“How did they do that? How did they get there?” Companies succeed because of the people who build them - operating leaders who grow businesses to new heights and make decisions every day that can impact entire industries. Our Operator Spotlight gives you the inside track from one of our incredible Operator LPs (Limited Partners) who are changing the game – building and scaling some of the world’s most successful companies. Read on for lessons learned and mistakes made, perspectives from the top, practical advice, and ideas on what’s next. 

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This month, we spoke with Heather Akuiyibo, VP of GTM Integration at Databricks where she is focused on helping companies accelerate their innovation cycle with Data and Artificial Intelligence. With over 20 years of leadership experience in high-growth companies, Heather has built a remarkable career driving revenue and developing teams across enterprise and commercial sectors. Prior to Databricks, she held various Customer Sales, Partner Sales and Customer Support leadership roles at Zendesk, Google, and Sun Microsystems. She is passionate about helping companies leverage data and AI to transform their businesses, and is known for her expertise in building high-performing sales organizations and driving strategic growth initiatives.

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Databricks has been at the forefront of the data and AI revolution. What do you see as the biggest opportunities and challenges for companies looking to accelerate their innovation with data and AI today?

Heather: The challenge has always been about integrating disparate data sources. Traditional systems like ERPs used to be standalone, but now they're just one of many data sources feeding into a larger data platform. The number and types of data sources have exploded, creating a massive problem that's not going away. Figuring out how to extract value from all these different data sources while managing their compounding growth is a significant challenge for every organization.

Where I've seen true transformation within enterprises is when they consolidate around a clear data strategy that turns all those disparate systems into a competitive weapon. This foundation enables companies to accelerate new business opportunities, make faster decisions, and fuel constant innovation.

What's happening now with AI is taking this to the next level. These AI systems are incredibly data-hungry and need to be trained on specific, high-quality data. Companies that have already built that consolidated data foundation can leapfrog ahead when they layer ML, AI, and GenAI on top. At Databricks, we've created the lakehouse architecture as a way to have that consolidated data environment. It's fascinating to see traditional companies becoming incredibly innovative to fend off startups – using their data as a real competitive advantage.

You've been at Databricks for nearly 8 years, experiencing tremendous growth during your tenure. What have been some of the biggest transformations you've witnessed and helped drive during this period?

Heather: When I joined Databricks, we were still fairly early in the product lifecycle. We had one key persona we sold to and satisfied: the data scientist who needed to explore large datasets. At that time, nobody really knew who we were – our open source project, Apache Spark, was much better known. I was constantly having to define how Databricks was different from our open source project.

Things got interesting when we started bringing on other persona types, like data engineers. We became a multi-persona solution, and the product started to grow. Then we added data analysts and machine learning engineers. Looking back, it might seem like we just jumped on the scene overnight, but in reality, we were adding these different personas and products incrementally to become a platform. We paid tremendous attention to what each distinct customer type needed to do their jobs well, constantly interviewing and diving deep on their requirements.

We evolved from a point solution to a platform, which led us to invent and create the category of the lakehouse. It's been a fantastic experience seeing our customers consolidate their data and watching the entire industry embrace the concept.

Now I'm working at the intersection of partnerships, product, and sales – really focusing on how to take the power of the platform deeper into new personas, particularly on the business user side, creating solutions that work for both business and technical teams.

You've transitioned between customer sales, partner sales, and customer support roles across different organizations. How has this diverse experience shaped your approach to leading growth and commercial sales?

Heather: I wouldn't say I was super intentional about getting all these different kinds of experiences, but Google gave me my first opportunity to do something really different with my career – moving to India to work in customer support. It opened my eyes to a completely different side of the business.

That experience, along with working in partnerships, gave me a 360-degree view of customers – their options, what they're thinking about, how they weigh decisions. The time I've spent away from direct selling has always reminded me how important it is to be in the line of revenue, but also how to stay scrappy and creative no matter what size company you're working at.

I've learned that people in different roles have really different motivations. Customer support teams are driven by different things than sales teams or partnerships teams. Understanding these differences has helped me become more well-rounded.

When I'm in a role outside of frontline sales, I try to learn as much as I possibly can as fast as I possibly can, knowing I'll weave it back into frontline sales and customer connections. The bulk of my career has been in frontline sales, but these diverse experiences have made me a more effective leader.

