Risk Analytics Engineer: Data Pipelines & Models

Propel Holdings Inc; Toronto; On-site; CAD 70,000 - 100,000; Full time

Benefits offered by this job

Growth opportunities

Best Place to Work culture

Competitive salary and health benefits

Group health and dental benefits

Group RRSP program

Support for new parents

Diverse and inclusive workplace

Job summary

Propel, a Toronto-based fintech, is seeking an Analytics Engineer, Risk, to own data infrastructure for credit risk models, including ETL pipelines and production data delivery.

You will primarily work in Python and SQL, write clean, testable code, and collaborate with Data Scientists, Model Developers, and Infrastructure to deploy scalable pipelines on AWS.

This fast-paced role offers growth, competitive compensation, and the chance to shape data-driven decision-making across regions.

Qualifications

• Bachelor’s degree in computer science, data engineering, statistics, mathematics, or related field.
• Minimum of 3 years of hands-on Python skills with production-quality, testable, reusable code.
• Familiarity with pandas, NumPy, scikit-learn.
• Experience building or maintaining ETL/data pipelines.
• Experience with orchestration tools (Airflow, Metaflow, Prefect, Dagster).
• Strong SQL skills and experience with MySQL or data warehouses.
• Understanding ML data lifecycle: feature engineering, training, scoring, monitoring.
• Version control (Git) and basic CI/CD practices.
• Collaborative with Data Scientists, Model Developers, and Infrastructure teams.
• Fintech/credit risk or regulated data experience is a plus.

Responsibilities

• Own the integration of new data sources into Propel’s model-ready data standards across CA/US/UK.
• Design, build, and maintain ETL pipelines for model training, retraining, and scoring.
• Develop pipelines within the shared ETL/orchestration framework for rapid onboarding of new models.
• Create feature engineering and data transformations for tabular and time-series data.
• Monitor data pipelines for health, drift, and quality; resolve issues proactively.
• Write clean, production-grade Python code and reusable modules.
• Work with Docker and Kubernetes, and AWS S3 for data storage and orchestration.
• Query and transform data using SQL across relational DBs and data warehouses.
• Document data sources and pipelines for auditability and handoff.
• Collaborate with Data Scientists, Model Developers, and Infrastructure teams on data platform work.

Skills

Python

ETL pipelines

SQL

pandas

NumPy

scikit-learn

Git

CI/CD

Data modelling

Education

Bachelor’s degree in computer science, data engineering, statistics, mathematics, or a related quantitative field

Tools

Airflow

Metaflow

Dagster

Docker

Kubernetes

Job description

Propel, a Toronto-based fintech, is seeking an Analytics Engineer, Risk, to own data infrastructure for credit risk models, including ETL pipelines and production data delivery.

You will primarily work in Python and SQL, write clean, testable code, and collaborate with Data Scientists, Model Developers, and Infrastructure to deploy scalable pipelines on AWS.

This fast-paced role offers growth, competitive compensation, and the chance to shape data-driven decision-making across regions.