Kaylie Boogaerts

We all know that pay is just as hard as it is important. Having a team distributed over 11+ countries makes pay even harder than in a traditional setup.

I joined Checkly in mid-2021 with the promise of a fair and transparent culture in a remote tech startup and my goal was clear: make Checkly one of the best employers in our industry. A topic that I wanted to tackle early on was pay. How can we make pay less nebulous and more transparent, fair, and predictable? How can we do this properly and have it grow with us?

In this post, you’ll read about how we tackled this challenge and what solution we ended up with.

Before diving into research and solutions, we needed to get a clear overview of:

Our goal: what exactly are we trying to achieve?

What does success for this project look like?

These are questions I always try to answer before diving into big projects. It helps me look at projects like this with a product mindset, which in turn helps me further the mindset of iterative development.

Our overarching goal was to make pay transparent, fair, predictable, and if possible even self-service. Once that was defined, it was time to start the research around different methodologies and other (remote) companies’ approaches. At our first retreat beginning of September 2021, I sat down with our co-founders to ask them compensation-related questions (10 compensation principles) and become aligned before starting the next phase of our pay project. From this session, we quickly realized we wanted to set up a pay formula and already discussed which factors we thought would make sense for us.

Benchmarking took up lots of time (2 months to be exact). It was important to do this right, but also to be pragmatic about it as we are still a small startup with just one People Ops person juggling other responsibilities. To understand data relevancy, quality and quantity we benchmarked against:

multiple locations (Berlin, New York, San Francisco, and eventually London) (more about this in our documentation)

multiple sources (Glassdoor, Salaries.com, LinkedIn Salaries, Payscale, the Option Impact survey and conversations with other startups such as Remote, Oyster, Adjust, Rasa, and Contentful)

After lots of benchmarking work, it was finally time to design our first pay formula and understand the impact of it on our own team and the roles we’re hiring for. After that, we wrote the documentation on our pay calculator and its different factors. You can find the link to our documentation with details on the factors of our pay calculator if you keep reading on!

Then it was time to discuss both the formula and the documentation with our C-levels and iterate based on their feedback. We tried to do this as async as possible, iterate on feedback and then have a sync meeting for the final go/no-go.

Time to launch! The launch included 3 major steps:

To launch the pay calculator internally, we shared the Notion documentation asynchronously with the team, encouraged them to read through it on their own time, and announced the upcoming Q&A session.

This session was for anyone in the team who wanted to join and had any more questions about our pay calculator. This session was recorded and the questions and answers were also summarized on Notion afterward. The team received the pay calculator and the philosophy behind it very well.

3. Publish and open-source the pay calculator:

Anyone can now access our pay calculator and our documentation about our pay calculator. We did have to take out a couple of roles as some people in our team had privacy and security concerns about sharing their role’s data publicly. These concerns are something we did not expect but do want to respect.

Our goal for sharing the calculator and the work we put in to create it is fourfold:

Point candidates to the pay calculator so they know exactly what base salary they can expect if they start working with us.

Push transparency, not only internally but set an example for other companies and push them to challenge themselves as well when it comes to transparency.

Improve fair pay practices. One of our team members gave a great example of this during the Q&A session: Engineers from Africa, who move to Berlin, do not know or have little resources to check what a decent base salary in Berlin is. They end up hired as Senior Engineers but paid a Junior salary because they don't know they could ask for more. The more data is out there, the better people can figure out what a fair salary is.

It's a great employer branding opportunity 😇

We’re definitely not done. We’ve got some points that we need to tackle written down in our Notion documentation. We will update our benchmarks at least once per year. Additionally, we’re keeping our eyes and ears open to stay aware of any rapid changes that we may need to react to.

This is only the first iteration of our pay calculator and we expect that as we gain more experience with it, we’ll have to iterate on it to make sure it keeps working for our team, culture, and scale. Expect many more iterations to come!

For now, the calculator is working well for us: We’re using it to make offer decisions when hiring and when current team members deserve pay increases. It’s made our decision-making less subjective, clearer to our team as well as faster because there’s less back-and-forth.

The feedback from candidates has been really positive so far, even when the calculator wasn’t officially public. In our job ads, we mention we’re working on a calculator and we are transparent about ranges. It has also come up in some interviews. Candidates really appreciate the transparency and clarity around pay, which in turn has resulted in no back-and-forth about the offered pay between the candidates and us.

We would love to hear your feedback on our Pay Calculator and how your team is approaching this topic.

Want to work on a team that has a transparent pay structure? Check out our job openings here.

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Open sourcing our pay calculator