Senior Data Engineer - Python, SQL

FP Inc.

Toronto
Contract
Hybrid
C$70 - C$83
Python DevelopmentAdvanced SQLData Frameworks

Role Overview

Join the Retail Risk Analytics Engineering (RRAE) team within Global Banking and Markets Engineering (GBME) at a leading Canadian financial institution. As a Senior Data Engineer, you will play a pivotal role in building certified data stores and developing scalable, extensible data pipelines that serve mission-critical regulatory and non-regulatory credit risk initiatives, including Basel III and IFRS9 modelling.

This is a demanding, hands-on role requiring technical depth and the ability to thrive in complex enterprise data environments. You will work closely with senior quants, model developers, and cloud architects to modernise the bank’s data ecosystem within Google Cloud Platform (GCP).

Key Responsibilities

  • Pipeline Engineering: Design, build, and optimise robust ETL pipelines, data structures, and transformation frameworks to support large-scale modelling workloads.
  • Software Development: Write high-quality, production-grade Python code following industry best practices and object-oriented design principles.
  • Data Transformation: Perform advanced SQL development for complex data ingestion, wrangling, and validation across multiple database environments.
  • System Architecture: Develop scalable and reusable data frameworks, contributing to architecture discussions and producing detailed technical documentation and data flow diagrams.
  • Collaboration: Participate in rigorous code reviews and work cross-functionally with Risk Models, Regulatory Capital, and Enterprise Stress Testing teams.

Required Skills & Qualifications

  • Minimum 8 years of experience in software or data engineering with end-to-end application development expertise.
  • At least 3 years of recent, intensive hands-on Python programming experience.
  • Advanced proficiency in SQL and data manipulation techniques.
  • Experience working within complex enterprise data environments and high-volume data structures.
  • Strong analytical ability and persistence in solving ambiguous technical challenges.

Preferred Qualifications

  • Proficiency with GCP native technologies (BigQuery, DataFlow, Cloud Functions).
  • Experience with Pandas, Numpy, and orchestration tools like Airflow or Tidal.
  • Familiarity with Kubernetes, Docker, and Terraform.
  • Knowledge of Retail Credit Risk or regulatory frameworks (Basel III, IFRS9).