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Lead Software Engineer
lead · Technology / Software Development
Assume a critical role in defining the future of a globally recognized firm and have a direct and significant effect in a realm tailored for top achievers in a tech team.
As a Lead Engineer at JPMorgan Chase within the Enterprise technology, Corporate technology team , you hold a leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. Take lead and conduct design reviews, break up complex problems into digestible work for other engineers, act as a technical lead for medium to large-sized products, and provide advice and mentoring to other engineers.
Job responsibilities
- 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
- Uses enterprise-authorized AI capabilities within the work environment to accelerate reliability design and operational decisioning (e.g., incident/post-incident analysis and requirements traceability), validating outputs and handling operational data according to sensitivity and security requirements.
- Develops secure high-quality production code, and reviews and debugs code written by others
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Demonstrates a high level of technical expertise within one or more technical domains and proactively identifies and solves technology-related bottlenecks in your areas of expertise
- Acts as the main point of contact during major incidents for your application and demonstrates the skills to identify and solve issues quickly to avoid financial losses
- Documents and shares knowledge within your organization via internal forums and communities of practice
- Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., testing/validation automation and production readiness), ensuring traceability/auditability, resiliency, and security controls.
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts plus 12 years of applied experience
- Deep proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction and site reliability best practices with the ability to implement these practices within an application or platform
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve reliability engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to set team practices for safe AI usage in operations (e.g., review/approval expectations and escalation paths) while maintaining resiliency, security, and auditability outcomes.
- Fluency in at least one programming language such as (e.g., Python, Java Spring Boot, .Net, etc.)
- Experience and exposure to observability tools such as white and black box monitoring, SLO alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.
- Proficiency in continuous integration and continuous delivery tools (e.g., Jenkins, GitLab, Terraform, etc.)
- Proficiency in automation and continuous delivery methods
- Experience with container and container orchestration (e.g., ECS, Kubernetes, Docker, etc.)
- Experience with troubleshooting common networking technologies and issues
- Practical cloud native experience
- Ability to identify and solve problems related to complex data structures and algorithms
- Drive to self-educate and evaluate new technology
- Ability to expand and collaborate across different levels and stakeholder groups