Lead Data Engineer
Role Overview
Analyze and deliver data engineering solutions for business and technical data/AI use cases.
Build scalable, production-grade data platforms supporting analytics, machine learning, and generative AI.
Key Skills for This Role
Full Job Posting
Role mission
Analyze and deliver data engineering solutions for business and technical data/AI use cases.
Build scalable, production-grade data platforms supporting analytics, machine learning, and generative AI.
Duties
- Analyze data and AI use-case needs with Product Owners and Data Scientists.
- Design and implement robust, scalable pipelines for ingestion, transformation, and exposure.
- Ensure data quality, monitoring, and traceability; optimize batch and real-time architectures for performance and volume.
- Industrialize data flows for analytics, ML, and generative AI, including RAG and feature pipelines.
- Manage data lakes, warehouses, vector databases, and access.
- Partner with Data Science and MLOps teams to deploy models in production.
- Ensure data security and regulatory compliance; document pipelines and architectures.
- Improve and standardize Data Engineering practices, tools, and frameworks.
Required qualifications
- At least 7 years of Data Engineering experience delivering large-scale production pipelines.
- Strong distributed data architecture design skills; expertise in ETL/ELT, data pipelines, data quality, and orchestration.
- Experience with Spark/PySpark, Azure Data Lake, Databricks, data lakes or warehouses, advanced SQL, and Kafka, Event Hub, or similar.
- Experience with AI/ML data use cases and understanding of RAG architectures and vector database management.
- Python programming; Azure services or equivalent cloud; Git, CI/CD, Docker, and orchestration such as Airflow or AWX.
- Knowledge of agile methodologies.
- French at C1+ and English at B2+ proficiency.
Nice-to-have qualifications
- Vector database experience with PGVector, Azure AI Search, Weaviate, or similar; MLOps experience with MLflow or feature stores.
- Generative AI industrialization, advanced real-time architectures, TDD, or Kubernetes experience.
About CGI
CGI describes a culture of ownership, teamwork, respect, and belonging, with opportunities to develop skills and work with global capabilities.
AI tools may assist recruitment assessment, but hiring decisions remain with recruitment professionals.
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