🏗️ DATA ENGINEER (M/F)

The Data Engineer designs, builds, and maintains the pipelines and infrastructure that collect, transform, and make the company's data available. This is the foundation on which analysts, data scientists, and AI teams build their work: without reliable data engineering, there is no usable data.

🎯 Tasks

• Build ingestion and processing pipelines (batch and streaming).
• Model data in the data warehouse/lakehouse and standardize data transformations.
• Ensure the quality, freshness, and traceability of data (testing, monitoring, lineage).
• Optimize the costs and performance of cloud data platforms.
• Provide clean data for BI, ML, and data products.

🛠️ Skills & Tech Stack 2026

• Programming Languages: Advanced SQL and Python—the must-have duo.
• Transformation: dbt (de facto standard), Spark for large volumes.
• Orchestration: Airflow, Dagster.
• Platforms: Snowflake, BigQuery, Databricks, lakehouse (Delta, Iceberg).
• Streaming: Kafka.
• Quality & Operations: Great Expectations, CI/CD, Terraform, Data Contracts.

⚠️ Legacy stacks (Talend, Sqoop, Pig, SAS) are reaching the end of their lifecycle: they are now used only in migration scenarios.

💰 2026 Salaries (as reported in France)

• Junior: 42–50 k€
• Confirmed: 50–68 k€
• Senior / Lead: 68–95 k€ and up
• Freelance: Average daily rate of 500–800 €.

🔍 Not to be confused with

The Data Analyst (analysis and reporting), the Data Scientist (modeling), andthe Analytics Engineer (business-oriented data transformation, a hybrid role between analyst and engineer).

❓ Frequently Asked Questions

Do you need to know machine learning? A basic understanding is enough; however, the rise of generative AI makes data engineers even more strategic: you can’t have good models without good data.

Snowflake, BigQuery, or Databricks? All three dominate the French market; the underlying logic (SQL, modeling, dbt) is largely transferable from one to the other.

📈 Trends

Lead Data Engineer, Data Architect, Head of Data, Machine Learning Engineer.