that’s built, and sure I’m sure people get value out of it, but is that really the most valuable thing you could be doing? I would encourage data people to think more about what is the company strategy, how can you use data to support those objectives, and then measure progress against those and focus your resources. Because if you’re not doing that, your data team is probably ballooning your infrastructure, ballooning your assets, probably ballooning the roles on your team to just do more stuff that people are asking you for. Eventually, some leader is going to go, well what’s the ROI that that team is delivering? If it’s not very easy to discern that it’s positive, you’re probably going to be hit with budget cuts, different things like that. We’ve seen that a lot over the last little while where the economy not doing as well, companies had to cut, and data teams started to go for the first time in my career — hearing about data people getting laid off. I think focusing on being a strategic function and thinking about how am I a strategic partner to the business, and then planning your data activities based on that — the technical side is the execution of it.
James Winegar: For the individual contributors that are listening, even if you’re a data engineer or an analytics engineer or data analyst or whatever, you should really understand what’s core to the business. What are the — this metrics tree stuff we talked about earlier — what are the key metrics that I want to keep track of? How do I support that? Everything else is really tangential to that activity.
Lindsay Murphy: Yeah.
James Winegar: Which leads into the next question, which is tools in the modern data stack — there’s thousands of tools. The modern data stack solves a lot of problems that historically were engineering process, but how do you actually stay focused not on tools but the measurable business outcome?
Lindsay Murphy: Again, it’s focusing on the outcome that you’re trying to drive first. Even — I fall prey to this a little bit sometimes — people will reach out with the newest cool thing and you’re like, oh, I want to try that, or you’ll spend all your time making your dbt project run really nicely and it’s like, okay, it runs a bit faster, but what is the actual impact of that? Did we save 50 bucks a day or something? Does my business even care about that? Again, these are the things where if you don’t know as a data team what you’re trying to achieve and you don’t have a vision or a data strategy around that, you can’t really make sure that you’re focusing your efforts in the right place. For smaller teams where your company is maybe more cost-sensitive, look towards open-source tools, try to build lean and deliver as much value as you can with as little as you can. And then try to enable others to do things. If you’re building a self-serve stack, set up dbt, set up a warehouse, start getting people access to it and teach them how to run their own queries on well-modeled data. That’s going to get you a lot further than you taking every single request that somebody has, like hey can you pull this data for me, can you build this dashboard. You can be a one-person show and get a ton done with something like that.
James Winegar: Yes, good semantic model — you enable the 50 other people at the company to do most of what they need so they don’t have to come talk to you. Then you’re not a router for a bunch of nonsense where it should take somebody five minutes, but they gotta bring it to you because there’s not enablement for them. It’s great talking to you. Let’s get into what are the opportunities at Hiive and how can candidates reach out to you?
Lindsay Murphy: We do have an open role right now for an analytics engineer, it’s an intermediate analytics engineer level. You can apply through Ashby I think is our portal, or if you just go look at the Hiive career site, you can take a look at the open roles there. We’re looking for someone who has hopefully some dbt experience but is very well-rounded and well-versed at least in SQL. And then this concept of data modeling that we didn’t talk about but it’s a bit of a lost art. Trying to find folks who can think a little bit about dimensional data modeling. If that’s not a skill that you’re familiar with, if you work in data you definitely want to spend some time learning that. We’re seeing a lot more people talk about data modeling these days. If you’re interested, definitely apply through the portal. You can find me on LinkedIn, I talk to people there a lot as well, so reach out.
James Winegar: Lindsay, you also do your own podcast, Women Lead Data. Can you tell us about your podcast and why people should tune in?
Lindsay Murphy: Women Lead Data — you can listen to it on Apple Podcasts and Spotify. I think both men and women listen to the podcast, which is great. I try to do it every couple weeks, I’m still trying to find my new cadence now. I have guests on who are in different leadership positions and they tell their story of their career, how they got there, and then we usually dive into a different topic. Give it a listen.
James Winegar: All right, awesome. Well, it’s great to talk to you Lindsay, hope you have a great day.
Lindsay Murphy: You too. Thanks for having me.
Jason: That’s it for this episode of Data BS. If you enjoyed this episode, make sure you subscribe wherever you listen to your podcasts and not miss the next one. This episode was sponsored by CorrDyn, a data consultancy that helps organizations unlock the power of their data. If you have a data challenge, we can help. Visit corrdyn.com, C-O-R-R-D-Y-N.com to learn more. See you next time.
