Software Developer - Engineering Productivity

About the Role

We are looking for a Software Developer, Engineering Productivity with expertise in backend infrastructure, platform engineering, and SDLC observability to join our globally distributed engineering team. You won't just write code to spec — you'll understand the business problem, engage with stakeholders, and shape the solution. In this role, your customers are our own engineers: you will act as a high-leverage technical bridge between Quality and Eng Core, building the automated safety nets and engineering telemetry required to increase deployment velocity while driving code-related incidents to zero.

Our engineering team is spread across time zones, and this role works closely with colleagues in Hong Kong. In practice that means regular early-morning or evening calls — we keep the load shared fairly, but comfort with that rhythm matters for this role.

Responsibilities

• Design and champion internal Quality Programmes: Drive engineering-wide process changes and ensure pods adopt new reliability standards without friction. You will operate with a product-owner mindset to define and evangelize the internal reliability roadmap.
• Build SDLC observability pipelines: Harness the APIs across our existing stack (GitHub, Linear, GCP, Sentry, Grafana, incident.io) to collect, aggregate, and visualize engineering and quality telemetry — delivering self-service dashboards that give Engineering Managers and pod leads clear visibility into delivery efficiency and SDLC bottlenecks.
• Define and enforce hard metrics: Establish and automate reporting for the Key Quality Indicators that objectively describe deployment health — Change Failure Rate, Deployment Frequency, Lead Time for Changes, Time to Restore, Regression Rate, Release Failure Rate, PR-failure rate, and code review depth — creating a quantifiable baseline for platform reliability.
• Architect Shift-Left Pipeline Gates: Weave automated Performance, Security, and Accessibility checks directly into the CI/CD pipeline at the PR level in tight partnership with Eng Core.
• Build Real-World Load Testing: Shift load and performance testing left for every customer onboarding, validating real-world assumptions using tools such as k6, Locust, Gatling, or JMeter.
• Engineer Synthetic Data & Production Canaries: Build the architecture for safe synthetic data injection to unblock heavy load-testing and live-production canaries, strictly isolating test data from authentic user telemetry.
• Leverage Generative AI Tooling: Actively utilize AI assistants (e.g., Gemini, Claude, Cursor, Codex) to accelerate the development of testing frameworks, automate infrastructure code, and design advanced testing architectures.
• Drive Tooling Consolidation: Lead the technical migration away from expensive, legacy testing infrastructure to a unified, AI-supported automation stack — maximizing the value of the platforms we already have rather than introducing unnecessary vendor complexity.
• Help define and maintain development practices: Enable fast iteration while ensuring quality, including writing tests and documenting.