Data to engineers ratio: A deep dive into 50 top European tech companies

Why some companies have doubled their data teams and business model is a good indicator for data team size

You know data is having its moment when the number of open data roles for top tech companies is approaching that of engineering. But just how many data people does the best tech companies employ?

My analysis from 50 of the top European tech companies shows that the median data to engineers ratio is 1:4. 

This matches what I hear when I talk to data leaders but as you can see in the chart below, the ratio ranges from below 1:10 to 1:2 - no small span! 

Check out all the ratios below¹

When I talk to high growth companies most of them are trying to hire data people fast so I suspect this ratio will only increase.  

At Monzo where I work we’ve more than doubled the size of the data team in just two years. And with 74 open data roles (and 102 open engineering roles) Glovo is looking for almost as many new data people as engineers.

So… why is the data hiring arms race happening?

• Data is getting more complex and more is expected of data teams as sales, marketing, operations and product teams all are expected to operate in data-driven ways

• Companies are starting to see real wins from data teams - from machine learning models that are having real-world impact to large scale A/B tests to bringing data directly to operational tools. Data teams now have meaningful ROI stories to show and are often being asked by other teams to accelerate hiring

• Data is how you win and if you don’t get it right you can’t compete (at least in some industries)

Data is the secret sauce 

Data-driven is not just a trendy buzzword. It’s how modern companies win. To quote a16z’s Martin Casado from Coalesce a few weeks back

In the past so much of the differentiation was software. Today, the way you differentiate is how you manage data. If you’re building a dog walking app you no longer win on software but instead on your matching algorithm, pricing model or ability to detect fraud

Plotting a matrix of design and data to engineers ratio tells an interesting story of what the focus is for companies and their business models.

Some companies such as Snyk, TravelPerk and Onfido have smaller data teams relative to their engineering teams while companies such as Glovo and Deliveroo have more than 2x as many data people per engineer. Is that by chance? Probably not. 

If you’re a B2B SaaS company such as TravelPerk you’re probably data driven. You have dashboards that guide decisions, everyone has access to self-serve data through your BI tool, you may use a few machine learning models and do some A/B testing to improve conversion funnels.

But if you're a marketplace like Deliveroo you have to be data enabled. You win based on how good you are at predicting demand, guiding restaurants to pick the best price, helping riders decide on the best route or scheduling riders to come to work at the right time. In such a fierce marketplace where multiple well-funded companies fight over “share of stomach” only the ones using data best will survive. 

My guess is that all business models will become data enabled in the future - not just marketplaces. 

Stats by business model

If we group the companies into B2B, B2C, Fintech and Marketplaces/physical goods they align almost perfectly in clusters with just a few outliers. The contrast is stark between the groups; for example, the data to engineers ratio for marketplaces / physical goods is 2-3x higher than for B2B companies.

How come they’re clustered so neatly? Let’s look at each business model.

B2B (i.e. Onfido, Travelperk, Contentful): This is the category with the lowest data to engineers ratio. Companies here have fewer, larger customers and it’s more difficult to run A/B tests and deploy machine learning models at scale. Often differentiation happens through sales and engineering efforts. 

Fintech (i.e. Wise, Monzo, Revolut): These companies have a higher data to engineer ratio spanning 0.2x - 0.3x. Companies such as Monzo, Revolut and eToro have a higher data to engineers ratio while others such as N26, Starling and Tide invest less in data. Fintech companies are often well placed to directly improve their core business with data, for example by predicting fraud, deciding who is eligible for a loan or streamlining customer operations. One example is Monzo’s fraud detection machine learning model that was nominated for an award.

B2C (i.e. Cazoo, Gymshark, Trustpilot): These companies are more scattered, often with a higher data to engineers ratio than B2B companies and the highest designer to engineers ratio. Many have millions of customers and are well placed to run large scale A/B tests, deploy machine learning models and drive their business through data. A great example of this is Cazoo’s recent example of building a model to predict return on ad spend to understand which channels to allocate budget. 

Marketplaces / physical goods (i.e. HelloFresh, Glovo, VOI): This is today’s data battlefield with by far the highest data to engineer ratio. These companies have tight profit margins and using data is how they stay ahead of competition. A great example of this is Deliveroo, having created a dispatch engine to automatically match the best combination of riders with customer orders.

Investing in the future

If you’re a data or engineering leader and find that your data to engineer ratio is much lower than your peers, what do you do?

Maybe it’s fine. Your data setup may be simple and you’ve got the right amount of self-serve capabilities and are currently investing in building out your core product instead.

Maybe you don’t get the full value of data yet and you’re skeptical to invest more. Your data team may be drowning in ad-hoc requests as there are not enough people to support the basics or your tools may be out of date. If that’s the case, you’re not being as data driven as you could and scaling up the data team is probably a good idea.

Maybe you’re falling behind. If your competitors are data enabled and directly use data to create cost efficiencies or better customer experiences you should too. 

Maybe you could be leading. If you’re in a B2B segment where none of your competitors run data enabled business, could you do this to stand out?

If you have any interesting insights to share from your company just reach out to me on LinkedIn.

1. 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.