Senior Software Engineering Manager

Salary range: CAD $160,000 to $195,000 annually, eligible for bonus programs when introduced

About the company

We are a SaaS company with a live research data management and analysis platform that brings together multi-modal data- including imaging, genetics, behavioral, and biosample data- for large-scale research studies. Thousands of researchers worldwide rely on our platform to accelerate scientific discovery.

Our diverse team includes experts in medicine, engineering, software development, imaging, epidemiology, and genetics. We foster innovation, collaboration, and continuous learning while empowering scientists to focus on research instead of technology.

About the Role

The team is hiring a Manager of Engineering to lead the engineering group that builds and operates our research data management platform. Reporting to the CTO, you will lead how the platform is designed, built and delivered, and you will be the senior technical authority for the engineering group day to day.

This is a broad, high-ownership role. You will contribute at the level of architecture and direction as well as run delivery. You will stay technical enough to hold the respect of strong developers and to step into hard problems yourself, while working to ensure the health and scalability of the platform.

AI is not optional at our company:

we expect every leader on this team to have already internalized AI and to model that for the organization. So the person we're looking for will bring demonstrated, hands-on fluency with AI: both in how they work (AI-assisted development, tooling, code review) and in what they build (integrating AI capabilities into the platform.)

Set the standard for AI-native engineering practice across the development team: agentic workflows, LLM-assisted development and review, copilot tooling and AI-powered engineering automation as baseline expectations, not optional habits. You are expected to model this yourself, continuously evaluate emerging AI engineering tools and ensure their effective adoption across the organization.

What You Will Do

Organizational and Technical Leadership

• Act as the senior technical authority for day-to-day development decisions and as a trusted partner to the CTO on strategy and vision.
• Lead the development group end to end and take ownership for the technical direction of the company's platform in close collaboration with the CTO.
• Set the standard for AI-native engineering practice across the development team: agentic workflows, LLM-assisted development and review, and copilot tooling as baseline expectations, not optional habits. You are expected to model this yourself and hold the bar for the team.
• See around corners on technology choices, anticipating scaling and integration needs before they become blockers.
• Own standards for code quality, testing and documentation across the development teams.

Delivery and Program Accountability

• Own end-to-end delivery across the development team: planning, prioritization and execution against committed timelines.
• Translate requirements from the Platform Architecture, Solution Architecture and product teams and with business and program goals into technical roadmaps and a clear, sequenced plan of work.
• Carry functional accountability for platform delivery to major programs and data coordinating centres.
• Track progress, raise risks early and keep delivery predictable and transparent.
• Establish and refine processes (Agile or hybrid) that fit the team and the stage of the company.

Architecture and Hands-On Engineering

• Work with the architecture teams to implement technical roadmap
• Provide technical planning and resource allocation for development.
• Stay technically current and go deep where it matters most: design, AI, critical-path work, complex problems and production issues.
• Working with the CTO, security lead and other relevant team members, take the lead on technical architecture for AI integration into the company's platform: retrieval-augmented generation, model serving, evaluation pipelines and responsible deployment of AI capabilities in a research and health data context.
• Continuously improve the engineering development lifecycle, optimizing build performance, test execution, CI/CD pipelines and local development environments to increase developer productivity and delivery efficiency.
• Drive automation across development, testing, deployment and operational workflows, eliminating repetitive manual tasks and enabling engineers to focus on high-value work.

Engineering Excellence and Developer Experience

• Build and evolve internal engineering platforms, shared libraries, frameworks and self-service capabilities that improve consistency, reduce duplication and accelerate software delivery.
• Continuously evaluate, adopt and optimize AI-assisted engineering tools, agentic workflows and intelligent automation that measurably improve developer productivity, software quality and delivery speed.
• Define engineering standards and best practices for the effective, secure and responsible use of AI throughout the software development lifecycle, including code generation, testing, documentation, code review and operational support.
• Drive automation across development, testing, deployment and operational workflows, leveraging AI where appropriate to eliminate repetitive manual work and enable engineers to focus on higher-value technical challenges.
• Establish engineering documentation standards, leveraging AI-assisted documentation where appropriate, to ensure architecture, APIs, engineering practices, operational procedures and development workflows are accurate, discoverable and maintained.

Operations and Reliability

• Build a team culture where operational risk is identified and addressed before it surfaces as a client incident: instill norms of proactive diagnosis, solution ownership and clear escalation, not problem-passing.
• Partner with Security and Cloud Operations on incident response, root cause analysis and the closing of operational gaps before they surface as incidents for clients.
• Hold responsibility for the Development teams' contribution to platform quality and reliability.

Product, Client and Executive Collaboration

• Partner with product and leadership to define the roadmap and make scope and sequencing trade-offs.
• Represent engineering in client, partner and internal meetings, translating between technical and non-technical audiences.
• Be a dependable, senior point of accountability for engineering commitments across the company.

What We Are Looking For

Required

• 10-15+ years in software engineering, including substantial time owning architecture and several years leading and managing engineering teams.
• Has run an engineering team or function end to end, with full accountability for delivery, quality, people and operations.
• Expert, hands-on technical depth: a strong coder and systems architect whom engineers trust and who can go deep when it counts.
• Demonstrated ability to design systems that scale, and to see around corners on architecture and integration.
• You have shipped LLM-integrated features, you work with agentic coding tools as a matter of course, and you can architect AI capabilities that are reliable, observable and auditable in a regulated data environment.
• Real experience in a technology start-up or comparable fast-paced, high-ownership environment.
• Solid grounding in cloud architecture (AWS and GCP), RESTful APIs, CI/CD and observability practices, enabling effective collaboration with Security & Cloud and Release Engineering counterparts.
• Demonstrated success improving engineering effectiveness through developer tooling, CI/CD optimization, automation, documentation and AI-assisted development practices.
• Legally authorized to work in Canada.

Strongly Preferred

• Experience scaling a development group through a high-growth SaaS inflection point: past initial product-market fit, into multi-program, multi-client complexity.
• Background in health care, life sciences, research data or another regulated, compliance-driven domain.
• Familiarity with data engineering and integration patterns (ETL and ELT, BPMN workflow engines such as Camunda, event-driven systems, messaging).

#J-18808-Ljbffr