Manager of Software Engineering - Java, AWS

As a Manager of Software Engineering at JPMorganChase within the Consumer and Community Banking, 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

• Lead complex public cloud migrations, proactively identify potential issues, and implement risk mitigation strategies to ensure the success of multiple cloud initiatives.
• Collaborate with product and engineering teams to deliver robust, cloud-based solutions that improve customer experiences.
• Take ownership of platform issues from end to end, resolve challenges related to platform build and performance on AWS Cloud, and ensure high-quality, bug-free deliverables.
• Develop strategies and provide guidance to product teams on public cloud onboarding standards and best practices, ensuring migrations are successful and compliant with all regulatory requirements.
• Drive the adoption and effective use of Amazon’s utility computing services (e.g., AWS EC2, S3, RDS, CloudFront, EFS, DynamoDB, CloudWatch, EKS, ECS, MFTS, API Gateway, VPC Peering).
• Design resilient, secure, and high-performance platforms in the public cloud, following JPMC best practices.
• Measure and optimize system performance, anticipate customer needs, and drive continuous innovation. Provide operational support and engineering for the public cloud platform, troubleshoot and optimize systems, and automate routine tasks.
• Work closely with cross-functional teams to develop practical solutions and ensure positive user experiences at every touchpoint. Manage costs by designing and optimizing public cloud platforms and technologies. Organize and lead game days, resiliency tests, and chaos engineering exercises.
• Apply expertise in programming languages such as Java, Python, SQL, Node, Go, and Scala; work with open source RDBMS and NoSQL databases; utilize container orchestration tools like Docker and Kubernetes; and leverage a wide range of AWS services.
• 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
• A strong understanding of business technology drivers and their impact on architecture design, performance and monitoring, best practices
• Design and building web environments on AWS, which includes working with services like EC2, ELB, RDS, DynamoDB, EKS, ECS, MFTS, SQS/SNS, S3 and Route53
• Proficiency in few modern technologies such as: Java version 8+, Spring Boot, Restful Microservices, AWS or Cloud Foundry, Kubernetes.
• Experience using DevOps tools in a cloud environment, such as Ansible, Artifactory, Docker, GitHub, Jenkins, Kubernetes, Maven, and Sonar Qube
• Experience using monitoring solutions like CloudWatch, Prometheus, Datadog, Splunk, Dynatrace. Experience and knowledge of writing Infrastructure-as-Code (IaC), using tools like CloudFormation or Terraform
• Experience with one or more public cloud platforms like AWS, GCP, Azure
• Experience with high volume, SLA critical applications and their interdependencies with other applications and databases. Experience with high volume, mission critical applications, and building upon messaging and or event-driven architectures
• Keen understanding of financial industry and their IT systems and optimization of Public Cloud expenses
• 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.