Software Engineering Expert

AutoTrader.ca•Toronto, ON

1d•CA$149,000 - CA$168,000•Onsite

About The Position

We are TRADER, a Canadian leader in digital automotive solutions. Our flagship brands — AutoTrader.ca, AutoSync, Dealertrack Canada and CMS — help Canadians buy, sell, and finance vehicles with confidence. As part of AutoScout24 group, Europe’s largest online car marketplace, we’re shaping the future of automotive retail in Canada and beyond. This role will be within the AutoSync team. AutoSync supports over 3,500 dealerships nationwide, delivering cutting-edge software tools to simplify operations, boost marketing, and drive sales. The opportunity: We are looking for a hands-on Engineering Expert who will be: setting technical direction across product boundaries while shaping designs, writing and reviewing production code, debugging difficult problems, and helping teams deliver. You will be the senior engineering authority for the Consumer Conversion domain. This is not a purely advisory or governance role. You will spend meaningful time in the systems and delivery work, turning ambiguous requirements into durable architecture, reducing integration risk, and helping teams move from fragmented delivery to a credible path to production and sustainable operation.

Requirements

8+ years of software engineering experience, including significant hands-on technical leadership across complex, customer-facing products.
Demonstrated success owning platform architecture and delivery across multiple teams, systems, or product domains.
Strong language-agnostic full-stack background and experience building scalable web- based applications and integrations.
Practical experience with distributed systems, APIs, data flows, cloud infrastructure, platform capabilities, and modern software architecture patterns; AWS experience is a must.
A track record of improving requirements quality, technical design, release readiness, observability, and operational outcomes through direct engineering involvement.
Experience leading through ambiguity, recovery, staffing transitions, or competing priorities while remaining close to the work.
Proven use of AI-assisted engineering tools in daily work, with sound judgment around verification, security, privacy, and quality.
A genuine commitment to coaching, inclusive leadership, feedback, and the growth of engineers and technical leaders.
Exceptional communication skills and the ability to adapt your approach across engineers, product partners, executives, and customers.
Data-informed decision-making balanced with empathy, pragmatism, and a strong sense of ownership.

Responsibilities

Shape and implement platform architecture: Own the technical shape of domain services and initiatives and its key integrations, covering APIs, data flows, shared services, environments, deployment paths, and operational dependencies.
Be hands-on: prototype or implement critical paths, review production code, debug cross-system failures, pair with engineers, and remove technical blockers.
Make architecture decisions explicit and pragmatic, balancing scalability, performance, security, privacy, resilience, maintainability, cost, and delivery speed.
Lead technical delivery and readiness: Translate product goals into complete, testable requirements, technical specifications, acceptance criteria, dependency maps, and clear decision records.
Own the technical path to green: clarify the critical path, expose risks early, align decision owners, and make milestone confidence visible through evidence.
Drive staging readiness, environment automation, release workflow, rollback planning, production support ownership, and post-launch hypercare.
Protect continuity through documentation, pairing, and knowledge transfer as project staffing and ownership change.
Establish engineering and operational standards: Set a practical definition of done across design, implementation, testing, observability, documentation, rollout, and support readiness.
Embed reliability practices through meaningful SLOs, monitoring, alert quality, runbooks, incident learning, data-quality checks, and measurable post-release outcomes.
Create reusable patterns and lightweight architecture reviews that reduce fragile coupling, duplicated capability, and unsupported operational knowledge without adding unnecessary process.
Multiply engineering capability: Coach engineers and technical leads through architecture, coding, debugging, delivery, operational ownership, communication, and decision-making.
Make the engineering growth framework concrete through clear expectations, actionable feedback, mentoring, stretch opportunities, and evidence of progression.
Help teams adopt AI-assisted engineering safely across coding, reviews, testing, documentation, requirements analysis, and decision support, with appropriate verification and data protection.
Create an environment where people can challenge designs, learn from incidents, and contribute across product boundaries; this role influences people but is not defined as a line-management position.
Connect technical choices to customer and business outcomes: Make complex trade-offs understandable to Product, Design, QA, Data, Sales, Operations, and leadership, and turn decisions into clear action.
Align application and portfolio architecture with customer value, commercial outcomes, and the Consumer Conversion operating model.
Set a high bar for clarity, follow-through, transparency, and respectful challenge across teams.