Recruitment agency forData
Data Analyst, Data Engineer, Data Scientist, ML Engineer, Analytics Engineer, Data Product Manager, Head of Data…
The best Data candidates don’t apply for jobs. We approach them directly.
0 trial periods ended early in 2025
- 70
Data hires since 2023
- 80,000+
candidates in our network
- 90%
of candidates accept our clients’ offers
Our approach
Why trust us with your Data hiring
Targeted search
We approach the best data candidates directly through our LinkedIn Recruiter license, our talent pool and referrals. We select each candidate for their technical skills, their projects in production and their ability to work with business teams.
Our sourcing channels
Assessment
We build a clear scorecard with you based on your data needs: stack skills (Python, SQL, Spark, dbt), pipeline architecture, business acumen and communication. We verify every achievement a candidate cites before we present them.
Sample Data scorecard
Long-term partnership
We share our market insights (salary grids by data role, stack trends, early signs that people are ready to move) and our search reports to help you build a strong data team.
What we deliver
Tailored search report
Up-to-date salary benchmark
Tracking of candidates open to a move
Closing and onboarding follow-up
Our process
6 steps to hire the right person
Role definition and scorecard
Targeting and search strategy
Screening and assessment
Candidate presentation and reports
Support through to the signed offer
KPI review and continuous improvement
Role definition and scorecard
Targeting and search strategy
Screening and assessment
Candidate presentation and reports
Support through to the signed offer
KPI review and continuous improvement
Our Data expertise
Roles we cover, stacks we know, salary ranges and seniority levels.
Data Analyst
Answers business teams’ questions with SQL and dashboards, and turns the numbers into concrete decisions.
Data Engineer
Builds the pipelines and the data warehouse (Snowflake, BigQuery, dbt, Airflow) that the whole data team works on.
Data Scientist
Builds statistical or machine learning models to forecast, classify or recommend, then measures their impact.
Head of Data
Leads the data team, chooses the stack and sets priorities across reporting, data engineering and models.
BI Consultant
Delivers business intelligence projects for clients: Power BI, Tableau, data modeling and user training.
BI Engineer
Builds and runs internal BI systems: the warehouse, ETL flows and the reports business teams use every day.
Data Manager
Runs data governance: data quality, the data catalog, access rights and GDPR compliance for datasets.
Data Product Manager
Manages data products as products: self-service platforms, data APIs and shared metrics.
Companies that trust us with Data hiring
A dedicated expert, client testimonials and completed hires to back it up.
Data client testimonials
Charline Patay
Chief of Staff · Orus Energy
« We were looking for a rare find. A dynamic person combining technical skill and ingenuity, with one condition: their hard skills matter as much as their fit with the rest of the team. Lity took ownership of our criteria, filled the pipeline with truly relevant candidates and presented us with the profile we were hoping for. We recommend their services without hesitation. »
Related Data case studies
Your Data recruitment consultant
5+ years recruiting Data talent
Data talent has been in short supply since 2022. Hiring a data engineer, a data scientist or a Head of Data who can build a modern stack (Snowflake, dbt, Airflow) takes months in-house, and counteroffers are common.
Julie Porchez leads our data practice at Lity. She has recruited data analysts, data engineers, data scientists, ML engineers, analytics engineers and Heads of Data for more than 5 years.
Learn more about our approachShow less
Data organizations now have distinct layers: an analytics team on the business side, a data engineering team on the platform side and a data science/ML team on the product side. Hiring without defining what you expect (BI, pipelines, ML, governance) almost guarantees a poor fit. Senior candidates who are strong on both the technical and the business side are rare and in high demand.
Julie knows modern data stacks (Snowflake, BigQuery, dbt, Airflow, Databricks, Looker, Metabase) and the different ways data teams are organized (data mesh, centralized, decentralized). She can calibrate a junior data engineer, a senior ML engineer or a Head of Data, check how mature their production projects really are and read a track record without being misled.
Our method draws on 4 complementary sources: our LinkedIn Recruiter license, to approach currently employed candidates directly; our talent pool of data profiles, which we keep up to date; referrals from our network of top performers; and our presence at data community events. We build the scorecard with you. Our client case studies document each search, with actual timelines and results.
We work with companies of every size
Startup, scale-up, SME or large enterprise: we adapt our methods to your stage of growth and your hiring needs.
Startup
1 to 30 employees
At a startup, one versatile data analyst is enough to build the first dashboards and set up a light stack (BigQuery + Metabase).
Scale-up
30 to 300 employees
At a scale-up, the team gets organized: a data engineer for the pipeline, an analytics engineer for the models, and a data analyst for each domain.
