Leads multiple software quality engineering teams within asset and wealth management. Responsibilities include developing enterprise QE strategies, automated and performance testing frameworks, CI/CD orchestration, load and scalability testing, monitoring and analytics, AWS validation, and bottleneck analysis. Manages 6–8 QEs, partners cross-functionally, ensures compliance, and guides responsible adoption of AI-assisted engineering and SDLC automation practices.
This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions.
As a Manager of Software Engineering at JPMorganChase within the Asset and Wealth Management organization, you lead multiple teams and manage day-to-day implementation activities by identifying and escalating issues and ensuring your team’s work adheres to compliance standards, business requirements, and tactical best practices.
Job responsibilities
As a Manager of Software Engineering at JPMorganChase within the Asset and Wealth Management organization, you lead multiple teams and manage day-to-day implementation activities by identifying and escalating issues and ensuring your team’s work adheres to compliance standards, business requirements, and tactical best practices.
Job responsibilities
- Build holistic QE strategies around multi-year, multi application programs
- Design, develop, and maintain reusable automated frameworks, tooling and utilities . Build CI automation capability
- Design, develop, and maintain automated performance testing frameworks and utilities. Implement and manage CI/CD pipelines for performance test orchestration and automated result generation.
- Execute load, stress, and scalability tests using tools such as JMeter, LoadRunner, Gatling, BlazeMeter, and similar.
- Integrate and leverage monitoring tools (APM, AppDynamics) and logging platforms (ELK Stack, OpenSearch, Splunk, Grafana) for real-time analysis and reporting.
- Collaborate with cross-functional teams to define performance requirements and ensure robust coverage.Analyze test results, identify bottlenecks, and recommend optimizations. Support pre- and post-deployment performance validation in AWS environments.
- People management for around 6-8 technofunctional QEs
- Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across 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 and support capacity unlock initiatives.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering* concepts and 5+ years applied experience. In addition, demonstrated coaching and mentoring experience
- Strong understanding of the AWM functioning and trading
- Strong hands-on experience with AWS stack (EC2, S3, Lambda, CloudWatch, etc.
- Proficiency in performance testing tools: JMeter, LoadRunner, Gatling, BlazeMeter etc. Experience with CI/CD tools (Jenkins, GitLab CI, etc.) and test orchestration.
- Exposure to APM tools (AppDynamics, Dynatrace) and logging/monitoring platforms (ELK Stack, OpenSearch, Splunk, Grafana).
- Solid programming/scripting skills (Python, Java, Groovy, NodeJS, SQL).
- Excellent analytical, communication, and leadership abilities
- Strong programming skills in Python, NodeJS, Java, Groovy, SQL, big data and an appetite to be able work on diverse technology at scale
- Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage.
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