yoinka

Lead Software Engineer - Full Stack

JPMorgan Chase

San Francisco, CA, United StatesSeniorH-1B sponsor company
Sign in to applyVerified 1h ago
Location
San Francisco, CA, United States
Work model
On-Site
Level
Senior
H-1B history
1,524 approvals (FY2023)
Posted
20h ago

Skills

AgileAirflowGrafanaJavaScriptKafkaKubernetesPythonReactSplunkTypeScript

About this role

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 – Full Stack at JPMorgan Chase within the Enterprise Technology - Network Services 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. 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. This role is hands-on and suited for seasoned full stack engineers who can own end-to-end delivery—from system design through implementation, deployment on Kubernetes, and production operations (monitoring, troubleshooting, and performance tuning).

Job responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems Owns end-to-end full stack delivery across; Frontend (React/TypeScript), Backend (Python services), Data/Workflow Services (Apache Airflow - DAG design) and Database (CockroachDB - data modeling) Designs and delivers scalable, highly available services and user experiences for large-scale applications; drives architecture decisions that improve throughput, latency, reliability, and operability Builds and maintains cloud-native deployments on Kubernetes, including configuration, scaling strategies, and operational readiness (health checks, rollouts, rollback strategies, capacity considerations) Drives monitoring, observability, and performance tuning across the stack using tools such as Splunk and Grafana (and related logging/metrics/tracing patterns); leads root-cause analysis and remediation Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems 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 Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Demonstrated full stack engineering capability (frontend + backend) and ability to deliver independently across the SDLC Strong proficiency in Python and experience building microservices and automation frameworks; working knowledge of JavaScript for tooling/UI/integrations and strong React experience building production-grade web applications (performance, usability, maintainability) Hands-on experience deploying and operating workloads on Kubernetes (deployments, services, scaling, configuration, troubleshooting) and hands-on experience with API gateways, Kafka, and event-driven architectures Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security. Strong