Data Analyst: Salary and Responsibilities in 2026

Within the Data or Tech team, the Data Analyst comes in after the work of the Data Scientist and the Data Engineer. They manipulate data to make sense of it and surface insights and trends. These analyses are used by managers to inform strategic decisions, but also by business and marketing teams.

The Data Analyst role can also be found under the names Data Miner Business Analyst, Business Intelligence (BI) Analyst, and BI Manager.

Job profile last updated on 09/06/2026.

What is data?

All companies collect data, intentionally or not. Through their commercial, marketing, and operational activity, they capture every day a volume of data proportional to the intensity of their activity: CRM, connected objects, social networks, search engines, user-driven service usage, etc.

Over the past two decades or so, with the exponential growth of technology, they have been learning to leverage this data - internally to improve their product/service, support decision-making, automate through AI, measure, etc., or externally by reselling it to other companies.

Now that everyone has understood the importance of data, the goal is no longer to lose a single drop of it. It's the new wealth of the 21st century: as oil reserves are running dry, data reserves are filling up… and they have no limit.

What is their role?

Their goal is to leverage and extract value from the massive data collected and processed by the Data Engineer, who needs a highly skilled technician: the Data Analyst.

Why do companies need a Data Analyst?

The Data Analyst is hired by all kinds of companies that want to rely on processed data to support their strategic, marketing, and business decisions.

Wherever there is data, they can deliver advice based on data that a human alone couldn't translate. Their work allows the company and managers to make decisions backed by their data.

More concretely, this value shows up in turning data into reports, dashboards, and visualization tools to present results in a way that's understandable to everyone.

They work in all kinds of companies across various sectors that handle large volumes of data.

What are the responsibilities of the Data Analyst?

Today, digital plays such a central role that companies receive a phenomenal amount of information every day, allowing them to optimize their strategies. To leverage the massive data collected, they need a highly skilled technician: the Data Analyst.

Their job is to process the various data on customers, products, or company performance to extract metrics useful to decision-makers.

The information provided by the Data Analyst helps companies define which products to offer customers based on their needs, the marketing strategy to adopt, or improvements to make to the production process.

The Data Analyst is an expert in statistics and analytics. Their role is to make data speak. They manipulate so-called "clean" data to observe and understand phenomena and behaviors.

These data can come from internal sources (CRM, DB) that have been previously prepared by the Data Engineer, but they can also come from external sources (web, data providers).

The Data Analyst then produces reports, dashboards, and visualization tools to present results in a way that's understandable to everyone. Their work allows the company and managers to make decisions.

In short: tables, charts, and reviews - that's their day-to-day!

Team collaboration

In a small tech team, they may carry the responsibility of an entire data function, combining the Data Engineer and Data Analyst roles. They will own the entire data value chain without being able to go deep into any single topic.

This requires horizontal knowledge of data issues without the ability to develop vertical expertise.

In a large tech team, they work under the responsibility of a Data Manager (Head of Data, CDO, Lead Data Manager), in collaboration with the Data Engineer who provides them with a volume of so-called "clean" data ready to be used.

The Data Scientist may work on the same decision-making problems but with a different output. The Data Analyst will develop visual tools (dashboards) and reporting, while the Data Scientist will build predictive models.

Outside the data function, the Data Analyst can also be attached to the leadership of a business team to work on their issues in direct contact with those teams (very often marketing, but also business, finance, or HR depending on the company's orientation).

What kinds of problems does the Data Analyst tackle?

Here is their day-to-day work:

• Supporting business teams (AD HOC) that need a report on a data crossover, to get one specific piece of information.

For example: an operational team member asks the Data Analyst, "Can you give me the number of ride-hail trips yesterday in this area?"

• Creating and maintaining dashboards to track performance.
• Sales team revenue progression dashboard over a given period, for sales leadership.
• Long-term study and analysis to understand a phenomenon: behavior, mechanics, user perception.__

For example: comparing user behavior - what behavior does someone coming from a Google search adopt versus someone coming from this ad we're funding?

Another example: deep-dive study on a specific topic - what's this partner's contribution? Was it a good decision to set up this partnership? What changes have we seen since it launched?****

• Maintaining the data architecture to keep access to information and ensure the durability of future tasks.

Technologies & platforms used

They also use reporting and data visualization tools such as:

• DB language: SQL
• DB management: MongoDB, Cassandra, Hbase
• Data analysis: Hadoop suite
• Data management & visualization: Power BI, Elastic Suite, Tableau

They will use the following languages and frameworks:

• Languages (+): R, Python, Java + Scala, C++
• Big Data frameworks: Spark, Hadoop, Hive

What skills does a Data Analyst have?

