Performance Engineer (Analyst)

Job Summary

Ralph Lauren Corporation is seeking a Performance Engineer (Analyst) to support the optimization, monitoring, and continuous improvement of our global digital commerce platform.

About the Role

This role will focus on ensuring fast, reliable, and measurable customer experiences across our composable architecture built on Next.js, HarperDB, and Akamai. The Performance Engineer (Analyst) will work closely with Engineering, Architecture, QA, Product, and Platform teams to identify performance opportunities, implement monitoring solutions, automate testing processes, and provide actionable insights using observability platforms including Datadog, Catchpoint, and Akamai mPulse.

Responsibilities

• Monitor, analyze, and improve application performance across browser, CDN, edge, API, and origin layers.
• Support performance optimization initiatives for Next.js applications, APIs, and composable commerce experiences.
• Configure, maintain, and enhance monitoring solutions utilizing Datadog, Catchpoint, and Akamai mPulse.
• Create and maintain dashboards, alerts, reports, and automated performance monitoring workflows.
• Analyze Core Web Vitals, user experience metrics, and synthetic monitoring data to identify performance opportunities and risks.
• Partner with development teams to improve rendering performance, caching effectiveness, and overall application responsiveness.
• Assist with CDN and edge optimization efforts, including cache strategy validation and performance analysis.
• Support performance testing activities across multiple regions, devices, browsers, and network conditions.
• Develop and maintain automated performance validation processes within CI/CD pipelines.
• Participate in release readiness reviews, performance testing, and post-deployment validation activities.
• Investigate performance incidents, conduct root cause analysis, and provide recommendations for remediation.
• Generate weekly and monthly performance reports that correlate synthetic, real user monitoring, and observability data.
• Collaborate with Engineering, Architecture, QA, and Product teams to establish and maintain performance standards and governance processes.
• Continuously evaluate new tools, technologies, and best practices related to performance engineering and digital experience monitoring.
• Maintain observability and monitoring standards for AI-powered applications and services.
• Leverage AI-assisted observability capabilities within Datadog and other monitoring platforms to accelerate anomaly detection, root cause analysis, and performance troubleshooting.
• Analyze the impact of AI crawlers, bots, and automated consumers on application performance, caching effectiveness, and infrastructure utilization.
• Support initiatives that optimize traffic segmentation, delivery, and performance for both human users and AI consumers across the digital ecosystem.

Requirements

• 3–5+ years of experience in Performance Engineering, Performance Analysis, Site Reliability Engineering, Application Monitoring, or a related technical discipline.
• Experience working with observability and monitoring platforms such as Datadog, Dynatrace, Catchpoint, Akamai mPulse, New Relic, or similar tools.
• Strong understanding of Core Web Vitals including LCP, INP, and CLS, as well as browser performance metrics such as TTFB, FCP, and Speed Index.
• Working knowledge of modern web technologies including React, Next.js, server-side rendering, and API-driven architectures.
• Experience analyzing performance across browsers, devices, networks, APIs, and backend services.
• Understanding of CDN concepts, caching strategies, cache-control headers, and edge delivery optimization.
• Experience creating dashboards, alerts, reports, and performance monitoring solutions.
• Basic scripting and automation experience using JavaScript, Python, or similar languages.
• Strong troubleshooting, analytical, and problem-solving skills.
• Excellent verbal and written communication skills with the ability to present technical findings to both technical and non-technical audiences.