Software Developer - Engineering Productivity
New
J
JobgetherSecurity & IT
USFull-TimeMiddle
SalaryCompetitive base salary range of approximately CAD $115,000 - $145,000, depending on experience, skills, and market factors.
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonGCPGoCI/CDGitHub
Requirements
- 5+ years of software engineering experience with proficiency in one or more programming languages such as Python or Go.
- Experience working with developer infrastructure, platform engineering, production telemetry, and engineering systems at scale.
- Strong ability to integrate, query, and analyze data from engineering tools and APIs such as GitHub, Linear, cloud platforms, monitoring tools, or equivalent technologies.
- Hands-on experience establishing engineering metrics, reliability indicators, SLIs/SLOs, error budgets, or software delivery analytics.
- Strong understanding of distributed systems, infrastructure automation, and declarative Infrastructure as Code (IaC) practices.
- Experience working with cloud platforms such as GCP, AWS, or similar environments.
- Familiarity with load testing, performance engineering, synthetic data generation, and safe testing practices.
- Experience using AI development tools such as Gemini, Claude, Cursor, Codex, or similar technologies to enhance engineering workflows.
- Ability to evaluate and validate AI-generated code, infrastructure changes, and automated solutions before production use.
- Product-oriented mindset with the ability to understand business objectives and connect technical solutions to measurable outcomes.
Responsibilities
- Design and implement internal engineering productivity initiatives that improve software quality, reliability, and development efficiency across engineering teams.
- Build and maintain SDLC observability solutions by integrating data from engineering tools, APIs, and platforms to provide actionable insights into delivery performance.
- Develop dashboards and reporting systems that measure key engineering health indicators, including deployment frequency, change failure rate, lead time, recovery time, regression rates, and release quality.
- Architect and implement automated quality gates within CI/CD pipelines, incorporating performance, security, accessibility, and reliability checks.
- Create scalable load testing strategies using tools such as k6, Locust, Gatling, or JMeter to validate system performance and customer readiness.
- Develop safe synthetic data solutions and production canary approaches to support testing while protecting real user data and telemetry.
- Leverage AI-powered development tools to accelerate engineering workflows, automate repetitive tasks, and improve testing and infrastructure processes.
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