Software Developer - Engineering Productivity

About the role

This role focuses on enhancing the internal developer experience by building systems that improve software delivery speed and reliability. You will create automated safeguards and telemetry to reduce code-related issues and increase deployment frequency.

Key facts

What you'll do

• Design and implement internal quality programs to guide engineering teams toward adopting new reliability standards.
• Develop pipelines to collect and visualize engineering telemetry from tools like GitHub, Linear, GCP, Sentry, Grafana, and incident.io.
• Establish and automate reporting for key quality metrics such as Change Failure Rate, Deployment Frequency, and Lead Time for Changes.
• Integrate automated performance, security, and accessibility checks into the CI/CD pipeline at the pull request stage.
• Build systems for real-world load and performance testing, including handling synthetic data for safe testing.
• Utilize generative AI tools to accelerate development of testing frameworks and automate infrastructure code.
• Lead the migration from legacy testing infrastructure to a consolidated, AI-supported automation stack.
• Contribute to defining and maintaining development practices that balance speed with quality.

Requirements

• Minimum 5 years of software development experience, with proficiency in languages like Python or Go.
• Experience working with developer infrastructure and production telemetry, including processing data from tool APIs.
• Proven ability to instrument and measure quality metrics like Change Failure Rate, Deployment Frequency, or Lead Time for Changes.
• A product-oriented mindset with a focus on understanding business impact.
• Experience building and refactoring complex systems, including declarative infrastructure as code.
• Familiarity with public cloud platforms such as GCP or AWS.
• Experience with load and performance testing tools such as k6, Locust, Gatling, or JMeter.
• Experience using AI tools for testing and architecture, with strategies for verifying AI-generated code.
• Strong communication and collaboration skills.

Nice to have

• Experience with synthetic data generation and production canaries.

Skills & tools

• Python, Go
• GitHub, Linear, GCP, Sentry, Grafana, incident.io (or equivalents like GitLab, Jira, Datadog)
• k6, Locust, Gatling, JMeter
• Gemini, Claude, Cursor, Codex (or similar AI assistants)

Practical notes

• Salary range: 115,000 CAD - 145,000 CAD per year.
• This role requires regular participation in early morning or evening calls with colleagues in Hong Kong.
• Comprehensive benefits package including medical, dental, optical, and mental health support.
• Professional development funding and mentorship opportunities.
• Remote-first culture with office setup reimbursement.
• Paid parental leave.