Job Overview
The Data Engineer is the technical pillar of data projects. He designs, builds and maintains architectures enabling to collect, store and process massive data volumes. True architect of data pipelines, he guarantees data quality, availability and reliability for data science, analytics and business intelligence teams. Without solid data infrastructure, data scientists cannot develop ML models, nor analysts produce reliable dashboards. The Data Engineer is the guardian of Data Quality and Data Governance.
Main Missions
Data architecture design
Define data systems architecture: technology choices, workflow modelling, infrastructure sizing. Arbitrate between batch and streaming based on business needs.
ETL/ELT pipeline development
Build robust pipelines to extract data (APIs, databases, files), transform (cleaning, enrichment, aggregation) and load into target systems. Automate and orchestrate processes with Apache Airflow, Prefect or Dagster.
Storage infrastructure management
Administer data warehouses (Snowflake, BigQuery, Redshift), data lakes (S3, Azure Data Lake) and databases (PostgreSQL, MongoDB, Cassandra). Optimise performance and costs.
Monitoring and optimisation
Monitor data quality, detect anomalies, optimise SQL queries and Spark jobs. Guarantee pipeline availability and critical SLAs.
Team collaboration
Work with data scientists to prepare ML training datasets, with analysts to build data models, with product teams to integrate analytics events.
Typical Technical Stack
| Category | Technologies |
|---|---|
| Languages | Python (pandas, PySpark), Advanced SQL, Scala, Shell/Bash |
| Orchestration | Apache Airflow, Prefect, Dagster, Luigi |
| Distributed processing | Apache Spark, Dask, Flink, Kafka |
| Data Warehouses | Snowflake, Google BigQuery, Amazon Redshift, Databricks |
| Cloud | AWS (Glue, EMR, Athena), GCP (Dataflow), Azure (Data Factory) |
| Data Quality | Great Expectations, Soda, Monte Carlo, dbt |
Required Skills
Technical Skills vs. Soft Skills
- Mastery of Python and/or Scala for data processing
- Advanced SQL expertise (optimisation, window functions, CTEs)
- Knowledge of Big Data frameworks (Apache Spark, Kafka, Airflow)
- Mastery of cloud provider (AWS, GCP or Azure)
- Experience with modern data warehouses (Snowflake, BigQuery, Redshift)
- Data modelling skills (normalisation, star schema)
- Containerisation knowledge (Docker, Kubernetes)
- Rigour and attention to detail for data quality
- Analytical mind and problem-solving ability
- Autonomy and capacity to manage complex technical projects
- Communication to translate business needs into technical solutions
- Curiosity and continuous technology watch
- Ability to work in multidisciplinary teams
Salary Grid 2026
| Experience | SME/Startup | Mid-size | Large group | Ile-de-France |
|---|---|---|---|---|
| Junior (0-2 years) | 40-48K EUR | 45-52K EUR | 48-55K EUR | +10-15% |
| Confirmed (2-5 years) | 50-60K EUR | 55-65K EUR | 60-70K EUR | +10-15% |
| Senior (5-10 years) | 60-75K EUR | 65-80K EUR | 70-90K EUR | +15-20% |
| Lead/Staff (10+ years) | 75-95K EUR | 80-100K EUR | 90-120K EUR | +20-25% |
Career Progression
Junior Data Engineer
Pipeline development under supervision
Data Engineer
Project autonomy, architecture design
Senior Data Engineer
Technical lead, mentoring, architectural decisions
Lead Data Engineer / Staff Engineer
Global architecture, standards, cross-team
Data Architect / Head of Data Engineering
Data strategy, team management
Sectors Hiring
| Sector | Specifics | Examples |
|---|---|---|
| E-commerce | Massive transactional data, real-time | Amazon, Cdiscount, Back Market |
| Fintech/Banking | Strict regulatory, maximum security | Lydia, Qonto, BNP Paribas |
| AdTech/MarTech | High-frequency streaming, attribution | Criteo, ContentSquare |
| SaaS B2B | Product analytics, usage tracking | Datadog, Algolia, Doctolib |
| Transport | IoT, geolocation, optimisation | Blablacar, Uber |
Frequently Asked Questions about Data Engineer Role
What is the difference between Data Engineer and Data Scientist?
Do I need to know Machine Learning to be a Data Engineer?
Data Engineer vs Analytics Engineer: what's the difference?
Is the role accessible for career changers?
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