Building a great team culture in the data industry

Not into podcasts? No worries—I’ve turned our conversation into a blog post, where I cover:

1. 1. How Euno is improving collaboration and workflows for data teams
   2. How to balance analysts’ freedom and data model governance 
   3. How to build the best teams to solve the most pressing problems
   4. How to make short-term compromises to secure the best talent 
   5. How to assess the inclusivity of a team culture before joining

Lindsay and I both found the last point especially valuable—don’t miss it! Ready when you are. 

But first, who am I to tell you how to lead your team?

I’m Sarah Levy, the co-founder and CEO of Euno. I’ve been dealing with data for the past 20 years in various tech fields. I spent over a decade in cybersecurity, building data products. I also worked for almost another decade at a MedTech company as the CTO, where I led R&D, product, regulation, production, and operations. This involved a lot of work with data, both from a data science and data analytics perspective. It was a really interesting experience using data across those different disciplines before starting Euno. I also happened to lead a large real estate business unit in a major FinTech company, where the whole idea was to use data to optimize the real estate funnel. 

It all led me to build various homegrown solutions and face challenges that today I’m solving for data teams. I’m also a mother of three children, which is no less important than my career—perhaps my other career. So, a mother and a co-founder and CEO—that’s me.

Taking it one step further, you see a lot of companies where the founders might not fully understand the problem they’re trying to solve. That’s why it was so important for me and my co-founder, Eyal Firstenberg, to tackle a problem we’ve deeply felt and experienced. We were diving into these challenges in the data space, but it’s crucial, even though we’ve experienced it ourselves, to rely on more than just our intuition for understanding the real problem and finding the right product-market fit. 

What gave us a competitive edge was that when we started working on Euno, we spent six months interviewing data leaders from over 300 companies. We wanted a real perspective on how these challenges are experienced across organizations of different sizes and with different data stacks. That’s essential because many times you think, ‘Oh, I’ve experienced it firsthand, so it must be a real challenge for others’—but that’s rarely the case. Those interviews with hundreds of data leaders really shaped how we built and designed Euno.

The problem we solve for data teams

I think it starts with a very common problem that every data organization faces: trusting your data model or trusting your definitions, entities, and calculations. In simple terms, what is an Active User, an Engaged User, a Transaction, or Churn for your business? What is Total Revenue? In many organizations, it’s difficult to agree on a single term or calculation, especially at scale. When you’re small, it’s easy—everyone controls everything. But as organizations grow, each business domain continues evolving its business terms and logic. This is part of the daily job and needs to happen—but it becomes challenging to align everything into a consistent business language, terminology, and data model. 

This is a classic problem that different solutions and products have been trying to solve for years. In recent years, we’ve seen a trend to shift this business logic from the consumption layer (the BI layer, the analyst layer) to the data layer, where it’s coded and formalized in a way that allows for governance, so the entire organization can align on a consistent business logic and reuse consistent terms at scale. This is an amazing practice, and we now have the technology to do that. We’re big fans of dbt™ and try to leverage it as much as possible for this purpose. 

However, almost every organization still struggles with this. On one hand, the business and analysts need the freedom to create new business terms as they go—it’s part of their job, and you can’t slow down the business because the organization depends on it. On the other hand, you need to be able to go back, code those terms, and formalize them for efficiency and consistency in future use. But this practice doesn’t always scale easily. There are different roles and practitioners in the data space—engineers, analysts, and now analytics engineers—trying to bridge these gaps. 

That’s why we’re building Euno: to serve the new practitioners in the data space, known as analytics engineers. It’s designed to help them with their day-to-day challenges and to provide governance and control over the entire data model without sacrificing the freedom and independence of the analysts who work with the business domains.

It’s all about scale

The more the data stack expands, the more tools and roles emerge, becoming an inseparable part of data teams. But the different functions, from analytics engineers to data analysts, create the challenge of establishing a consistent and smooth workflow. Building a workflow that fosters collaboration between engineers and analysts is at the core of Euno’s mission. 

The bonus? Many companies develop their own homegrown solutions, automations, and processes—and we’re here to productize that for scale. Crafting efficient workflows while juggling other data-related tasks is tough, and that’s where we come in—building a tool designed to empower teams to do just that. Analytics engineers, we’re here to change your life!

Behind the scenes

We’re currently a Seed-stage startup, having emerged from stealth about six months ago. What makes our journey unique is that we consciously decided to join forces early on. Without even having a pitch deck, we spent half a year interviewing data teams before hiring our first employee. We raised Seed funding from VCs who believed in us—not just because of our idea, but because they knew our careers and wanted to be part of this journey. We simply shared the space we were tackling, and the rest followed.

Now, as we prepare for our Series A round next year, we’re focused on building a strong customer pipeline. At the Seed stage, you’re not fulfilling the entire vision yet. The challenge is balancing product-market fit, delivering value to early adopters, and knowing when to hold off on solving every problem. That’s the tradeoff we’re managing carefully.

It’ll be interesting to reconnect after the A round—I’ll have plenty of stories to share! There are so many challenges to tackle, and it’s easy to spread yourself too thin. Even before launching the company, we maintained close relationships with our investors, building ties beyond fundraising. Many of them offer valuable advice and introductions, which is why managing these networks with VCs and investors has been such a key part of our journey.

