Data roles are in very high demand. These positions are at the heart of the digital transformation of companies and startups. It is essential for both companies and candidates to know how to position themselves in a market that moves very quickly. With the growing need for talent in artificial intelligence (AI), some roles could see their pay continue to rise.
👉 Our articles on startup salaries and developer salaries may also interest you.
👉 For an overview of all IT roles, see our guide to IT salaries.
Overview of data salaries in France
The rest of this article goes into more detail by experience level, location and other data roles.
What is the salary of a Data Analyst in France?
The role of a Data Analyst is to carry out analyses and build dashboards so that the company can make use of its data. A Data Analyst’s salary varies a great deal because it depends heavily on their education. For graduates of an engineering grande école (France’s selective elite higher-education institutions), salaries will be above €40,000 gross a year, and can reach €45,000 or €50,000 gross a year.
For an Analytics Engineer with skills close to those of a Data Engineer, salaries can be higher (5-10% more on average).
Note: there are many career-change programmes for aspiring Data Analysts. Candidates with no training related to the role who have only completed a bootcamp may be offered lower pay than other candidates.
What is the salary of a Data Engineer in France?
The Data Engineer designs the architecture used to collect, process and deliver data within the company, for Analysts and Scientists. It is one of the most sought-after roles in data, and the number of open positions remains very high.
Among individual contributor roles, it is probably the one with the highest average pay, whatever the candidate’s degree.
What is the salary of a Data Scientist in France?
The Data Scientist designs and develops predictive models based on statistical data and Machine Learning algorithms. Data Scientists’ employability is strongly determined by their level of education. While it is difficult for a recent graduate to find a job in this type of role, graduates of the best engineering schools can command very attractive salaries straight out of school.
Pay levels and employability therefore vary widely depending on candidates’ education and degree. A graduate of École Polytechnique, Centrale or Mines will be on a completely different salary grid from graduates of other programmes.
What is the salary of an ML Engineer?
The Machine Learning Engineer designs, develops and deploys machine learning models to integrate them into real-world applications. ML Engineer pay is broadly similar to Data Scientist pay.
The same education-related gap applies (a degree from an engineering grande école vs other programmes).
What is the salary for a Data Governance role in France?
Data Governance roles are found mainly in large companies that have reached a certain size. Their purpose is to define the responsibilities and processes that govern data across the organisation.
Note that this role does not exist in startups and scale-ups.
What is the salary of a Head of Data and a Chief Data Officer?
The Head of Data leads the data team. The title Chief Data Officer, or CDO, is used when the person sits on the executive committee, generally in companies where data is at the heart of the product, or in a large group or a scale-up. Where the focus is specifically on AI strategy and adoption, there is also the Chief AI Officer, an executive dedicated to AI. To hire for this kind of C-level role, see our Chief Data Officer recruitment agency.
How to recruit data talent in France?
Recruiting data talent is complex:
- Candidates are in short supply.
- It is difficult to reach the best talent.
- To attract the best talent for experienced or senior roles, you need to build your network and approach candidates directly (headhunting).
For junior candidates and recent graduates, it is important to build relationships with the best data schools. For senior candidates, proactive sourcing can be essential because they rarely apply for jobs, particularly for Data Engineer and Analytics Engineer roles.
You may need to work with a Data recruitment agency such as Licorne Society, which can help you find and attract the best talent in this field.
Sectors and companies hiring in data
The war for data talent is not the same everywhere. Technical skills (Python, SQL, Cloud) are the baseline, but it is the business sector and the company’s maturity that drive up the bidding and redefine salary grids.
DeepTech, FinTech and HealthTech: the sectors that stand out
While e-commerce and retail have constant needs, three sectors clearly stand out for their ability to attract the best profiles.
- FinTech/InsurTech: here, data is the product. Risk modelling, fraud detection, algorithmic trading: the financial impact is immediate. Investment funds and neobanks pay a premium for domain expertise.
- HealthTech: medical R&D and MedTech generate massive demand. The complexity and sensitivity of the data require niche profiles (bioinformaticians, Data Engineers specialised in imaging), and salaries follow.
- DeepTech (and Generative AI): this is the playground of cutting-edge AI (LLMs, Computer Vision). Companies are fighting over the elite: researchers (PhDs) and MLOps engineers able to industrialise these complex models. Their scarcity gives them negotiating power.
Large companies vs startups: the impact on salaries
The type of company directly affects the pay package.
- Large groups (CAC 40 (France’s leading listed companies), GAFAM): they rely on job security and the overall package (high base salary, profit-sharing, RTT (extra days off under the 35-hour week)). At listed “Big Tech” companies, shares (RSUs) make the difference and can double the base salary over several years. The downside: more rigid pay grids and often slower progression.
- Startups / Scale-ups: the bet is on equity (BSPCE (French startup stock warrants)). The base salary is sometimes lower, but the potential gain at exit (acquisition, IPO) is the main argument. Scale-ups (in hypergrowth) are the most aggressive: they have to match the base salaries of large groups and offer attractive BSPCE to win over “builders” ready to create everything from scratch.
