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- 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.
