Sr Manager of Software Engineering

JPMorgan Chase JPMorgan Chase · Banking · Columbus, OH +1 · Consumer & Community Banking

Senior Manager of Software Engineering at JPMorgan Chase, leading multiple technical teams in the Consumer & Community Banking division. The role focuses on platform strategy and modernization, including AI enablement for efficiency, anomaly detection, and automation. Responsibilities include managing platform strategy, championing engineering best practices, leading the design of high-throughput, low-latency applications leveraging machine learning architectures on AWS, and developing secure, scalable microservices. Requires formal software engineering training/certification and experience leading technologists, with practical experience in system design, application development, testing, and operational stability, particularly with Java-based microservices on AWS and near real-time stream processing.

What you'd actually do

  1. Manage platform strategy and modernization (cloud-first, data-first), including AI enablement for efficiency, anomaly detection, and automation of controls.
  2. Champion engineering best practices for stream processing (Flink), microservices (Java/Python), API design, CI/CD, observability, and resiliency.
  3. Leads design of high-throughput, low-latency applications leveraging state-of-the-art machine learning architectures deployed on AWS
  4. Designs and develops secure, scalable microservices, and reviews and debugs code written by others
  5. Creates architecture and design artifacts for complex components and platform capabilities

Skills

Required

  • software engineering concepts
  • leading technologists
  • system design
  • application development
  • testing
  • operational stability
  • Java based microservices-based applications
  • AWS
  • EKS
  • ECS/Fargate
  • S3
  • Kafka
  • Kinesis
  • Flink
  • agile methodologies
  • CI/CD
  • application resiliency
  • security best practices
  • Git
  • Bitbucket
  • SVN
  • cloud
  • AI/ML
  • automation
  • continuous delivery methods
  • SDLC

Nice to have

  • high-volume Flink processing
  • IntelliJ/Eclipse
  • Maven
  • Gradle
  • Spring Boot
  • Spring MVC
  • Spring Cloud
  • recommendation and personalization systems
  • financial services domain

What the JD emphasized

  • high-throughput, low-latency applications
  • machine learning architectures
  • AWS
  • secure, scalable microservices
  • near real-time streaming and event-driven processing
  • Kafka, Kinesis, and Flink
  • cloud-native experience on AWS
  • highly scalable Java based microservices-based applications

Other signals

  • AI enablement for efficiency, anomaly detection, and automation of controls
  • machine learning architectures
  • AWS
  • near real-time streaming and event-driven processing
  • cloud-native experience on AWS