Data to engineers ratio: US vs Europe
Why European companies have larger data teams, US companies have larger engineering teams and analytics companies are not that analytical.
Last week I made the claim that data was having its moment with companies doubling down on data hiring and the data to engineers ratio approaching 1:2 for some top European tech companies.
How does it compare across the pond? 🇺🇸
The median data to engineers ratio for the US companies I looked at is 1:7 compared to 1:4 for the European companies. And the design to engineers ratio is 1:9 for both groups.
This post gives some answers to why this is but also leaves some questions unanswered.
Let’s dig in!1
That’s quite the gap between companies at different ends of the scale.
Analytics companies are in fact not that analytical. Disappointing as it may be, analytics companies such as Amplitude, Pendo, Snowflake and Fullstory are… not that analytical. All of the companies above only have ~2% of their total workforce in data roles and more than half of their employees don’t work in tech roles at all.
Developer tools companies really are for developers. HashiCorp, Sentry and Gitlab are all bottom of the list when it comes to number of data people and designers per engineer. This makes sense and they all have large engineering teams - some exceeding 50% of their total workforce.
Data is the secret Texan hot sauce
Plotting a matrix of design and data to engineers tells us an interesting story of where companies focus.
Is this any different than how it looked for European companies? Quite a bit!
Only 50% of US companies have more data people than designers compared to 80% of European companies I looked at.
This doesn’t mean that Europeans invest less in design; they just invest more in data. The median design to developer ratio is 1:9 for both US and European companies. This is not too far from what Nielsen Norman Group found in a study of 500 companies which showed that 50% were targeting a design to developer ratio of at least 1:10.
US companies are more often engineering first. 36 of the US companies fall in that quadrant in comparison to 19 of the European companies.
Is this a coincidence or is there something structural going on here? Let’s go deeper 🇺🇸 🤺 🇪🇺.
Cookie or biscuit? How US companies compare to Europeans
From last week’s analysis we know that the business model is one of the best indicators for the data to engineers ratio; marketplaces for example had 2-3x as many data people per engineer compared to B2B. Did I simply just pick more companies from a low data ratio business model for the US? Perhaps, but even within the same business models US and European companies have very different data to engineers ratios.
The picture is clear. European companies have notably more data people per engineer compared to US companies across all business models.
US is a larger market with larger engineering teams - ex. Doordash & Deliveroo.
With a data to engineers ratio of 0.42, Deliveroo’s (🇪🇺) ratio is more than double that of Doordash (🇺🇸) at 0.18. This doesn’t mean that Doordash is less data driven; in fact, in absolute terms they have more data people than Deliveroo and there’s no shortage of awesome data work coming out of their team. They just have a much larger engineering team than Deliveroo.
Looking at all 100 companies in my analyses engineers as a percentage of total workforce for US companies is 21% compared to 17% for the Europeans.
Europe has more deep tech and US more Dev tools - ex. HashiCorp & Onfido.
Onfido (🇪🇺) has a data to engineer ratio of 0.16 compared to 0.02 for HashiCorp (🇺🇸). If you’re Onfido, that makes sense. Machine learning is a core part of your offering and investing more in data is a good idea. Europe is home to many similar deep tech startups. In contrast, Dev tools have a very low data ratio to engineers and the US has by far the largest share of the DevOps tools market.
If we plot the data to engineers ratio side by side the contrast is stark with the gap exceeding 2x for some verticals.
While larger engineering teams in the US and a focus on deep tech in Europe helps explain some of this there are also other factors at play. Here are a few of my (speculative) hypothesis
1) The US market is larger, more competitive and there are more experienced sales executives. Therefore US-based B2B companies have relatively larger sales forces.
2) European startups are often scattered across many markets from day one. This creates unique data challenges that require larger data teams.
3) More US companies are led by engineering founders that create a more engineering-focused culture.
For good order sake, I also looked at if there’s any correlation between company size and data to engineers ratio as the US companies in my sample are slightly larger (the average US company size is 2,100 compared to 1,500 for the Europeans). I found no significant correlation.
Where do we go from here
If your company doesn’t invest as much in data as your peers it’s worth asking the question - are you falling behind or do you have a good reason to focus elsewhere?
If your competitors are data enabled and directly use data to create cost efficiencies or better customer experiences you should too.
It will be interesting to see if the US and European data to engineers ratio will start to converge or if the systematic differences around larger engineering teams and business models will keep this gap as it is.
Let me know if you have any thoughts or ideas.
I’ve looked at the keywords on LinkedIn and included all matches (for example, product engineer would be matched from the engineer term)
Data: Data Analyst, Data Scientist, Machine Learning, Data Engineer, Data Manager, Analytics Engineer, Product Analyst, Business Intelligence, Data Lead/Manager/Director/VP
Engineering: Engineer (excluding Data Engineer), Tech/Technical Lead
Design: Design(er), User Experience, UX, User Research
I’ve deliberately not included all analyst roles which means that roles such as financial analyst, sales analyst and strategy analyst are not counted as data roles although you could classify some of their work as data work.