Career Stages 

We’ve plotted career stages and salary below, since we thought it was something people would appreciate. We didn’t find any major surprises in the data, but it’s good practice to check for the unexpected (i.e some other result).

It would appear that once a worker has “enough” compensation, it becomes much harder to know how the package stacks up. All the medians of the people above Neutral stack atop each other. It’s possible this suggests a struggle to understand the difference between “Mildly Well Compensated” up through “Extremely Well Compensated”, but we don’t know for sure.

Sometimes it’s difficult to understand why we’re getting compensated more. If we get more uncertain in our worth as salary rises, then it’s helpful to see what causes those rises. One obvious candidate is career stage:

Career level and salary

This plot is solid, and expected in terms of its progression. It supports the hypothesis that salary rises with experience. However, it doesn’t help us understand the variation in higher salaries we see in “Compensation.”

“Career Level” is a somewhat vague term, so let’s go deeper. We asked about both years of expertise in DevRel work, and also the years in the current company. That suggests a place to look. First, let’s examine years of experience:

Salary, time in field, and time at current company

The right plot is not hugely informative – “Years at Company” alone isn’t telling us much. The left plot suggests a trend of around $6000 increase for each year of DevRel expertise, but there’s a catch!

People tend to gain more money as they gain experience. What we really want to do is compare people who stay in one job to people who move around – both are gaining “years” on the left plot, but one keeps resetting to “0” years on the right plot with each job move.

So, we built a model using both variables. Visualising such models is difficult – we have more than one “y-axis”, and we want to understand the effect of both things, but 3D plots are difficult to read clearly. However, we can compare two purely hypotheticalscenarios – one where a person stays at the same company for twelve years, and one where they move companies every three years:

Modeling job strategy: Salary, while staying or remaining at company

The lower line (all circles) shows the person who stays at Company 1 for twelve years. The upper line (with different shapes) shows the person who hops from company to company.

We’ve omitted the uncertainty (it’s quite large, as the sample is small), but a trend is apparent. In this hypothetical model, while the effect starts off small, there’s a noticeable salary jump after each job hop, and person 2 ends up earning ~$15,000 more after twelve years. In other words, seniority matters, but in this model staying in one place does indeed lead to decreased raises. (This is a well-researched effect, but it’s nice to find it in our data too).

This is a good explanation for some of the variation seen in the Compensation and Perceived Fairness plot – some people will feel well compensated because of how their career shook out, and others will have less satisfaction with their package.

TABLE OF CONTENTS

Introduction

Pt. 1 | Pt. 2 | Pt. 3 | Pt. 4 | Pt. 5 | Pt. 6 | Pt. 7 | Pt. 8

Conclusion and Takeaways

Appendix A: | Appendix B:

SPECIAL THANKS TO OUR DATA SCIENTIST, GREG SUTCLIFFE

Greg Sutcliffe has been working in community management for a decade, and is currently the Principal Data Scientist for the Ansible Community. He's interested in how appropriate use of data can inform the development and governance of communities, especially with regards to open source projects. He also likes cooking.