Building out a great team

The same way I prioritized building relationships with investors, I approach hiring a founding team with the same mindset. Everyone says they want the “best” people, but defining what “best” means is where it gets interesting. For me, it’s about finding individuals who are passionate about tackling deep, complex problems—something that’s driven me for the past 20 years. I’m drawn to smart and creative problem-solvers, not necessarily the most experienced, but those who thrive on tough challenges. That’s the kind of team I want to build, and it’s rare to find.

I trust my intuition and experience when choosing who to bring aboard, and I go all-in to get the right people. In startups, everything moves fast—going from the third round to Series A, from the first customer to the tenth. But in the end, this is a long-term journey. You’re not hiring for six months; you’re hiring for years. These are the partners you’re going to build your company with, so I believe in doing whatever it takes to get them onboard.

I’m serious about this—I pursued my VP of R&D for over a year before she agreed to join. I started talking to her six months before we even raised our Seed funding, and when she joined, she was three months pregnant. It didn’t matter. We’re in it for the long haul. We built the team, set up processes, and when she went on maternity leave, we adjusted. She’s back now, and things are running smoothly again.

To me, it’s all about finding the best talent and being flexible enough to create the right conditions for them. They need to be passionate and committed, but that doesn’t mean they’ll always be in the right geography, time zone, or availability. Flexibility is key, and great talent is always worth it.

I’ve noticed a common mindset in the tech space, especially with startups, where companies say, “We can’t afford to invest in people; we need to move fast.” It’s always about short-term incentives—quick hires, immediate progress. But there’s a balance to strike between short- and long-term gains. The startup mentality often leans heavily on short-term solutions, but investing in the right people from the start is a long-term differentiator. I believe that by building a team I’ve truly invested in, I’ll see stronger results in the long run, while many other startups experience high turnover because they didn’t prioritize that investment.

Even in the short term, fast iterations and constant turnover are costly. You spend so much time onboarding and training people, only for them to leave. In the data space, it’s no different—onboarding new hires can eat up valuable time. But when I invest in someone and they stay, I not only benefit in the long term but also gain in the short term. Startups often lose time onboarding, and if you can hold onto talent, it pays off quickly.

Looking for a tech job for women

I joined a startup as CTO when I was three months pregnant, and it shaped how I approach leadership and hiring today. Someone believed in me back then, which reinforced my decision to hire my VP of R&D even though she was also three months pregnant. That experience left a lasting impact on me.

If I could offer one piece of advice, it would be to always work in places you’re passionate about, alongside people you admire. For women in particular, it’s easy to see companies today touting equality and inclusivity, but the reality is often disappointing. A lack of women in the founding or management teams can be a sign of a deeper issue. Sometimes we don’t realize it’s a problem until someone quietly mentions how poor the culture is or that they’re only there for the paycheck. Those are huge red flags.

So, speak to people who work there and ask if they genuinely enjoy the environment. No one should have to apologize for caring about their family or needing flexibility. You should focus on the value you bring and your passion for the work, and let the company confront their own unrealistic expectations.

Every professional relationship should be a mutual fit. When interviewing, it’s not just about impressing them; it’s about choosing a company that aligns with your values and personality. The right culture for you is essential, and there are many successful companies where you might not fit in, and that’s perfectly okay.

When I give a negative response to a candidate, it’s never about their capability—it’s about the match. Sometimes it’s not a good fit for either party, and that’s a key part of the process. Just as you invest in personal relationships, you should also carefully consider the work environment you’re entering. You’re choosing not just a job, but a boss, a leadership team, and peers who should support you and share your values.

As a CEO, I want my team to feel supported and valued. I aim to build a culture where people are proud of their contributions and feel they have a meaningful impact. I hope every team member feels the same way about our company.

Women in data leadership roles

To make space for women in leadership roles, focus on hiring and promoting the best talent without falling into biased practices. Women make up about half the population, so they represent half the talent out there. Sure, there might be biases in fields like data science where men are more prevalent, but the goal should always be to hire based on skills and fit, not artificial quotas.

Having women in leadership positions, like a female CEO or VP of R&D, can naturally attract more women candidates who might feel more comfortable and supported. At my company, we’ve got a balanced team without trying to skew the numbers—it’s about hiring the right people and letting that balance happen organically.

Also, a supportive culture should recognize and respect everyone’s personal lives, not just try to look equal on paper. For example, if you expect your women employees to balance family and work, you should extend the same understanding to men. It’s unfair to expect women to take time off for family while not expecting the same from men. You can’t ignore that fathers also want time with their families. Besides, inclusivity isn’t just about gender—it’s also about broader factors like age. If your team activities are always late-night events centered around drinking, you’re not considering those with family responsibilities. 

When building a culture, think about what genuinely makes your people happy and engaged. It’s not just about following a model to impress outsiders; it’s about creating an environment where your team feels respected and supported. Talk to your employees, understand their needs, and build a culture together that reflects those values. Ultimately, it’s about creating a workplace where people feel respected and valued for who they are, not just for meeting some external standard.

Before you go

I’m a firm believer in pursuing what you’re passionate about and working with people you genuinely admire. That’s essential for building great companies and finding the right place to work. In the data space, we’re focused on creating a product that makes a real impact. But it’s equally important to listen to our customers and understand the needs of data engineers and analytics engineers, rather than just pretending we know best.

We’re also building a company culture that I’m proud of, and I hope to be just as proud when we’re 200 or 500 people strong. I encourage every founder and manager to focus on these values. Your company is your people—it’s not just about the product. Your people are the ones who invent and shape everything. That’s something I deeply believe in.

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Watch the full interview on Lindsay Murphy’s Women Lead Data: