Senior Lead Software Engineer - Java / AWS

JPMorgan Chase & Co.•Columbus, OH

About The Position

As a Senior Lead Software Engineer at JPMorganChase within the Consumer and Community banking technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Requirements

Formal training or certification on software engineering concepts and 12+ years applied experience
Extensive experience designing, deploying, and managing AWS infrastructure (VPC, EC2, ALB, Elastic Cache, Route 53, S3, IAM) using Terraform for automated, version-controlled provisioning.
Proficient in writing modular, reusable Terraform code for multi-environment infrastructure automation, state management, and CI/CD integration.
Hands-on experience deploying, scaling, and managing containerized applications on AWS EKS, including cluster setup, node groups, Kustomize, Helm charts, and service mesh integration.
Proven track record supporting production platforms with incident response, root cause analysis, troubleshooting, and cross-team collaboration to ensure high availability and reliability.
Skilled in designing and executing load, stress, and performance tests using JMeter and BlazeMeter to identify bottlenecks and optimize application performance.
Proficient in coding in one or more languages - Core Modern Java (Version 17 or higher) (Example: Streams, Multithreading, Collections, and Exception handling mechanism, Lamdas) or Python.
Strong development experience building RESTful APIs, microservices, and enterprise applications using Java and Spring Boot with a focus on scalability and maintainability.
Familiar with SRE principles including SLIs/SLOs/SLAs, incident management, post-mortems, automation, observability, and reliability engineering practices.
Experienced in leveraging Splunk, Datadog, and Dynatrace, Cloudwatch, X-Ray for log aggregation, real-time monitoring, alerting, APM, and performance troubleshooting in production environments.

Nice To Haves

Developer or Solutions Architect Level AWS Certification
Experience with any of these Observability tools: Splunk, Datadog, Dynatrace, or Grafana.
Experience with distributed databases like AWS DynamoDB, AWS RDS Aurora, Cassandra, and Terraform infrastructure-as-code.
Proficiency in multiple modern programming languages (JAVA, Python, SQL)
Proficient in AI-assisted coding workflows (Copilot, Claude Code) and collaborative, iterative development with AI tools.

Responsibilities

Engage with development team throughout agile sprints to develop software for reliability and scale, ensuring minimal refactoring or changes
Identify application patterns and analytics in support of better service level objectives. Design automated software and product upgrades, change management, and release management solutions.
Gain Experience in Operating Services in Public Cloud, Strong grasp of SRE principles; SLIs/ SLOs, error budgets, incident management, observability, and resilience patterns. Hands-on with observability and incident tooling, Proficiency with CI/CD and deployment strategies
Drive prioritized remediation programs across change/configuration, capacity/performance, dependency resilience, and code quality.
Troubleshoot priority and escalation incidents, facilitate blameless post-mortems and ensure permanent closure of incidents and subsequent problem tasks.
Establish comprehensive automated functional testing with dependable regression suites integrated into CI/CD to gate releases; improve reliability and speed through robust test data and include non-functional checks (performance, resilience, accessibility) in pre‑prod and readiness reviews.
Implement demand forecasting, load testing, and performance engineering in pre-prod; validate scale assumptions before peak events.
Run game days and chaos experiments to validate failover, degraded-mode operation, and dependency timeouts.
Embed shift‑left quality and partner with Product to mature testing practices: co‑define clear acceptance criteria and Definition of Ready/Done, align coverage to critical user journeys, and track quality KPIs (defect escape rate, automated coverage on key paths, change failure rate) tied to service objectives and release readiness.
Utilize platform and automation