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Propel Holdings

Analytics Engineer, Risk

Toronto, ON, M5E 1K3, Canada

$70,000 - $100,000 · Full-time

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Job details

Pay: $70,000 - $100,000
Job type: Full-time
Posting language: English
Posted: Oct 23, 2026

Original job description

About Us:

Propel (TSX: PRL) is the fintech company building a new world of financial opportunity by facilitating access to credit for consumers underserved by traditional financial institutions. Through its AI-driven platform, Propel evaluates customers in a more comprehensive way than traditional credit scores can. Our revolutionary fintech platform has already helped consumers access over one million loans and lines of credit and over one billion dollars in credit.

To build a new world of opportunity we bring together the brightest talent to help us build opportunities. We are entrepreneurs and believe in measuring success through results and growing within; talent and hard work never goes unnoticed. At Propel, we are here to change the way employees, customers and shareholders succeed together.

We are a team of passionate entrepreneurs, who foster curiosity and growth in our employees. Our culture is why we have been so successful and why our employees choose Propel to build their careers. It is also why we are one of North America’s fastest growing companies and a Best Place to Work.

Join us as we change the way employees, customers and shareholders succeed together.

About You:

You thrive in a vibrant, entrepreneurial organization where your ideas are valued. You are motivated by goals, a self-starter, and enjoy wearing multiple hats in a fast-growing fintech environment.

You are a builder at heart - someone who prefers creating reliable data pipelines over another dashboard. You thrive on owning the “plumbing” that delivers clean, timely data to machine learning models and enjoy turning manual, messy processes into repeatable automation. As the Analytics Engineer, Risk, you’ll join the Model Development team within Risk, working closely with the Manager, Model Development, Data Scientists, and Model Developers. This highly technical, hands-on role focuses on the data infrastructure supporting credit risk models, including ETL pipelines for model training and scoring, standardized ingestion of new data sources - including UK data - and day-to-day delivery of stable production data. You’ll work primarily in Python and SQL with tabular, time-series, and, as we grow, graph-structured data such as Neo4j.

Responsibilities

Own the integration of new data sources, into Propel’s model-ready data standards, ensuring consistent schemas, definitions, and quality across regions (CA/US/UK)
Design, build, and maintain ETL pipelines that prepare and deliver data for model training, retraining, and scoring across multiple model types, including survival, classification, and regression models
Build and maintain pipelines within the shared ETL/orchestration framework for model development, following the templates and patterns established by the Model Development team so new models can be onboarded quickly
Build feature engineering and data transformation logic for tabular and time-series data, using tools such as tsfresh or similar
Monitor the health and stability of data pipelines feeding models in production; proactively identify and resolve data drift, quality issues, and pipeline failures
Write clean, well-tested, production-grade Python code, including scripts and reusable packages or modules, that other team members can build on
Work with containerized model deployments using Docker and Kubernetes, as well as cloud infrastructure such as AWS and S3 for data storage and workflow orchestration, with support from our Infrastructure team
Query and transform data from relational databases and data warehouses using SQL.
Document data sources, pipeline logic, and transformations clearly so pipelines are auditable, maintainable, and easy to hand off
Collaborate closely with Data Scientists and Model Developers to translate model requirements into scalable, production-ready data pipelines, and with the Business Analytics Engineering and Infrastructure teams where pipelines intersect with broader data platform work

Requirements

Must Have

Bachelor’s degree in computer science, data engineering, statistics, mathematics, or a related quantitative field
Minimum of 3 years of hands-on Python skills and comfortable writing production-quality, testable, reusable code
Familiarity with common data and ML libraries such as pandas, NumPy, and scikit-learn
Experience building or maintaining ETL or data pipelines
Experience with pipeline or workflow orchestration tools such as Metaflow, Airflow, Prefect, Dagster, or similar is a plus
Comfortable working with different data shapes: tabular and time-series data
Solid SQL skills for querying and transforming data into relational databases such as MySQL and/or data warehouses
Understanding of the ML data lifecycle, including feature engineering, training and scoring data preparation, and model monitoring and drift concepts
Experience with version control using Git and basic CI/CD practices
Strong communication and collaboration skills, with the ability to work closely with Data Scientists, Model Developers, and Infrastructure/Engineering stakeholders
Experience in fintech, credit risk, or another regulated or data-intensive industry is a plus
Experience using generative AI tools such as ChatGPT, Claude, or Copilot to accelerate day-to-day engineering work is an asset

Nice to Have

Exposure to graph data or graph databases such as Neo4j (nice-to-have)
Snowflake experience (nice-to-have)
Exposure to Docker, containerization, and cloud infrastructure such as AWS, S3, EKS, or similar services (nice-to-have)

Benefits to Joining Propel

Growth and opportunity – we pride ourselves on promoting from within
Incredible company culture
Competitive salary and health benefits
Comprehensive vacation package
Group health and dental benefits
Group RRSP program
Support for new parents
Diverse and inclusive workplace

Salary Range

$70,000 - $100,000

Final compensation is determined by market conditions, location, and the candidate’s experience, skills, and education. This role may also be eligible for performance-based incentive programs and total compensation may include variable incentives, such as bonuses and commissions.

This posting is for an existing vacancy within our organization.

Our Culture

Propel brings together the brightest talent to build opportunities. We are entrepreneurs who measure success through results and growth from within; talent and hard work never go unnoticed. Our team fosters curiosity and growth, making Propel one of North America’s fastest-growing companies and a Best Place to Work.

Commitment to Diversity & Inclusion

Propel welcomes and encourages applications from all groups, including Indigenous peoples, women, visible minorities, persons with disabilities, people from gender and sexually diverse communities, and those with intersectional identities. Should you require accommodation throughout any stage of the recruitment and selection process, please specify your requirements when submitting your application and we will work with you to meet your needs.

AI Disclosure

We use AI to assist in reviewing applications and assessing candidates. These tools support our recruitment team, however, all hiring decisions are made by our trained hiring managers and recruitment professionals, not AI.

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More about this job

Responsibilities

The Analytics Engineer will design, build, and maintain ETL pipelines to support credit risk models and ensure data quality across regions. They will collaborate with data scientists to translate model requirements into scalable, production-ready data infrastructure.

Requirements

Candidates must have a bachelor's degree in a quantitative field and at least 3 years of experience with Python and SQL. Proficiency in building data pipelines and familiarity with machine learning libraries are essential for this role.

Skills

Python
SQL
ETL pipelines
Data engineering
Machine learning
Data modeling
Feature engineering
Pandas
NumPy
Scikit-learn
Docker
Kubernetes
AWS
Git
Data drift monitoring
Time-series analysis

Benefits

Competitive salary
Health benefits
Group health and dental benefits
Group RRSP program
Support for new parents

Visa sponsorship

Not detected in the job text

Keywords

Fintech
Credit risk
Data infrastructure
Python
SQL
ETL
Machine learning
Data pipelines
Pandas
NumPy
Scikit-learn
Docker
Kubernetes
AWS
S3
Git
Neo4j
Data transformation
Feature engineering
Time-series data
Tabular data
Data drift
CI/CD
Model development
Data warehousing
MySQL
Orchestration
Airflow
Metaflow
Generative AI

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