About the role

Mission :

Analyze the business and technical needs of data/AI use cases in collaboration with Product Owners and Data Scientists
Design and implement robust, scalable and industrialize data pipelines (ingestion, transformation, exposure)
Ensure data quality, monitoring and traceability
Implement and optimize data architectures (batch and real-time) according to performance and volume constraints
Industrialize data flows to support analytical, ML and generative AI use cases (RAG, feature pipelines, etc.)
Manage data storage and access (data lakes, warehouses, vector databases)
Collaborate closely with Data Science / MLOps teams to facilitate the deployment of models in production
Ensure data security and regulatory compliance
Document pipelines and architectures to ensure maintainability and knowledge sharing
Contribute to the continuous improvement of Data Engineering practices and the standardization of tools and frameworks

Education & Experience

Minimum 6+ years of experience in Data Engineering with large-scale data pipeline production

Strong skills in designing distributed data architectures
Expertise in:
- Data Engineering : ETL/ELT, data pipelines, data quality, orchestration
- Big Data : Spark / PySpark, Azure Data Lake, Databricks
- Stockage : Data Lake, Data Warehouse (Databricks)
- Databases: Advanced SQL
- Streaming: Kafka, Event Hub or similar
Experience on AI/ML use cases with data exposure for models
GenAI (data interactions): understanding of RAG architectures, management of vector DBs
Programming: Python, SQL
Cloud : Azure (Data Factory, Synapse, Data Lake, Event Hub…) ou équivalent
DevOps & DataOps : Git, CI/CD, Docker, orchestration (Airflow, AWX)

Nice to have :

Lead Data Engineer