Benchmark Your Data Team

Over the past years, we’ve looked into different benchmarks for data teams across team size, team composition, data-to-engineers ratio, and salary benchmarks.

You can now access all this data to see how your team compares to some of the best companies such as Notion, Meta, and Monzo. Get benchmarks like these:

• Average data team size as % of the company (hint: 3%)
• Median salary across data roles for 500 job postings in Europe
• Distribution of analytics engineers, data engineers, and analysts
• The data-to-engineer ratio at top tech companies

Check it out here: databenchmarks.com

The data has been collected from hundreds of companies and thousands of data points on LinkedIn, open job boards, and a few other sources.

Data team size benchmark

How big should your data team be relative to total company size?

The range for most companies is between 1-5%. It’s a broad range, and it should be, as there’s no one-fit-all number.‍ Fintech companies come out on top with 3.5% of the workforce being in data roles while B2B companies have the lowest ratio at 2.4%.

Deep dive into the data used here: databenchmarks.com

B2B companies like Talkdesk, Figma, and Notion rely more on sales and engineering, though product-led growth is increasing data’s role. B2C firms such as Opendoor and Oscar use large-scale A/B testing and ML for funnel optimization and marketing. Fintechs like Robinhood, Monzo, and Revolut have the highest data role share, balancing regulatory needs with ML-driven fraud detection and lending. Marketplaces like HelloFresh, Flexport, and VOI use data to improve margins and user experience, often merging data and engineering roles.

Data team composition benchmark

What’s the right mix of data roles in top data teams? Looking at the composition of data roles across analysts, data scientists, data engineers, analytics engineers, data platform, data governance, and machine learning shows some interesting patterns.

If you over-index on analytical roles, you may risk slowing everyone down as the quality of the data platform starts to deteriorate. If you over-index on data engineers, you may have a world-class data platform but no insights or data products that drive business impact to show for it.

Deep dive into the data used here: databenchmarks.com

We only have to look at a few examples to see the caution you have to show when comparing companies. These also highlight that the optimal ratio may vary significantly depending on the company’s priorities.

• Revolut has a large share of analysts, many of whom are distributed across different markets and working on areas such as financial crime and credit.
• Zendesk has a large machine learning team in line with the company’s recent refocus to “the world’s most complete CX solution for the AI era”
• Nubank now refers to all data analysts as analytics engineers to demonstrate its focus on better applying established software engineering principles and data modeling techniques to all business domains.

Data-to-engineering team ratio benchmark

How large should your data team be relative to your engineering and product team?

For the companies in the sample, the median data-to-engineer ratio is 0.13x and the median product-to-engineer ratio is 0.12x. While it’s more common to have a slightly larger data team relative to the product team, there are also companies where the product team outnumbers the data team.

Deep dive into the data used here: databenchmarks.com

The companies can be grouped into data-first, product-first, and engineering-first.

Data first: Data-led companies have large data teams relative to the engineering team. These companies typically have business models where data directly contributes to the bottom line such as Tinder, Doordash, and Instacart that all use data as a core part of their business model and operations.

Product first: Some companies such as Personio, checkout.com, and Cover Genius have relatively larger product teams. There’s not one factor that determines why this is the case. Cover Genius for example has a handful of people employed with the “Insurance Product Manager” title.

Engineer first: These are predominantly platform and development-heavy companies such as Rippling, GitLab, and GitHub. In many cases they have engineers carry out part of the job that overlaps with what the product and data team would normally do.

Europe data salary benchmark

How much should you pay your data team? Looking at salaries across 500 European-based jobs can help us benchmark salaries for data engineers, data analysts, analytics engineers, and data scientists.

• Junior: Median compensation is $70k with few people, mainly at international tech companies, exceeding $100k
• Mid: Median compensation is $86k with 25% exceeding $100k
• Senior: Median compensation is $110k with 25% exceeding $150k

Deep dive into the data used here: databenchmarks.com

The benchmark looks at total compensation which also includes stock rewards and bonuses to give the most fair comparison. I’ve only looked at data for individual contributors (IC). While I can’t guarantee the accuracy of all data it should give a good indication of compensation in data roles in Europe. For ease of comparison, I’ve converted all numbers to USD ($).