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The objective of this project is to generate a comprehensive representation of the tech-talent landscape across Europe. This project focused on studying countries within the European Economic Area, while also including Switzerland, the United Kingdom, and Ukraine in its scope.

Qualitative and quantitative data collection methods were employed, including two quantitative surveys, 17 in-depth interviews, analysis of data from SeekOut, and Ledgy (who consented to share their data for the purposes of this study), as well as supporting desk research.

Recruiter survey

The recruiter survey was completed by 125 recruiters at European tech companies of various sizes in cities across the region (Figure 1). All responses were gathered online between July and September 2022. The large majority (89%) of respondents held recruitment and talent positions in their companies, while the rest were founders or occupied C-suite positions. Respondents were contacted directly by Sequoia.

Talent Survey

The talent survey was completed in August 2022 by 1,035 participants across 15 countries: a mix of STEM university students (22%) and software engineers (78%). Cint research survey software was utilised (Figure 2).

Desk research

SeekOut is a database featuring hundreds of millions of professional profiles, populated by scouring public data from LinkedIn and GitHub. Utilising natural-language processing (NLP) and machine-learning technologies, it categorises the expertise of every individual in the database. 

To identify fourteen unique areas of technological expertise, labelled in this report as “specialities,” general desk research was conducted and insights from industry experts were sought. Each of these specialities is associated with a specific set of foundational technical skills. Via SeekOut, these skills were used to identify 2.7 million software engineers and developers in cities across Europe. In each city, the density of engineers in each speciality was calculated as a percentage of that city’s total engineering population. 

For each speciality, a list of the 50 cities with the highest concentration of talent was produced. Using these rankings, certain cities were identified as being “Top”, “Notable” or “Standout” reservoirs of a particular speciality talent (Figure 3). These are the 24 cities featured in Atlas, accompanied by in-depth write-ups containing insights gleaned from further analysis and investigation.

For each speciality, the range of the city set (difference between the highest and lowest concentration) was divided into decile brackets. When the coefficient of variation (CV) was calculated, different specialities displayed varying dispersion. Thus, two distinct methodologies were devised: a broader criteria was applied to specialities exhibiting less variability in concentration and a narrower one for those with more variability, allowing talent-rich cities to be identified with the necessary accuracy, fairness and granularity.

The study adopted an iterative method, utilising both extensive desk research and insights from tech industry professionals to ensure the selected geographical radii accurately depicted the relevant cities and metro areas. Depending on the specific context, either a broader radius incorporating nearby towns and cities was chosen, or a more concentrated approach was preferred. For example, a narrow radius around Edinburgh avoided pulling in engineers from nearby Glasgow, which are clearly separate cities. In the case of Stuttgart, the similarity of the skill profile of Tübingen (44 km away) suggested including both within a wider radius. Similarly, Espoo is included within the greater Helsinki area for the purposes of this project. was utilised to gather data on venture capital, the financial value of the tech ecosystem, and the number of startups, accelerators and unicorns in each city. This platform sources and processes data from various avenues, including news articles, public filings, company and investor websites, proprietary research and partnerships with over 80 governments. To ensure data accuracy and regular updates, employs a blend of automated systems and manual research carried out by its analyst team. They also offer features such as the HQ filter and founding location filter, which allows for more accurate tracking of companies that may shift their headquarters over time.

For our analysis, we used the HQ filter, with the caveat being that companies that have relocated their headquarters may not be included in the founding city’s data. maintains a record of “unicorns,” counting those companies currently valued at $1bn or more, as well as those that previously realised a valuation of $1bn or more via acquisition or IPO even if their current valuation has declined. Furthermore, they track the presence of accelerators, regardless of their current operational status. The platform encourages user contributions and update suggestions, further enhancing the accuracy of their datasets. This feature also provides entrepreneurs with the opportunity to verify and suggest updates, ensuring a more comprehensive representation of the tech ecosystem.

This data was supplemented by several other sources:

  • supplied details on hiring processes for each city.
  • Ledgy offered data on equity-vesting schedules for each city.
  • was used to obtain the cost of living for each of the 243 European cities.  From this, the European average was calculated and assigned a value of 100%; all other cities were adjusted accordingly to create a comparable metric. The Cost of Living Index (excluding rent) serves as a comparative gauge of the average expenses related to consumer goods, encompassing areas like groceries, dining out, transportation, and utilities. This index does not consider housing costs, such as rent or mortgage. 
  • BNP Paribas Real Estate data for the 2021 Q2 to 2022 Q2 period was gathered via to determine office rental costs across cities. An average rental price for Europe was calculated using the 31 cities in the available Statista dataset. Furthermore, some rent data have been supported by Knight and Frank’s research.


Seventeen in-depth interviews were conducted with recruiters and founders of tech startups across Europe, all carried out online via Zoom or Teams. A thematic analysis was performed on the transcripts of these interviews, resulting in the emergence of four themes: recruitment, remote working, compensation and culture. These themes informed the content of the Insights articles on these and other topics.