Lead Software Engineer-Full Stack

JPMC Candidate Experience page
New York, NY

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. 

Job responsibilities

  • Leads design and implementation of complex features and services across upstream/downstream services; define API contracts, versioning, and backward compatibility.
  • Owns production readiness and operational excellence: define SLOs/SLIs, drive observability (logs, metrics, traces), manage incidents, and reduce MTTR through durable fixes and runbooks.
  • Champions secure by default and resilient designs: timeouts, retries with exponential backoff/jitter, circuit breakers, bulkheads, idempotency, graceful degradation.
  • Conducts and facilitate design reviews and consult with Architecture team for enterprise alignment with partner teams to minimize integration risk.
  • Writes secure, concise, high quality production code and review/debug code written by others across backend and frontend.
  • Engages in pair programming, rigorous code reviews, and reliable estimation; maintain performance budgets and accessibility standards for user facing experiences.
  • Applies database rigor across SQL/NoSQL: schema design, indexing, query tuning, and caching to improve latency and throughput
  • Integrates firm approved, privacy safe AI assistants into day to day development to accelerate test generation, documentation, refactoring, and static analysis triage—always with human in the loop review and CI quality gates.
  • Establishes guardrails and governance for AI use: prompt hygiene, secrets/PII protection, logging/provenance for generated code, and objective acceptance criteria: lint clean, coverage thresholds, Static App Security Tests/Dynamic App Security Tests, performance checks.
  • Defines and track measurable outcomes from AI assisted workflows: developer throughput, review cycle time, defect density without compromising security or reliability.
     

Required qualifications, capabilities, and skills

 

  • Formal training or certification in software engineering concepts and 10+ years of applied experience, including leading delivery for a squad or stream of work.
  • B.S. Computer Science/Engineering or related field.
  • Proficiency in Java and Spring Boot development, SQL, SNS, SQS, and Kafka
  • AWS and cloud‑native services (e.g., ECS/EKS/Lambda, API Gateway, RDS/DynamoDB/CassandraDB, S3, CloudWatch, IAM)
  • Proficiency in Javascript, HTML 5, CSS3
  • Hands‑on experience in system design, RESTful JSON API design, micro services, application development, testing strategy, and operational stability within large, distributed corporate environments.
  • Hands-on experience across the Software Development Life Cycle (SDLC) including CI/CD pipelines automated testing
  • Understanding of security and resiliency: OSWASP Top 10, 0Auth2/OIDC, secrets management, encryption in transit/at rest; resilient integration patterns: timeouts, retries, circuit breakers, bulkheads.
  • Ability to lead design reviews, guide architectural decisions, and mentor engineers.

     

  • AI‑led development: operational experience integrating approved AI assistants into development workflows with governance, human review, and measurable outcomes.

 

Preferred qualifications, capabilities, and skills 
  • Experience in .NET/C# 
  • Experience in Typescript and React 
  • Travel or hospitality domain experience, especially with booking flows, inventory consistency, and resilient integrations.
  • Experience introducing evaluation frameworks for AI‑assisted development or AI features (offline metrics, canarying, telemetry, drift detection).
  • Familiarity with frontend performance and accessibility practices.
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