Performance Engineering Cloud Lead

Morgan Stanley•New York, NY
•$120,000 - $170,000

About The Position

The Performance Engineering Cloud Lead is a Director-level position in Morgan Stanley’s Technology division and the Performance, Performance Analytics & Management (PPAM) organization. The role establishes and drives the Performance Engineering strategy for migration to Google Cloud Platform (GCP), serving as a subject matter expert in cloud-native technologies and providing technical leadership across performance, scalability, resiliency, observability, capacity planning, and performance certification. The position bridges existing Performance Engineering capabilities with emerging cloud architectures by developing standards, tooling, automation, capacity models, performance guardrails, and certification methodologies. It partners with Architecture, Engineering, Infrastructure, and Platform teams to evaluate cloud architectures, lead performance proof-of-concepts and benchmark studies, establish engineering standards, and certify target architectures. The role helps ensure cloud-hosted applications meet performance, scalability, resiliency, capacity, and customer-experience objectives while reducing cloud migration risk and supporting modernization.

Requirements

  • 10+ years of experience in Performance Engineering, with strong expertise supporting distributed systems and large-scale customer-facing applications.
  • Hands-on experience with Google Cloud Platform (GCP) and cloud-native architecture.
  • Strong knowledge of Kubernetes/GKE, cloud networking, cloud databases, containers, and observability platforms.
  • Demonstrated experience defining performance strategies for cloud migration or large-scale technology modernization initiatives.
  • Deep understanding of scalability and resiliency patterns, including autoscaling, caching, asynchronous processing, event-driven architectures, multi-region deployments, and cell-based architectures.
  • Strong understanding of performance modeling, benchmarking, capacity planning, bottleneck analysis, and non-functional testing.
  • Hands-on experience with performance and observability technologies such as LoadRunner, JMeter, Grafana, Prometheus, Dynatrace, AppDynamics, Splunk, or equivalent platforms.
  • Experience defining and executing load, stress, endurance, scalability, failover, and resiliency testing.
  • Strong analytical and troubleshooting skills across multiple layers of the technology stack.
  • Ability to influence senior Engineering and Architecture stakeholders through data-driven technical recommendations.
  • Excellent written and verbal communication skills, including the ability to translate complex technical findings into clear architectural recommendations.

Responsibilities

  • Define and drive the Performance Engineering strategy for GCP transformation, embedding performance, scalability, resiliency, and capacity considerations into architecture decisions.
  • Partner with Architecture, Engineering, Infrastructure, and Platform teams to evaluate cloud-native designs and deployment patterns, including microservices, Kubernetes/GKE, service mesh, caching, messaging, and data platforms.
  • Lead performance assessments, proof-of-concepts, benchmarks, and comparative architecture studies, providing data-driven recommendations on latency, throughput, scalability, resiliency, and cost-performance tradeoffs.
  • Establish cloud performance engineering standards, methodologies, guardrails, and non-functional requirements for capacity, resiliency, failover, autoscaling, and observability.
  • Design performance validation frameworks for load, stress, endurance, scalability, failover, chaos, and resiliency testing.
  • Establish performance baselines and capacity models for application growth and efficient operation across multi-region and multi-cell cloud architectures.
  • Partner with development teams to identify and remediate performance bottlenecks across application, middleware, database, network, storage, container, and cloud infrastructure layers.
  • Evaluate and introduce performance testing, monitoring, observability, and automation capabilities to support continuous performance validation throughout the software delivery lifecycle.
  • Define performance certification criteria and governance to validate cloud workloads against service-level objectives before production rollout.
  • Use cloud-native telemetry and observability data to develop performance insights, dashboards, forecasting models, and capacity recommendations.
  • Provide technical leadership and mentorship across the Performance Engineering organization, building expertise in cloud performance engineering and cloud-native architecture.
  • Communicate performance risks, architectural tradeoffs, certification results, and readiness assessments to senior technology stakeholders.

Benefits

  • Comprehensive employee benefits and perks
  • Potential commission earnings, incentive compensation, discretionary bonuses, and other short- and long-term incentive packages
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