October 10, 2022

Kit: Why I joined Bigeye

In this series, we interview members of Bigeye about why they joined the team. Here, we talk to Bigeye's VP of Sales, Kit Wetzler.


In this series, we interview members of the Bigeye team about their careers and why they've landed at Bigeye. This time around, we spoke with VP of Sales, Kit Wetzler.

Q. What is your educational background?

I have a bachelor’s in psychology.

Q. What were some of your jobs before Bigeye?

I started out as a systems administrator. I was a technical marketing engineer, a web developer, and a sales engineer. I've also run customer success teams and been a product manager.

I spent a bunch of time at Citrix, where I got acquired and came over as a project manager. I project managed the integration of an acquisition into another product, and then became a sales engineer. I ended up really getting into sales as a result of that.

The area VP whom I reported into as a sales engineering leader decided my talents were wasted in sales engineering. He wanted to see me going into sales directly. And I wanted to make more money as a sales engineer. So he basically taught me how to forecast on the sly, and he told me how to forecast without telling me what I was doing. After doing it for a few months, he made me a sales director very shortly after. It was a whirlwind tour. I took over a fairly experienced sales team of 20 individuals. And they were like, "What the heck?" I had to win the respect of everyone in sort of a trial by fire!  

Q. Do you prefer startups or corporate environments?

I like startups better. I really enjoyed my ten years at Citrix, but got frustrated by not being able to get anything done. I have also started my own company, so I have the experience of being a founder. It's not what I want to repeat at this point.

In big companies, there are so many moving pieces, and not everyone moves in the same direction. I like the focused energy and excitement of a startup. I love building something up, not quite from nothing, but from the beginning. There's a huge sense of accomplishment in tripling the size of a company.

Q. What have you learned since joining Bigeye?

I've learned a ton. While I’ve sold into data infrastructure, I’ve never sold products that understand data itself. I understand how the various databases and services work, both SQL and not.

But never had a product that actually cared about the data itself. So having to understand some of the basics about SQL queries and the different measures of data quality has been really interesting. And why and how they vary from other types of observability.

Having data engineers as buyers is also a new experience. I've always sold to infra folks or data scientists. Those people use data to make decisions, but they're not data engineers. I had to learn about ETL and ELT and how data gets from one place to another.

Q. How would you describe Bigeye’s culture?

It’s very collaborative. I’ve found that Bigeye has a great culture from the perspective of people willing to roll up their sleeves and help. If I’m blocked by something, there’s always someone willing to help unblock me.

I’ve found it’s a fairly transparent culture. Bigeye does a good job of sharing the information that we have. The culture is heavily driven by the founders, so kudos to them. As we scale, we're going to have to think a little bit more about how we intentionally build and maintain the great culture we have.

When it comes to sales, I'm focusing on building a sales team that's different. Sales teams in general can be very "fire and brimstone" and the world is always ending and we always have to get things done yesterday. There's an opportunity for us to do better. Sales teams don't have to be stressful; they can be collaborative and learn from each other.

Q. What is different about Bigeye when compared to other companies?

One of the things I look for in startups is founders who have solved the problem. Some companies had a good thesis around what should be solved. Bigeye’s solved the exact problem they’re ready to help the market solve. I was really excited about the credibility of our founders, since they actually were in the position of the people to whom we're selling.

Data observability is still a nascent space. There’s not a well-established leader in that market and there’s not established motion around it. That means there’s not an incumbent. There’s no bitterness and frustration built up around the space, so it’s easier for us to educate a customer on the right way to do things.

Q. Would you recommend Bigeye as a place to work for future prospects?

Yeah absolutely. The people who thrive here will be self-starters who are self-motivated. People who love to take the ball and run with it will thrive at Bigeye. It's a great opportunity for people who love to take ownership and get stuff done without being micromanaged.

I definitely recommend it for anyone who truly believes they’re suited for that environment. The things you want in a startup are: good leadership, good investors, and a good, known market opportunity. Data observability can’t go away, so it's not something people are going to forget about. Bigeye has all of these things.

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Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
Data analyst
Business analyst
Data/product manager
Total cost
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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