Lead Software Engineer-java/springboot/aws

JPMorgan Chase JPMorgan Chase · Banking · Wilmington, DE +1 · Corporate Sector

Lead Software Engineer with Java/Spring Boot/AWS expertise in a risk decision team within Consumer and Community Banking. Responsibilities include designing, developing, and troubleshooting software solutions, leading evaluation sessions, and driving awareness of new technologies. Requires strong experience in Java, microservices, distributed systems, CI/CD, AWS, Docker, Kubernetes, and databases.

What you'd actually do

  1. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  2. Develops secure high-quality production code, and reviews and debugs code written by others
  3. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
  4. Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
  5. Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies

Skills

Required

  • Java 17+
  • Spring Boot
  • thread-safe, highly concurrent code on the JVM
  • cloud-native Java microservices
  • large-scale distributed systems
  • real-time and batch processing
  • full Software Development Life Cycle (SDLC)
  • CI/CD
  • automation
  • application resiliency
  • security
  • continuous delivery
  • production excellence
  • event-driven architectures
  • distributed messaging platforms
  • Kafka
  • AWS
  • EKS
  • ECS
  • S3
  • ALB/NLB
  • DynamoDB
  • Aurora
  • Docker
  • Kubernetes
  • high-availability production environments
  • SQL
  • NoSQL databases
  • Oracle
  • PostgreSQL
  • DynamoDB
  • performance tuning at scale
  • infrastructure as code
  • Terraform
  • observability tooling
  • Grafana
  • Splunk
  • Dynatrace
  • Datadog
  • CloudWatch

Nice to have

  • Databricks
  • large-scale data processing platforms
  • mobile technologies
  • mobile application development ecosystems
  • machine learning concepts in production systems
  • advanced SQL
  • advanced NoSQL databases
  • CockroachDB
  • Cassandra
  • data-intensive or analytics-driven applications
  • AI/ML frameworks
  • cloud-native data services
  • modern data architectures
  • real-time decisioning