Senior Data Engineer to support the modernization of actuarial pipelines by migrating SAS to Py[...]

S I Systems; Montreal, Toronto; Hybrid; CAD 100,000 - 130,000; Full time

Job summary

A leading company in Montreal is seeking a Senior Data Engineer to modernize actuarial data pipelines by migrating legacy SAS code to Python/PySpark, optimizing for AWS and Databricks platforms. This role, part of an initial 6-month contract with hybrid working options, focuses on enhancing the performance and scalability of data processes while supporting the ML lifecycle.

Qualifications

• 7+ years experience as a Data Engineer.
• Expertise in PySpark and cloud-native architecture in AWS.
• Familiarity with ML lifecycle tools like MLflow and SageMaker.

Responsibilities

• Review and enhance data pipeline performance.
• Migrate SAS code to Python and optimize using modern frameworks.
• Develop and maintain CI/CD automation pipelines.

Skills

Data Engineering

PySpark

AWS

CI/CD

ML lifecycle tools

Databricks

Job description

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

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
• 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.