Performance Test Architect

Role Description

Amtech is seeking an experienced Performance Test Architect to define and drive the performance engineering strategy for enterprise-scale, distributed, cloud-native applications. The ideal candidate will bring deep expertise in performance testing, workload modeling, capacity planning, observability, application bottleneck analysis, and production-readiness validation.

Key Responsibilities

Performance Strategy & Architecture

• Define performance engineering strategies, standards, NFRs, KPIs, SLAs/SLOs, workload models, and release criteria.
• Participate in requirements, architecture, database, infrastructure, and design reviews to identify performance risks early.
• Design and execute baseline, load, stress, spike, endurance, scalability, capacity, volume, failover, and resilience tests.
• Develop realistic workload models based on customer usage, business volumes, production traffic, concurrency, and forecasted growth.
• Develop reusable performance solutions using JMeter, k6, Gatling, Locust, LoadRunner, NeoLoad, or similar tools.
• Establish production-readiness and performance quality criteria for major releases.

Performance Analysis & Optimization

• Analyze response time, throughput, latency, concurrency, error rates, CPU, memory, network, database, and infrastructure utilization.
• Identify bottlenecks across UI, APIs, microservices, databases, messaging, network, infrastructure, containers, and cloud platforms.
• Correlate logs, APM data, distributed traces, database metrics, infrastructure telemetry, and application code to identify root causes.
• Perform JVM, threading, connection-pool, caching, timeout, retry, queue, and autoscaling analysis.
• Perform SQL performance analysis including execution plans, indexing, locking, blocking, waits, I/O, and connection pooling.
• Partner with engineering teams to recommend, implement, and validate performance optimizations.
• Lead root-cause analysis of production performance issues and recurring regressions.

Scalability, Capacity & Resilience

• Validate horizontal and vertical scalability, autoscaling, application stability, and infrastructure capacity.
• Develop capacity models and recommend infrastructure sizing based on workload growth and utilization trends.
• Analyze Kubernetes pods, CPU/memory limits, HPA behavior, database capacity, thread pools, connections, and queue depths.
• Validate application behavior during peak workloads, infrastructure saturation, downstream failures, failover, and recovery.
• Support capacity planning and production readiness for major launches and business-critical events.
• Identify cloud and infrastructure optimization opportunities while maintaining performance and reliability.

Automation & Continuous Performance Testing

• Build reusable performance testing frameworks, utilities, and automated regression suites.
• Integrate performance testing into CI/CD pipelines using Jenkins, GitHub Actions, Azure DevOps, GitLab CI, or equivalent tools.
• Automate test data preparation, execution, monitoring, analysis, baseline comparison, and reporting.
• Establish automated performance quality gates and regression detection.
• Track performance baselines and trends across releases to proactively identify degradation before production.

Observability & Front-End Performance

• Define observability requirements and create dashboards for performance analysis and production-readiness assessments.
• Use tools such as Grafana, Prometheus, Splunk, Datadog, Dynatrace, New Relic, AppDynamics, Kibana, and InfluxDB.
• Evaluate browser performance, Core Web Vitals, page rendering, network activity, and responsiveness using Chrome DevTools, Lighthouse, WebPageTest, or Playwright tracing.

Quality Engineering & Technical Leadership

• Collaborate with Quality Engineers, Developers, Architects, Database, DevOps/SRE, and Infrastructure teams on functional and non-functional quality.
• Produce clear performance reports highlighting findings, trends, risks, capacity constraints, and recommendations.
• Mentor engineers on performance engineering practices and promote a performance-first quality mindset.
• Represent Performance Engineering in architecture, design, and release-readiness reviews.
• Introduce approved AI-assisted performance engineering approaches for workload modeling, script generation, log analysis, result interpretation, RCA, and defect investigation.

Skills & Qualifications

• Bachelor's degree in computer science, Engineering, Information Technology, or equivalent experience.
• 12+ years of Quality/Performance Engineering experience, with significant hands-on performance engineering expertise.
• Experience as a Performance Architect, Principal Performance Engineer, Performance Lead, or equivalent senior technical role.
• Strong experience with JMeter, k6, Gatling, Locust, LoadRunner, NeoLoad, or equivalent tools.
• Strong knowledge of workload modeling, scalability, capacity planning, resilience, and performance analysis.
• Experience with On-Premises, SaaS, cloud, hybrid, microservices, and distributed applications.
• Strong understanding of HTTP, REST APIs, GraphQL, WebSockets, caching, messaging, and asynchronous processing.
• Strong SQL and database performance troubleshooting skills; experience with SQL Server, Oracle, MySQL, or PostgreSQL is desirable.
• Experience with Java/JVM performance concepts including GC, memory, threading, and concurrency.
• Experience with observability/APM platforms and distributed tracing.
• Working knowledge of Linux, Windows Server, networking, containers, Kubernetes, AWS and/or Azure.
• Experience integrating performance tests into CI/CD pipelines.
• Programming or scripting experience in JavaScript, Java, Python, C#, Shell, or similar languages.
• Strong analytical, troubleshooting, communication, mentoring, and stakeholder-management skills.

Preferred Experience

• High-throughput and highly concurrent enterprise applications.
• SaaS, retail, telecom, e-commerce, messaging, or customer-facing platforms.
• Kafka, RabbitMQ, Redis, Elasticsearch, or similar distributed technologies.
• Cloud capacity optimization and infrastructure right-size.
• Resilience and production-readiness engineering.
• AI-assisted performance analysis and engineering automation.