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Wealthsimple is Canada’s leading financial innovator. The company offers a full suite of simple, sophisticated financial products across managed investing, do-it-yourself trading, cryptocurrency, tax filing, spending and saving. Wealthsimple currently serves more than 4 million Canadians and holds over $169 billion in assets under administration. The company was founded in 2014 by a team of financial experts and technology entrepreneurs, and is headquartered in Toronto, Canada.

We're proud of what we've built — and we're just getting started. Read our Culture Manual and learn more about how we work.

The Finance, Brokerage and Market Risk Data Science team is a dynamic group of data scientists and analytics developers who deliver high-quality financial metrics and advanced analytics that drive key decisions at Wealthsimple. We support financial planning, treasury operations, liquidity management, and brokerage risk assessment for products like margin trading and options.

Our team has created a solid foundation for data-driven financial decision-making. Now we're ready to scale these initiatives, optimizing financial resources and enhancing risk management as our product offerings grow increasingly sophisticated.

In this role, you'll make a significant impact through advanced analytics that support both strategic financial planning and sophisticated market risk management across our expanding brokerage services.

In this role, you will have the opportunity to

Own the development and maintenance of the finance and market risk data marts, optimizing for performance, data quality, reliability, and efficiency

Design, build, and document robust data pipelines that generate critical financial reports and metrics for stakeholders across the organization

Build, automate, and maintain brokerage reporting & monitoring systems, ensuring data integrity and accuracy across financial processes

Partner closely with business stakeholders in brokerage and market risk to launch and support new financial products and model development initiatives

Create insightful dashboards and visualizations to monitor brokerage KPIs, profitability metrics, and regulatory reporting requirements

Provide business intelligence support for trade and collateral monitoring, financial reporting, product performance analysis, and forecasting initiatives

Translate complex business requirements into scalable analytics solutions that address key financial and risk management needs

Participate in code reviews and contribute to best practices for data engineering and analytics development

Mentor junior team members and share knowledge to elevate the team's technical capabilities

What you bring

5+ years of experience in data engineering or analytics development, with demonstrated expertise in data mart architecture, design, and management

Strong proficiency in SQL with the ability to write complex queries, optimize performance, and work with large-scale datasets

Experience working at brokerage operations or financial services, with knowledge of accounting principles and market risk concepts (preferred)

Familiarity with regulatory reporting requirements and financial product structures (a plus)

Hands-on experience building and orchestrating data pipelines using modern tools such as dbt, Airflow, or similar workflow management platforms

Working knowledge of cloud infrastructure, particularly AWS services (S3, Redshift, Snowflake or equivalent)

Proficiency in Python for data manipulation, automation, and scripting

Experience with version control systems, particularly Git and GitHub, including branching strategies, pull requests, and code reviews

Proven ability to translate complex business requirements into scalable, data-driven solutions

Strong data visualization skills with experience using tools such as Tableau, Looker, Power BI, or similar platforms

Understanding of analytics engineering principles and best practices for building maintainable, well-documented data models

Excellent communication and interpersonal skills, with the ability to explain technical concepts to non-technical stakeholders