Technically, they master languages such as R, Python, or SQL used to manipulate databases. Strong command of data visualization tools is essential - Plotly, Tableau, Dataiku, or Qlik Sense / Qlik View, and of course Excel.

Analytical skills are essential to interpret and produce results. They are equally comfortable with querying and data mining as they are with synthesizing results.

In addition to mathematics and statistics skills, the Data Analyst is able to translate technical content for non-technical teams.

Soft skills

The Data Analyst has a strong analytical and synthesis mindset. They have an eye for detail and know how to keep a critical view of results to extract the key information hiding in the mass of data.

Strong communication is also important in this profile, since they will be presenting results to managers and teams unfamiliar with data.

What training is needed to become a Data Analyst?

A Data Analyst needs solid skills and a strong appetite for statistics, probability, and mathematics applied to computer science.

• University degree in computational statistics, mathematics, probability theory, etc.
• Engineering school
• Experience in finance can be a plus, or even a way into the Data Analyst role.

What is a Data Analyst's salary?

Here are the expected salary ranges based on experience:

• Junior Data Analyst: €35K to €45K
• Mid-level Data Analyst: €43K to €55K
• Senior Data Analyst: €50K to €65K+

How can a Data Analyst's career progress?

This role allows for a career as: Lead Data Analyst, Data Manager, Data Scientist, Big Data Expert.

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FAQ about the Data Analyst role

What is the difference between a Data Analyst and a Data Scientist?

The Data Analyst uses "clean" data to produce descriptive analyses, dashboards, and reports — they answer the question "what happened?". The Data Scientist goes further: they build predictive models and machine learning algorithms to answer "what will happen?" or "why?". In practice, in smaller structures, one person sometimes covers both roles; in larger data teams, the specialisation is clear.

What is the difference between a Data Analyst and a Business Intelligence (BI) Analyst?

The two roles overlap significantly. The BI Analyst is often more focused on building and maintaining the reporting infrastructure (data models, data warehouse, recurring dashboards). The Data Analyst typically has a broader scope: ad hoc analyses, behavioural studies, and closer collaboration with business teams (marketing, finance, product). In some companies, the titles are used interchangeably.

What is a Data Analyst's salary in France in 2026?

A junior Data Analyst earns between €35,000 and €45,000 gross per year. A mid-level profile reaches €43,000 to €55,000. A senior Data Analyst exceeds €50,000 to €65,000 and above. Profiles with strong sector expertise (fintech, healthcare, e-commerce) or advanced mastery of cloud tools (BigQuery, Snowflake, dbt) can negotiate higher salaries.

What tools must a Data Analyst master?

The non-negotiable core: SQL to query databases, Excel or Google Sheets for quick analyses, and a data visualisation tool like Tableau, Power BI, or Looker. Additionally: Python or R for advanced statistical analyses, some knowledge of dbt for data transformation, and familiarity with cloud environments (BigQuery, Snowflake, Redshift). Mastery of these tools directly determines the Data Analyst's level of autonomy.

Do you need to know how to code to become a Data Analyst?

SQL is non-negotiable: every Data Analyst must be able to write queries to extract and manipulate data. Python or R is a significant plus for advanced statistical analysis but is not always required at entry level. Strong Excel / Google Sheets skills (pivot tables, complex formulas) remain highly valued in many environments.

Which industries hire the most Data Analysts?

E-commerce and retail (purchasing behaviour analysis, price optimisation), fintech and banking (risk analysis, fraud, scoring), media and platforms (audience analysis, recommendation), digital marketing (attribution, campaign performance), and healthcare (patient data, clinical trials) are the biggest employers. Demand is strong in any company that generates a significant volume of data.

What training can lead to a Data Analyst career?

The most common paths: engineering school with a data/statistics specialisation, a master's in data science, statistics, or computer science, or a bachelor's in computer science or mathematics with a BI specialisation. Short training programmes (bootcamps like Jedha, DataScientest, Le Wagon) allow a career change in 3 to 6 months. SQL and a visualisation tool can be learned through self-study and are sufficient for junior positions.

How is the Data Analyst role evolving with generative AI?

Generative AI (ChatGPT, Copilot, BI tools with AI assistants) is automating repetitive tasks: writing basic SQL queries, generating standard dashboards, producing analysis summaries. The Data Analyst is increasingly focused on complex, high-value analyses: formulating the right business questions, critically interpreting results, and communicating insights to non-technical teams. Business context understanding is becoming a key differentiator.