Do I need a PhD to get a data science job in Europe?

A PhD is not required for most industry data science roles, though it can accelerate entry into research-focused positions at AI labs (DeepMind, Meta AI, Mistral). The majority of data science jobs across tech, finance, and e-commerce prioritise strong Python and ML skills, a portfolio of projects demonstrating real-world problem-solving, and the ability to communicate insights clearly to non-technical stakeholders.

What is an Analytics Engineer and why is it a growing role?

An Analytics Engineer sits between data engineering and data analytics — they build and maintain the data models, transformations, and semantic layer that analysts use. The role has grown rapidly with the adoption of dbt (data build tool), which enables analysts to write modular SQL models with version control. Analytics Engineers typically earn €60,000–€95,000 and are in high demand at data-mature companies.

Which data certifications are most valued by European employers?

Cloud certifications from Google (Professional Data Engineer, Professional ML Engineer), AWS (Certified Data Analytics), and Azure (DP-203, AI-102) are well-regarded for data engineering and ML roles. For analytics, dbt Certification and Looker certifications are increasingly recognised. SQL and Python proficiency demonstrated through portfolio projects often outweighs formal certifications.

How do I transition into a data role from a non-data background?

Many successful data professionals have transitioned from engineering, finance, research, or business roles. The most effective path is to build SQL and Python skills through online courses (Mode Analytics SQL Tutorial, Kaggle, fast.ai), complete project-based portfolio work using public datasets, and target junior analyst roles where domain knowledge from your background adds value.

Data Jobs — Analyst, Scientist & Engineer Roles in Europe

Last updated: 14 July 2026

Summary

Browse data analyst, scientist, and engineer roles at leading European tech, finance, and analytics companies.

What data roles are available at European companies?

Europe's data job market covers a rich range of specialisations: Data Analyst, Business Intelligence (BI) Analyst, Data Scientist, Machine Learning Engineer, Data Engineer, Analytics Engineer, ML Research Scientist, Data Platform Engineer, and Chief Data Officer (CDO). Financial services, tech, healthcare, e-commerce, and consulting sectors are the largest employers of data professionals across the continent.

What data skills are European employers prioritising in 2026–2026?

SQL remains the foundational skill for all data roles. Python (with pandas, scikit-learn, PyTorch) is essential for data science and ML engineering. dbt, Airflow, Spark, and cloud data platforms (BigQuery, Snowflake, Databricks, Redshift) are in high demand for data engineering. For BI and analytics, Looker, Tableau, and Power BI proficiency is standard. LLM fine-tuning and RAG architectures have become critical for AI-focused data science roles.

How much do data professionals earn in Europe?

Data Analysts typically earn €40,000–€65,000 in Western Europe. Data Scientists range from €60,000–€95,000 at mid-level, with senior scientists and ML Engineers commanding €90,000–€140,000+. Data Engineers with cloud platform expertise (dbt + Snowflake/BigQuery) typically earn €65,000–€110,000. Research Scientists at AI labs (DeepMind, Meta AI, Mistral) can earn well above €150,000 with significant equity. The data market remains one of the tightest talent markets in European tech.

What is the difference between a data analyst and a data scientist?

Data Analysts focus on descriptive and diagnostic analytics — understanding what happened and why — using SQL, BI tools, and dashboards. Data Scientists apply statistical modelling and machine learning to build predictive models and solve complex business problems. The distinction is becoming more fluid, with many companies using "Data Scientist" as a broad title covering both. Analytics Engineers bridge the gap between data engineering and analytics, building reliable data models using tools like dbt.

Are data jobs remote-friendly in Europe?

Data roles are among the most remote-friendly positions in tech, given the nature of the work. Many companies with distributed data teams operate fully remotely across EU time zones. Data engineering roles in particular are highly compatible with async workflows. Use the Work Setup filter to find fully remote data positions, which are common at SaaS companies and data-native startups.

How Many English-Speaking Jobs Are Available in Europe?

Faruse currently lists 134 matching jobs. Job listings are refreshed daily.

Latest Job Openings

Found 134 matching jobs

Data Scientist at Vivid Resourcing - Mechelen (Unknown) [Full-time]
Risk & Resilience Officer at Danmarks Nationalbank - Copenhagen (Unknown) [Full-time]
Business Intelligence Consultant (Fabric) at Magnit Global - Austria (Unknown) [Full-time]
AI Governance Engineer at Deeploy - Utrecht, Utrecht, Netherlands (Unknown) [Full-time]
Full-Stack Developer (Go + React) at Sporting Rock GmbH - Hamburg, Germany (Unknown) [Full-time]
Medewerker Servicemanagement at Nictiz - The Hague, South Holland, Netherlands (Unknown) [Full-time]
Desktop Support Specialist at Sharp Brains Solutions - Estenfeld, Bavaria, Germany (Unknown) [Contract]
Data Analyst at Hyra - Hesse, Germany (Unknown) [Full-time]
VP Technology & Data at nLighten - Schiphol-Rijk, North Holland, Netherlands (Unknown) [Full-time]
Customer Success Manager at Guardsix (formerly Logpoint) - Munich, Bavaria, Germany (Unknown) [Full-time]
People & Culture Intern (all genders) at revel8 - Munich, Bavaria, Germany (Unknown) [Full-time]
Global IT Manager at Medair - Lausanne Metropolitan Area (Unknown) [Full-time]

Frequently Asked Questions

Do I need a PhD to get a data science job in Europe?: A PhD is not required for most industry data science roles, though it can accelerate entry into research-focused positions at AI labs (DeepMind, Meta AI, Mistral). The majority of data science jobs across tech, finance, and e-commerce prioritise strong Python and ML skills, a portfolio of projects demonstrating real-world problem-solving, and the ability to communicate insights clearly to non-technical stakeholders.
What is an Analytics Engineer and why is it a growing role?: An Analytics Engineer sits between data engineering and data analytics — they build and maintain the data models, transformations, and semantic layer that analysts use. The role has grown rapidly with the adoption of dbt (data build tool), which enables analysts to write modular SQL models with version control. Analytics Engineers typically earn €60,000–€95,000 and are in high demand at data-mature companies.
Which data certifications are most valued by European employers?: Cloud certifications from Google (Professional Data Engineer, Professional ML Engineer), AWS (Certified Data Analytics), and Azure (DP-203, AI-102) are well-regarded for data engineering and ML roles. For analytics, dbt Certification and Looker certifications are increasingly recognised. SQL and Python proficiency demonstrated through portfolio projects often outweighs formal certifications.
How do I transition into a data role from a non-data background?: Many successful data professionals have transitioned from engineering, finance, research, or business roles. The most effective path is to build SQL and Python skills through online courses (Mode Analytics SQL Tutorial, Kaggle, fast.ai), complete project-based portfolio work using public datasets, and target junior analyst roles where domain knowledge from your background adds value.