SME
50 to 500 employees
In the expansion stage, companies hire a Head of Data, launch a dedicated ML/AI program and build data teams for each business unit, with a central platform team.
Large companies
Mid-market companies and large groups
In a large corporation, data is governed by a CDO, with dozens of data engineers, scientists and analysts and a shared data platform.
Further reading
Resources and other Lity practices.
Other Lity practices
All our recruitment practicesRelated
Keep exploring
- Hiring in FranceContracts, salaries and notice periods for US and UK companies.
- Salaries in FranceGross, net and employer cost by role, in euros, dollars and pounds.
- Freelance recruitmentFreelance experts, available within 7 days.
- Permanent recruitmentPermanent hires in France and Europe, paid on success.
Answers to your questions about recruiting in Data.
What do data roles pay in France in 2026?
Here are the 2026 salary ranges for data roles in Paris:
- Data Analyst: €48-60K mid-level, €58-75K senior
- Data Engineer: €55-68K mid-level, €70-85K senior
- Data Scientist: €55-72K mid-level, €70-90K senior
- ML Engineer: €62-78K mid-level, €78-100K senior
- AI Engineer / LLM Engineer: €70-95K mid-level, €90-130K senior
- Head of Data at a scale-up: €105-130K plus equity
Candidates who come from Mistral, Anthropic or OpenAI fall outside the usual salary bands, with packages in line with US levels.
Data engineer, data scientist, analytics engineer: what is the difference?
Three complementary roles that share the data workflow:
- Data Engineer: builds the data infrastructure (ETL/ELT pipelines, warehouses, data quality in production)
- Data Scientist: builds predictive and ML models (feature engineering, experimentation, deployment)
- Analytics Engineer: connects the two with dbt, advanced SQL and business metrics modeling
The Modern Data Stack originally relied on the data engineer and the data scientist.
The analytics engineer became a core role in 2022-2024, especially at Series A+ scale-ups, where the need to govern business KPIs grows quickly.
How do you assess a data engineer in 2026?
Four areas to assess a data engineer in 2026:
- Stack: real production experience with at least one modern stack (Snowflake or BigQuery plus dbt plus Airflow)
- Pipeline quality: automated tests, observability, SLA management
- Architecture: ability to design a scalable distributed system (Spark, Kafka, real-time streaming)
- Business acumen: understanding business KPIs and balancing technical rigor with delivery
A hands-on exercise on a real case (modeling a dataset, optimizing a complex SQL query) shows how deep a candidate’s skills actually go, beyond the buzzwords.
Is the data job market in France tight in 2026?
Yes. The French data market is tight in 2026, especially in three areas:
- Senior data engineers: those who know the Modern Data Stack are in very high demand at Series B+ scale-ups
- AI engineers and LLM engineers: the hardest roles to fill in 2026, driven by the growth of AI copilots and agentic AI
- Heads of Data: those with a track record at international scale-ups are rare and heavily recruited
By contrast, demand for traditional data analysts is leveling off, while analytics engineers are gaining strategic value through dbt and metrics governance.
What does the Modern Data Stack look like in 2026?
ELT (Extract-Load-Transform) has replaced traditional ETL. The 2026 Modern Data Stack is built around:
- Ingestion: Fivetran or Airbyte
- Warehouse: Snowflake or BigQuery
- Transformation: dbt
- Orchestration: Airflow
- Reporting: Looker or Metabase
- Streaming: Kafka, Kinesis or Flink
- ML: MLflow, Weights & Biases, feature stores (Tecton, Feast)
- Agentic AI: LangChain, LangGraph, vector databases (Pinecone, Weaviate), evals (LangSmith, Braintrust)
Large, data-heavy organizations are structured around a data mesh.
What career paths are open in data?
Data careers can go in several directions.
On the management track:
- Data Analyst → Lead Analyst → Head of Analytics
- Data Engineer → Lead DE → Head of Data Engineering
- Data Scientist → Lead DS → Head of Data Science → Head of Data
The individual contributor track leads to Staff Data Engineer or Principal Data Scientist.
Common career moves:
- AI Engineer: a very common move for data scientists
- Data Product Manager / Founding Data Engineer: many senior data engineers move into these roles, the founding role mostly at startups
- Data strategy leadership: a common next step for analytics engineers
Trust us with your hiring in Data.
A hands-on approach for hires that last.
Intro call (30 min)
A video call to understand your needs and walk you through our approach.
On-site visit
We meet you on site to get a detailed understanding of your culture and your hiring plans.
Targeted search
The search starts. We present only the candidates who match your requirements.