Data Engineer vs Data Analyst: What Are the Differences in 2025?
Understanding the differences between Data Engineer and Data Analyst: missions, skills, salaries and career progression. Guide to choosing the data career that suits you.
Two Complementary Roles in the Data Ecosystem
Data Engineer and Data Analyst are often confused, yet their missions are very different. In summary:
- The Data Engineer builds the pipes (infrastructure, pipelines)
- The Data Analyst uses the data that comes out of these pipes
At Datakhi, we recruit both profiles. Here's our guide to understanding these careers and choosing your path.
Daily Missions
Data Engineer: The Builder
The Data Engineer is responsible for the company's data infrastructure:
- Design and maintain data pipelines (ETL/ELT)
- Build and optimize Data Warehouses and Data Lakes
- Ensure the quality and reliability of data flows
- Manage system performance and scalability
- Automate ingestion and transformation processes
Data Analyst: The Interpreter
The Data Analyst transforms data into business insights:
- Analyze data to answer business questions
- Create dashboards and reports
- Identify trends and anomalies
- Formulate recommendations based on data
- Communicate results to business teams
Technical Skills
Data Engineer Tech Stack
| Domain | Technologies |
| Languages | Python, SQL, Scala, Java |
| Big Data | Spark, Hadoop, Kafka |
| Cloud | AWS, Azure, GCP |
| Orchestration | Airflow, Prefect, Dagster |
| Databases | PostgreSQL, MongoDB, Redis |
| Data Warehouses | Snowflake, BigQuery, Redshift, Fabric |
| Tools | Docker, Kubernetes, Git, Terraform |
Data Analyst Tech Stack
| Domain | Technologies |
| Languages | SQL, Python (Pandas), R |
| Visualization | Power BI, Tableau, Looker |
| Spreadsheet | Advanced Excel, Google Sheets |
| Statistics | Statistical tests, regression |
| Databases | SQL Server, PostgreSQL (queries) |
Level of Technicality
The Data Engineer has a more technical and engineering profile:
- Mastery of cloud infrastructure and DevOps
- Advanced programming (OOP, design patterns)
- Understanding of distributed systems
The Data Analyst has a more analytical and business profile:
- Excellent business understanding
- Synthesis and communication skills
- Sense of visualization and storytelling
Required Education
Data Engineer
- Master's degree generally required
- Engineering school in computer science
- Master's in Data Engineering or Computer Science
- Big Data or Cloud specialization
Data Analyst
- Bachelor's to Master's degree depending on position
- Business school with data specialization
- Master's in statistics, economics, marketing
- Data bootcamp (career change)
Salaries in France (2025)
| Level | Data Engineer | Data Analyst |
| Junior (0-2 years) | €38,000 - €45,000 | €35,000 - €43,000 |
| Intermediate (2-5 years) | €50,000 - €65,000 | €43,000 - €55,000 |
| Senior (5+ years) | €65,000 - €85,000 | €55,000 - €75,000 |
The Data Engineer is on average 15-20% better paid than the Data Analyst due to the higher technical complexity of the role.
International
In Switzerland, Germany or the United States, a senior Data Engineer can reach €80,000 to €120,000 gross per year.
Career Progression
For the Data Engineer
- Lead Data Engineer: Technical team management
- Data Architect: Overall architecture design
- Platform Engineer: Infrastructure specialization
- Machine Learning Engineer: Bridge to data science
For the Data Analyst
- Senior Data Analyst: Domain expertise
- Data Scientist: Evolution towards ML
- Analytics Manager: Team management
- Product Analyst: Product specialization
How to Choose?
Choose Data Engineer if you like:
- Coding and solving complex technical problems
- Building robust and scalable systems
- Working with cloud infrastructure
- Automation and optimization
Choose Data Analyst if you like:
- Telling stories with data
- Understanding business challenges
- Creating impactful visualizations
- Collaborating with business teams
Can They Work Together?
Absolutely! In a high-performing data team:
- The Data Engineer sets up the pipeline that collects sales data
- They transform and load it into the Data Warehouse
- The Data Analyst creates a Power BI dashboard on this data
- They identify a drop in sales in a region
- The Data Engineer optimizes the pipeline to get fresher data
At Datakhi
We recruit Data Analysts passionate about Power BI and business analysis. We also work with Data Engineers on our Microsoft Fabric projects.
Want to join a dynamic data team? Discover our opportunities and apply!