Salary is a sensitive topic. Should it be?

There are a lot of good reasons why salaries should be transparent. It helps candidates to not waste time on opportunities that pay less than they want. It shines light on pay gaps between genders and other groups. And it helps employees understand if they’re underpaid in their current roles. 

Salary transparency is a timely topic. The UK Government thinks we should talk more about pay and launched a pilot last week to address this.

“Participating employers will run pilots aimed at closing salary gaps by publishing salaries on all job adverts” – UK Equal Pay Pilot

But you don’t always need the government to make this happen. Several initiatives have started to appear where employees find ways of assessing pay within their organisations.

One recent example is HelloFresh employees doing the Open Salary Initiative 2022 where you can submit your salary and in turn see how you compare to your colleagues. This is a great “give some, get some” incentive that lets employees stay anonymous. 

All this made me want to try to shine some light on data compensation using data. I’ve previously worked at Google and know many people at FAANG companies (Facebook, Apple, Amazon, Netflix, Google). This is where I’ve taken my starting point by looking at more than 4,000 data points from sources such as levels.fyi and otta.com to put some numbers to pay.

Let’s dig in.

The median data total compensation in 2022 in the US is $187,000. This varies a lot across companies and seniority. For example, the median data compensation at Netflix is $450,000.

Data from levels.fyi

Move to the US, work for Netflix, get rich.  

US-based data jobs are paid well. Particularly if you’re among the lucky 440 working in a data role at Netflix. If you’ve read Reid Hastings book No Rules Rule you’ll know this is not by chance. Netflix has a policy where they encourage their employees to interview different places and bring back the offers they get. If Netflix sees that they’re being outbid they’ll increase your salary and do the same for everyone across the company in similar roles. 

“It costs a lot more to lose people and to recruit replacements than to overpay a little in the first place” – Reid Hastings, No Rules Rule

The other top tech companies also have many people in data roles earning more than $450,000 total compensation per year but not as consistently as Netflix.

Data salaries in US, Europe and elsewhere

It’s no surprise that US tech and data salaries are high. But how do they compare to Europa and to the rest of the world? 

Median US data salary for companies I looked at is $187,000 compared to $108,000 in Europe and $87,000 in the rest of the world. No small gap.

Data from levels.fyi, otta.com and a few other sources

While I wasn’t able to collect as many salary data points outside the US the pattern is still clear; you get paid much more in the US. That being said, a quick look at data jobs on otta.com shows that there are plenty of jobs in the UK that pay more than the median.

Data salaries by seniority

If money is cold, it’s cold at the top. The largest tech companies have different variations of a level (L) system that indicates seniority. Most employees fall in the range L2-3 (junior or mid) to L6 (expert or manager). At Google the level goes all the way up to L10 but the lucky few here are unlikely to share their salaries (a quick look at levels.fyi shows that the reported few salaries at L8 is more than $1,000,000 so I dare you to dream of where it ends at L10). 

Data from levels.fyi. Data is directional only and levels are not perfectly comparable across companies

If you go from L3 to L6 you more than double your salary. Some lucky top paid data people earn upwards $700,000 (and some possibly much more).

You’re now thinking to yourself that $400,000+ per year sounds quite nice. Where do you go from here?

You could join Netflix as a Senior Data Engineer and work on experimentation tooling. You’d need some knowledge around experimentation techniques and statistics and be good at working in cross functional teams.

Or you could join Google as a Data Scientist Technical Lead and work with large datasets and solve difficult analysis problems and applying advanced analytical methods.

Or if you just have a few years of experience you could join Google as a Business Data Scientist in Ads & Marketing and work your way up (from my experience at Google you can expect a promotion every two year at lower levels but this gets progressively harder the more senior you become).

Should you go chasing these high paying jobs? Maybe, maybe not.

But that’s not the point of this article. The goal is to open the discussion to whether we should talk more about salaries. Salary remains a sensitive topic but it makes for a good discussion. 

I’m keen to hear your thoughts. Reach out to me here.