Performance Engineer (Analyst)

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

As Ralph Lauren continues to expand its digital and AI-driven customer experiences, the Performance Engineer (Analyst) will also support the performance, observability, and delivery of AI-enabled services and traffic patterns. This includes monitoring AI and LLM-driven traffic, evaluating the impact of generative AI integrations on customer experience, and ensuring that both human and AI consumers of Ralph Lauren's digital platforms receive reliable, scalable, and optimized experiences.

This role is ideal for an analytical and technically skilled professional who is passionate about web performance, observability, Core Web Vitals, and modern digital experiences. The successful candidate will help establish performance best practices, support release validation activities, and contribute to the overall performance governance program across Ralph Lauren's global digital ecosystem.

• 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.

• Monitor and analyze AI and LLM-generated traffic patterns to ensure optimal platform performance, scalability, and resource utilization.

• Support performance strategies for AI-enabled customer experiences, search capabilities, recommendation engines, and future generative AI integrations.

• Partner with engineering and architecture teams to establish 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.

Required Qualifications

• 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.

Preferred Qualifications

• Experience with Akamai CDN, EdgeWorkers, or other edge delivery platforms.

• Experience supporting Next.js applications and composable commerce architectures.

• Experience with Datadog APM, RUM, Logs, and Synthetic Monitoring.

• Experience with Catchpoint synthetic testing and performance automation.

• Familiarity with HarperDB or modern distributed data platforms.

• Experience integrating performance testing into CI/CD pipelines.

• Familiarity with BrowserStack, Selenium, Storybook, Lighthouse, or related testing frameworks.

• Experience supporting high-traffic e-commerce websites and digital customer experiences.

• Understanding of release validation, feature flagging, and performance governance practices.

• Understanding of emerging AI, LLM, and generative AI technologies and their impact on web performance, observability, scalability, and digital customer experiences.

• Familiarity with monitoring and analyzing AI-driven traffic, bot behavior, API consumption patterns, and AI-enabled applications.

• Experience leveraging AI-assisted capabilities within observability platforms to improve operational efficiency and accelerate issue detection and resolution.

• Knowledge of performance considerations related to AI-powered services, inference workloads, API integrations, and large-scale automated traffic patterns is a plus.