Senior Data Engineer to support the modernization of actuarial pipelines by migrating SAS to Python/PySpark and optimizing performance across AWS and Datab

Our client is actively seeking a Senior Data Engineer to support the modernization of actuarial pipelines by migrating SAS to Python/PySpark and optimizing performance across AWS and Databricks.

Initial 6 month contract with possibility of extension. Hybrid work model - 2 days in office & 3 days remote - open to Montreal and Toronto.

Responsibilities:

• Review and improve performance of data pipelines.
• Optimize PySpark configurations and database access patterns.
• Migrate and optimize legacy SAS code into Python and modern frameworks (e.g., PySpark, Polars).
• Experience deploying and debugging services in the AWS ecosystem.
• Develop and maintain CI/CD automation pipelines.
• Implement lifecycle and cleanup strategies for AWS S3, SageMaker, and Databricks.
• Support ML model lifecycle using MLflow and SageMaker.
• Help integrate Databricks and AWS workflows, including experiment tracking and model deployment.
• Build tooling to help actuaries and data scientists standardize and optimize their workflows.
• Define usage patterns and best practices (e.g., Python vs PySpark vs Polars).
• Recommend architectural changes to improve performance and scalability.
• Act as a technical bridge between actuarial teams, DevOps, and architecture.
• Contextualize and communicate platform needs to DevOps and cloud architects.

Must-Haves:

• 7+ years experience as a Data Engineer
• Strong Python development (libraries, automation, ML tools).
• PySpark and data pipeline experience.
• Cloud-native architecture, particularly in AWS (SageMaker, S3, Lambda, Glue, etc.).
• ML lifecycle tools (e.g., MLflow, SageMaker Experiments).
• Databricks platform experience.
• Understanding of CI/CD and infrastructure-as-code for ML systems.

Nice-to-Haves:

• French communication skills