What are some of the key skills you look for when building sales teams in the data and AI space, particularly when selling to technical audiences?

Heather: As a woman in tech and data, I focus very intentionally on building diverse teams. I approach this with eyes wide open – it's not an easy path, but I truly believe diverse teams have different approaches and can be just as successful, both collectively and as individuals.

The key is staying true to the definition of what 'good' looks like, no matter who the candidate is. I'm always going to try to find the best candidate for the job, but I'll dig deeper to find candidates who might not be obvious. If I'm seeing a lot of similar profiles, I'll push harder to get more people to interview.

For technical roles, I look for people who understand that there's both a technical decision and a business decision in play, and who are comfortable diving deep into the technology. I had to learn a tremendous amount about data myself – understanding the historical processes, what's happening now, and how to plan for where the industry is going. I did this with lots of mentors and a naturally curious mind.

I value people who are humble enough to identify when they don't know something and take action to learn more. Coachability is essential – we're constantly learning and growing in this field, and if you're actively seeking feedback and acting on it, that's incredibly powerful.

I also lean heavily into enablement – making sure the team truly knows what they're selling, who it matters to, and what challenges customers face. One of my proudest achievements was building a team where over 40% were women and people of color, with the leaderboard being equally split between men and women. Now, after investing time and energy into building great teams, I focus on helping team members grow their careers beyond their current roles.

You've worked at both large enterprises like Google and Sun Microsystems and growth-stage companies. What are some key learnings each environment can take from the other?

Heather: What I learned at Sun Microsystems, even as the company was struggling, is that you can learn and grow anywhere – it's more about mindset. I developed scrappiness there – I sold into thousands of square feet of empty office space by finding the few companies that were still growing and buying.

The structure I learned at Sun – weekly forecast calls, quarterly business reviews, customer QBRs – turned out to be valuable at Google, which was much more scrappy and growing rapidly. The cadence of a sales organization is constant and necessary no matter what stage you're in, because it helps drive the conversation and provides essential structure. You might move a bit faster in a startup, but maintaining that structure is vital – when you lose it, things fall apart.

At the same time, scrappiness matters regardless of company size. You have to stay hungry and creative no matter what stage your company is in.

How have you made a mark in your industry? What's something you've done that's perhaps counterintuitive in your field - broken any rules with interesting results?

Heather: One approach that's been important to my success across different companies is focusing not just on how to sell the product, but on how to embed it within customers' environments. At Databricks, we have what we call the 'built on' program – there are hundreds of companies running Databricks behind the scenes in their customer-facing applications without anyone knowing.

Our biggest example is Adobe Experience Platform, which has a tremendous amount of Databricks behind the scenes. We also work with startups like Abnormal Security, where Databricks helps with their machine learning models and data processing on the back end.

I've found that you can sell to a customer or you can sell through and with a customer. I've always found more interesting customer engagements and nonlinear success patterns when you sell with your customer.

What was one of your first jobs and what's one big lesson you learned?

Heather: I sold Christmas wreaths door-to-door for my swim team when I was younger. That experience has stuck with me – I love hiring people who have gone door-to-door because it's an incredibly humbling activity that builds grit, teaches you to think on your feet, and instills a strong work ethic.

I was also a competitive swimmer and coached swim lessons. I turned babysitting into swim coach opportunities in people's backyard pools. I'd work with kids who would be screaming and crying, saying they didn't want to get their face wet, and my job was to get them to want to do exactly that. I really learned to focus on who was paying me – the parents – while finding ways to make the experience work for the reluctant kids.

What's a piece of advice you would give to yourself 10 years ago, if you had the opportunity?

Heather: Everything changes. If things are challenging, stay true to who you are and your process. Don't make rash decisions. If you stay true to yourself, it is absolutely going to get better.

Why are you excited to be a part of Operator Collective?

Heather: I find Operator Collective to be a space that is so rare in that it brings together such a rich community at the intersection of venture, founders, and operators – and it's designed for people like me. Every time I come to a meeting or participate, I get new interesting insights, start thinking about problems differently, and meet really interesting women who are opening doors for me and for others. It's one of those places where I really want to pay it forward as much as I possibly can.