Build and maintain scalable, resilient AWS infrastructure using Terraform as modular, single-purpose stacks, supporting core services such as EKS, RDS (including Oracle), ElastiCache, S3, EFS, Amazon MSK (managed Kafka), KMS, Secrets Manager, EventBridge, SNS, CloudWatch, and Lambda
Support Amazon EKS lifecycle operations, including cluster version upgrades, node group and add-on updates, and remediation with minimal disruption to production workloads
Monitor and support the health of the AWS estate, including triage, root-cause analysis, remediation, and participation in incident and problem management processes
Contribute to the build and maintenance of CI/CD pipelines (e.g., Jenkins) that apply infrastructure changes safely and reliably
Instrument the estate for observability using tools such as Datadog, CloudWatch, and Dynatrace, and use telemetry insights to support improvements to infrastructure hygiene
Uses enterprise-authorized AI capabilities within the work environment to accelerate design comprehension and coding support (e.g., drafting unit tests and documentation), validating outputs and handling data according to sensitivity and security requirements
Contribute to server-side development on the Spectrum Portfolio Management Fixed Income product (Java and/or Kotlin, Spring Boot) as your skills expand
Support a team culture of diversity, inclusion, opportunity, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and expanding applied experience
Foundational, hands-on experience with Infrastructure as Code using Terraform — writing, structuring, and maintaining stacks to provision and manage cloud resources
Working knowledge of core AWS services including EC2/networking (VPC), IAM, RDS, S3, KMS, Secrets Manager, and CloudWatch
Foundational experience with Amazon EKS and Kubernetes, including deploying workloads and supporting cluster operations
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Practical experience working with cloud infrastructure on AWS in a production or near-production environment
Familiarity with production readiness practices including observability (metrics, tracing, logging) and incident management in distributed systems
Working knowledge of using enterprise-authorized AI capabilities within the work environment to support software engineering workflows with strong validation habits and awareness of data sensitivity
Ability to review and validate AI-assisted code and technical recommendations before use, escalating when uncertain and following security and data handling requirements
Preferred qualifications, capabilities, and skills
Experience with relational databases in the cloud (e.g., Oracle on RDS, SQL fundamentals) and event-driven messaging platforms (e.g., Amazon MSK/Kafka, EventBridge, SNS)
Familiarity with containerization and platform engineering patterns (Docker, Kubernetes, Helm) and scripting for automation (e.g., Python)
Familiarity with server-side development in Java and/or Kotlin using the Spring Boot ecosystem
Exposure to multi-region or disaster-recovery architecture patterns with automated failover
Domain awareness in financial services, including capital markets, trading workflows, or portfolio management concepts
About JPMorgan Chase
Banking323028 employees
Global financial services and investment banking firm.