ABOUT CLUTCH

We’re on a mission to reinvent how people buy, sell, and own cars. Are you game?
Clutch is Canada’s largest buyer and seller of cars, offering a digital-first, hassle-free experience. Customers can browse thousands of cars online, have their chosen vehicle delivered right to their door, and enjoy peace of mind with our 10-Day Money-Back Guarantee.
Looking to sell? We buy over $1 million worth of cars every day, providing a firm, AI-generated offer every 15 seconds—and when you sell to us, you get paid instantly, now that’s Clutch.
Recognized four years running on The Globe & Mail’s list of Canada’s Top Growing Companies—and with back-to-back spots on Deloitte’s Technology Fast 50™ and Fast 500™—we’re seeking curious, hard-working, and driven individuals to join our growing team.
Founded in 2017 and headquartered in Toronto, Clutch currently operates in Ontario, New Brunswick, Nova Scotia, and Prince Edward Island. Our world-class investors include Altos Ventures, Canaan, FJ Labs, BrandProject, and Upper90. To learn more, visit clutch.ca.

ABOUT THE ROLE

As a Senior Data Analyst, you’ll own the design and delivery of analytics that support our day-to-day operations and long-term growth. You’ll partner with operations, product, and leadership teams to measure performance, optimize processes, and uncover opportunities. This is a hands-on role where you’ll move fluidly between data wrangling, analysis, dashboarding, and strategic problem solving.

KEY RESPONSIBILITIES

• Partner with operations and business stakeholders to identify KPIs, build reporting, and track performance across manufacturing logistics functions.
• Develop scalable dashboards and self-service tools that empower non-technical teams to access insights.
• Dive into complex datasets to identify inefficiencies, bottlenecks, and opportunities for process improvements.
• Lead data investigations to support key operational and strategic decisions.
• Mentor junior analysts and set best practices for analytics across the team.
• Collaborate with data engineering to improve data infrastructure, ensuring reliability, accuracy, and scalability.
• Translate findings into clear recommendations and influence decision-making at the leadership level.