Lead Software Engineer - Performance Engineer

JPMorgan Chase Bank; Bengaluru; On-site; INR 3,000,000 - 4,500,000; Full time

Job summary

JPMorgan Chase Bank is seeking a Performance Engineer to execute performance engineering across platforms within the Commercial and Investment Bank Payment Performance Engineering team.

You will build and run automated performance tests, instrument observability, and embed performance gates into CI/CD, partnering with architecture, SRE, and application teams to identify and remediate performance risks before production.

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience, with 3+ years in performance engineering for high-traffic distributed systems (web, APIs, microservices, event-driven).
• Hands‑on software engineering experience with Java/Spring Boot; working knowledge of Kubernetes (EKS).
• Working knowledge of workload modeling and statistical analysis of latency/throughput; comfortable with percentile-based SLOs and error budgets.
• Proficiency with load and protocol testing tools such as JMeter and BlazeMeter.
• Scripting skills in Java, Python, or TypeScript for performance automation and execution control.
• Exposure to service virtualization and fault injection (e.g., WireMock, Mountebank, Toxiproxy).
• Experience with observability/APM using Dynatrace and/or OpenTelemetry.
• Experience building dashboards in Kibana and/or Grafana to drive actionable decisions.
• Familiarity with CI/CD and DevOps tooling (e.g., Jenkins, GitLab, GitHub Actions).
• Ability to collaborate across architecture, SRE, and application teams and communicate findings clearly.

Responsibilities

• Contributes to application- and endpoint-level NFRs and SLOs (p95/p99 latency, throughput, ramp profiles, error budgets) under the guidance of the Principal Engineer.
• Designs, builds, and maintains automated test suites for load, stress, soak, spike, and capacity scenarios.
• Executes and analyzes performance test runs, identify bottlenecks, and escalates architectural concerns with supporting evidence. Configures service virtualization and fault injection to validate components when upstream systems are unavailable.
• Runs environment-aware performance test execution (on-commit/overnight) with health checks and actionable sanity tests.
• Builds dashboards and alerts correlating performance test signals with production telemetry against defined SLOs.
• Provides clear reporting on SLO variance, drift, and per-endpoint hotspots using RUM, synthetic, and server-side metrics.
• Helps integrate performance gates into CI/CD pipelines (pre-deploy smoke, post-deploy validation, regression detection).
• Supports chaos and resiliency experiments (CPU, memory, network, latency, dependency failures) and validate autoscaling under load.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the teams.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Skills

Java

Spring Boot

Kubernetes (EKS)

Workload modeling

Latency/throughput analysis

JMeter

BlazeMeter

Java

Python

TypeScript

Service virtualization

Fault injection

Observability/APM

Kibana

Grafana

CI/CD tooling

Jenkins

GitLab

GitHub Actions

Data-platform knowledge

Linux tooling

JVM tuning

Containers

AWS primitives

k6

Terraform

CloudFormation

LLMs in testing

Financial services domain

Tools

Dynatrace

OpenTelemetry

WireMock

Mountebank

Toxiproxy

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.

As a Performance Engineer at JPMorganChase within the Commercial and Investment Bank Payment Performance Engineering team, you will execute performance engineering across our platforms under the direction of the Principal Software Engineer. You will be hands‑on‑building and running automated performance tests, contributing to non‑functional requirements (NFRs) and service level objectives (SLOs), instrumenting observability, and helping embed performance gates into CI/CD. You will partner with architecture, SRE, and application teams to identify and help remediate performance risks before production, growing toward broader technical ownership over time.

Job responsibilities

• Contributes to application- and endpoint-level NFRs and SLOs (p95/p99 latency, throughput, ramp profiles, error budgets) under the guidance of the Principal Engineer
• Designs, builds, and maintains automated test suites for load, stress, soak, spike, and capacity scenarios
• Executes and analyzes performance test runs, identify bottlenecks, and escalates architectural concerns with supporting evidence. Configures service virtualization and fault injection to validate components when upstream systems are unavailable
• Runs environment-aware performance test execution (on-commit/overnight) with health checks and actionable sanity tests
• Builds dashboards and alerts correlating performance test signals with production telemetry against defined SLOs
• Provides clear reporting on SLO variance, drift, and per-endpoint hotspots using RUM, synthetic, and server-side metrics
• Helps integrate performance gates into CI/CD pipelines (pre-deploy smoke, post-deploy validation, regression detection)
• Supports chaos and resiliency experiments (CPU, memory, network, latency, dependency failures) and validate autoscaling under load
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the teams.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience, with 3+ years in performance engineering for high-traffic distributed systems (web, APIs, microservices, event-driven)
• Hands‑on software engineering experience with Java/Spring Boot; working knowledge of Kubernetes (EKS)
• Working knowledge of workload modeling and statistical analysis of latency/throughput; comfortable with percentile-based SLOs and error budgets
• Proficiency with load and protocol testing tools such as JMeter and BlazeMeter
• Scripting skills in Java, Python, or TypeScript for performance automation and execution control
• Exposure to service virtualization and fault injection (e.g., WireMock, Mountebank, Toxiproxy)
• Experience with observability/APM using Dynatrace and/or OpenTelemetry
• Experience building dashboards in Kibana and/or Grafana to drive actionable decisions
• Familiarity with CI/CD and DevOps tooling (e.g., Jenkins, GitLab, GitHub Actions)
• Ability to collaborate across architecture, SRE, and application teams and communicate findings clearly

Preferred qualifications, capabilities, and skills

• Exposure to data-platform performance optimization (e.g., Oracle/JDBC pool tuning, Kafka throughput/partitioning, caching strategies)
• Foundational systems and cloud performance knowledge (Linux tooling, JVM tuning, containers, core AWS primitives)
• Experience with k6 or other modern cloud-native load testing frameworks
• Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation) and autoscaling concepts
• Practical application of LLMs for test generation, anomaly detection, or automated reporting
• Interest in financial-services scale, low-latency systems, and/or regulated environments

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