Lead Data Engineer

Data Engineer
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


Skills:
Must have :

. Minimum 7 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)
. Best practice of agile methodologies
. Language: French (C1) and English (B2)


Nice to have :

. Experience with vector databases (PGVector Azure AI Search Weaviate...)
. Connaissance des outils MLOps (MLflow feature stores)
. Experience in the industrialization of generative AI use cases
. Knowledge of advanced real time architectures
. Development Practices: TDD
. Experience on Kubernetes

• Artificial Intelligence
• Data Science
• Machine Learning
• AI Data Modeling
• Communication

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