About the role
Own growth through engineering excellence and experimentation — at a systems level Architect and build paved paths for online experimentation: standardized metrics, guardrails, analysis workflows, and rollout automation that improve reliability and decision quality across teams Lead multi‑workstream initiatives that span teams/products (e.g., unified growth measurement, cross‑surface funnels, experimentation quality improvements) Build and evolve core capabilities: telemetry foundations, experiment assignment/targeting, feature flighting, and risk controls (kill‑switches, guardrails, progressive delivery) Partner with Product, Data Science, Design, and Research to turn ambiguous goals into shippable, measurable systems Stay close to the work: write production code, review designs/PRs, and coach others through architecture and implementation tradeoffs Proven ability to design and ship systemic solutions (platforms, frameworks, primitives) that enable multiple teams to move faster — while remaining deeply hands‑on and detail‑oriented Software engineering fundamentals with experience building, shipping, and operating production services or client applications at scale Demonstrated technical leadership across architecture, service fundamentals, and quality (reliability, performance, operability, security, and maintainability) Track record of translating ambiguous product/business problems into measurable engineering outcomes, with clear hypotheses, instrumentation, and decision criteria High data aptitude: comfortable with metrics, telemetry, and experimental analysis — and able to raise the bar on experimentation rigor and interpretation Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 8+ years building and shipping high‑availability features for multi‑region, globally distributed systems (resiliency, failover, capacity planning) 4+ years of experience in experimentation with emphasis on quality: guardrails, bias detection, metric integrity, rollout risk management, and enterprise grade experimentation Demonstrated experience building experimentation/measurement platforms (feature flags/flighting, assignment, metrics pipelines, analysis tooling) used by multiple teams Demonstrated experience leaning heavily on AI to accelerate engineering velocity, including using AI tools to prototype, implement, debug, and iterate on production systems Cross‑team influence: you create clarity, align stakeholders, and drive execution across organizational boundaries — without becoming a “piece‑mover”
