Software Engineer

AutoTrader.ca•Toronto, ON

16d•CA$110,000 - CA$140,000

About The Position

This role is within the Dealertrack Canada team, the country’s leading automotive financing portal, processing over 6.5 million credit applications annually. Dealertrack Canada is the country's leading automotive financing portal, helping dealers, lenders, and consumers move through vehicle financing with greater speed, trust, and confidence. As part of Trader Corporation and AutoScout24 Group, we are connected to one of the largest automotive marketplaces in the world while working on deeply Canadian fintech problems. Our Fintech engineering teams are modernizing platforms that support millions of credit applications annually. We work on systems for credit application processing, lender and dealer workflows, fraud prevention, risk assessment, customer support, and operational decision-making. We are looking for a Software Engineer who wants to grow with a product engineering team that is raising the bar on reliability, automation, AI-assisted delivery, and service ownership. Automotive financing is changing. Dealers and lenders expect faster decisions, better integrations, stronger fraud controls, cleaner data, and more reliable digital workflows. Our platforms sit in the middle of that ecosystem. In this role, you will help build and modernize the software that powers those workflows. You will not just close tickets. You will learn the domain, understand the customer and operational impact of your work, contribute to solution design, ship production software, and help the team improve how we build and operate systems.

Requirements

Solid experience building production software with modern engineering practices.
Strong fundamentals in clean code, testing, debugging, and maintainable design.
Experience with .NET / C#, or strong willingness to work deeply in a .NET backend environment.
Experience with TypeScript and modern frontend development, ideally React and/or Next.js.
Ability to build modules or simple services from scratch while following existing patterns and standards.
Ability to contribute to technical design discussions and explain trade-offs clearly.
Comfort with production systems, monitoring, incident response, and learning from operational feedback.
Product-minded engineering judgment: you care about the customer and business problem, not only the implementation task.
Quality mindset: you see tests, automation, observability, and maintainability as part of your job.
Security-aware mindset, especially in systems that handle financing, risk, integrations, or sensitive customer data.
Clear communication, constructive code-review habits, and willingness to ask for or offer help.
Practical, responsible use of AI tools while staying accountable for the final output.

Responsibilities

Implement new features and modules using clean, effective, and testable code.
Create and maintain simple services using team and platform standards.
Write unit tests and contribute to automation that improves release confidence.
Improve the code you touch so it becomes easier to understand, operate, and extend.
Understand the business problem, customer outcome, risks, dependencies, and first deliverable slice before starting implementation.
Contribute to technical design discussions with senior engineers.
Raise unclear requirements, technical risks, data questions, or integration concerns early.
Explain your design and implementation choices clearly to your team.
Take responsibility for your work from implementation through release, monitoring, support, and follow-up.
Monitor and analyze team services to detect issues and improve reliability.
Participate in support workflows for services owned by the team.
Use incidents, support feedback, and production signals to improve future delivery.
Treat testing, observability, security, and maintainability as part of engineering delivery.
Think about critical paths and the checks that give the team confidence a change is safe to ship.
Suggest practical code-quality improvements in code reviews.
Include security and privacy best practices when planning and building features.
Pair with engineers, participate actively in code reviews, and share what you learn.
Use AI tools responsibly to improve speed, code quality, documentation, testing, and analysis.
Build stronger domain understanding across automotive financing, fraud prevention, risk decisioning, customer support, and dealer/lender workflows.
Help create the kind of engineering culture where teams move faster because ownership, quality, and clarity are strong.