Sr Manager of Software Engineering

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.

As a Senior Manager of Software Engineering (Vice President) at JPMorganChase within Consumer & Community Banking (CCB) – Personalization & Insights (P&I), you will lead one or more engineering teams delivering platform and product capabilities that enable personalized customer experiences. You will partner with product and business leaders to shape technology strategy and roadmap, drive predictable delivery, and ensure solutions meet core non-functional requirements (high availability, resiliency, low latency, and scale) with strong engineering excellence and controls.

Job Responsibilities

• Lead one or more engineering teams and own day-to-day execution against product priorities, driving predictable delivery and removing impediments
• Provide hands-on technical coaching, mentoring, and guidance on implementation approaches, tasks, and best practices
• Build and sustain an effective team culture (including opportunity, inclusion, and respect) and support hiring, development, and recognition of talent
• Partner with product and business leaders to define technology strategy, roadmap, and integration patterns with adjacent product areas
• Break down complex and ambiguous business problems into implementable technical designs; anticipate dependencies and drive successful cross-team delivery
• Champion engineering excellence and governance across the SDLC (automation/CI/CD, testing including automated functional testing, performance, resiliency, scalability), with accountability for controls and operational health
• Design and deliver reusable technical solutions (including performant APIs processing high-volume payloads) using microservices, domain-driven design, data modeling, and event/streaming architectures across cloud/hybrid/on-prem environments
• 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 in software engineering concepts and 5+ years of applied experience
• Demonstrated coaching and mentoring experience, including leading technology projects and Agile teams (scrum/backlog execution with product owners)
• Strong hands-on experience at the code level, engineering systems for high availability, resiliency, low latency, and scale
• Proficiency with modern backend stack including Java and Spring/Spring Boot; experience with distributed systems and cloud/hybrid/on-prem platforms, especially AWS
• Expertise with streaming/messaging and big data ecosystems (Kafka and Spark, including Spark SQL, tuning, and architecture), plus high-throughput messaging beyond Kafka (e.g., Kinesis)
• Experience with relevant AWS big data/services (e.g., Lambda, Glue, EMR), NoSQL databases (e.g., Cassandra, MongoDB), and cloud-native patterns/services for big data
• Deep expertise in front-end engineering: React, TypeScript/JavaScript, HTML/CSS, micro-frontends, reusable component libraries, design systems, accessibility/responsive design, RESTful APIs/state management, Git, and automated UI testing (e.g., Jest, React Testing Library)
• 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.

Preferred qualifications, capabilities, and skills

• Flink experience
• Stream processing