Lead Data Engineer

About the role

Industrialize data flows to support analytical, ML and generative AI use cases (RAG, feature pipelines, etc.) Document pipelines and architectures to ensure maintainability and knowledge sharing

What you'll do

• 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
• Manage data storage and access (data lakes, warehouses, vector databases)

What you need

• 6–11 yrs of experience
• Machine learning
• SQL
• ETL
• Python
• LLMs
• Spark
• Any graduate

Good to have

• Kubernetes
• Databricks
• Data warehousing
• Azure Data Factory
• Kafka
• Airflow