Leads enterprise software testing and quality engineering strategy across multiple global teams. Oversees automation frameworks, performance and resilience testing, AI-assisted testing, CI/CD integration, synthetic testing, quality metrics, and production issue escalation. Mentors engineering leaders, establishes governance and operating standards, and partners with senior stakeholders to improve reliability, delivery speed, security, and engineering quality.
When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this.
As a Senior Manager of Software Engineering at JPMorganChase within the Commercial & Investment Bank, Payments Technology, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.
Job responsibilities
As a Senior Manager of Software Engineering at JPMorganChase within the Commercial & Investment Bank, Payments Technology, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.
Job responsibilities
- Defines and own the enterprise test automation and quality engineering strategy, aligning with broader engineering and business objectives. Scales and mentor global Quality organization across APAC, LATAM, fostering a “shift Left” culture where quality is shared engineering responsibility
- Provides hands-on technical leadership in designing scalable automation frameworks using Rest Assured (API) and Playwright (UI/E2E), setting standards and best practices across teams.
- Drives the performance engineering roadmap — load, stress, scalability, and resilience testing — using tools such as JMeter, Blazemeter, Gremlin ensuring systems meet enterprise SLAs.
- Drives adoption of AI/GenAI-powered testing practices across the organization (AI-assisted test generation, self-healing automation, intelligent defect triage, predictive quality analytics) to improve engineering velocity and reduce manual effort.
- Deploys advanced synthetic testing to detect issues and latencies in before they impact a single customer in production.
- Partners with senior engineering, product, and business leadership to embed quality and testability into the SDLC from design through production (shift-left/shift-right practices).
- Establishes and report on quality metrics, KPIs, and executive dashboards (defect escape rate, automation coverage, MTTR, performance benchmarks) to senior stakeholders.
- Champions CI/CD-integrated testing practices, ensuring fast, reliable feedback loops across the release pipeline.
- Acts as an escalation point and technical authority for complex production performance issues and quality risk assessments.
- Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
- Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.
- Experience in software testing/quality engineering and in a leadership/management capacity (leading SDET/QE teams).
- Deep hands-on expertise with Rest Assured for API automation and Playwright for UI/E2E automation, with a track record of building frameworks at scale.
- Strong background in performance/load testing strategy and execution (JMeter), including capacity planning and production performance analysis.
- Demonstrated experience driving AI-enabled testing transformation — automation intelligence, GenAI copilots, or ML-based quality analytics. Proven track record building and scaling automation/performance frameworks across multiple teams or business units.
- Strong leadership skills — team building, mentoring, stakeholder management, and executive communication.
- Deep understanding of CI/CD, DevOps practices, cloud infrastructure (AWS/Azure/GCP), and microservices architecture.
- Experience presenting quality strategy and metrics to senior/executive leadership.
- Strong programming background (Java, JavaScript/TypeScript, or Python) with the ability to contribute hands-on when needed.
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