Venn
Chief Anti-Money Laundering Officer
Toronto · Exec
Sponsorship not specifiedDetected 93 days ago
Project ManagementCommunication
About the role
- About Venn Venn is the fastest-growing B2B fintech in Canada.
- Business owners are tired of the big banks' monopoly and are looking for modern tools that replace the legacy systems that have been their only option for over 100 years.
- We've raised over $26.5M from Tier 1 investors such as Gradient, Left Lane Capital, Intact, and XYZ Venture Capital.
Responsibilities
- Develop, implement, and monitor internal compliance policies and procedures.
- Lead regulatory reporting and liaise with authorities as needed.
Requirements
- 6+ years in compliance, AML, or risk.
- Strong knowledge of PCMLTFA, RPAA, and other regulatory frameworks.
- Experience in a high-growth fintech is a strong asset.
- Experience acting as a CAMLO.
- Knowledge with SQL.
Nice to have
- Certified compliance designation (e.g., CAMS) is a plus.
Skills
- Stay up to date with relevant regulations and ensure company-wide adherence.
- Conduct internal audits and risk assessments.
- Train staff on compliance and regulatory best practices.
- Support due diligence and third-party vendor reviews.
- Investigate and resolve compliance issues or breaches.
Compensation
- Competitive base salary with an uncapped commission structure, allowing you to directly benefit from your success.
Benefits
- Competitive base salary with an uncapped commission structure, allowing you to directly benefit from your success.
- Large equity ownership, among the most generous in the market, aligning your success with the company's growth.
- Comprehensive coverage of medical, dental, and vision insurance.
Company info
- This role is for an existing vacancy and we are looking to fill this role immediately.
Equal opportunity
- Venn is an equal opportunity employer and does not discriminate on the basis of race, colour, religion, sex, national origin, age, or disability.
This listing is sourced directly from Venn's careers page and normalized into a canonical job model.