Lead Software Engineer - SAS Viya

You will balance hands-on development with technical leadership—setting direction, raising engineering standards, and enabling predictable delivery across large-scale, distributed application platforms.

As a Lead Software Engineer at JPMorgan

Chase within the Corporate Technology organization, you will lead the design and delivery of scalable, secure, and high-quality applications that power critical business capabilities. You will partner with product managers, architects, and engineering teams to translate customer and business needs into well-architected solutions, drive modern engineering practices across the full software development lifecycle, and coach engineers to deliver features with strong quality, performance, and maintainability.

• Lead end-to-end application development in partnership with site reliability engineering for delivery across the software development lifecycle (plan, design, build, test, release, and operate), ensuring solutions are scalable, secure, and maintainable.
• Drive technical design and architecture for distributed application platforms, making sound trade-offs across performance, resiliency, cost, and delivery speed.
• Build and enhance software capabilities supporting analytics and data platforms, includingSAS Viya (GKP),SAS Viya (AWS), and Databricks (AWS), with clear ownership of integration patterns, automation, and operational readiness.
• Develop and maintain platform and application services that enable onboarding, configuration, environment management, user migrations, and governed access for SAS Viya and Databricks workloads in both on prem and AWS platforms.
• Partner with product managers and stakeholders to refine requirements, define delivery roadmaps, and translate business outcomes into well-scoped epics, user stories, and technical deliverables.
• Establish and enforce engineering standards (coding standards, API contracts, testing strategy, documentation, security controls) and ensure consistent quality through peer review and automation.
• Lead modernization initiatives (refactoring, re-platforming, performance optimization) to reduce technical debt and improve developer productivity and system maintainability.

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Demonstrated experience building and supporting production-grade services, with hands-on ownership of reliability, operational readiness, and root-cause remediation.
• Experience with cloud and containerized platforms and modern deployment practices (e.g., Kubernetes, infrastructure and service automation).
• Proven ability to lead cross-functional incident, problem, and change activities, with clear communication and disciplined follow-through.
• Drive CI/CD and release engineering practices to improve deployment frequency, reduce change risk, and enable repeatable, automated releases across application and platform components.
• Provide technical leadership and mentorship: coach engineers, raise the bar on design and implementation, and foster strong engineering culture and execution discipline.
• Use data (delivery metrics, quality signals, production telemetry, incident trends, usage patterns) to prioritize improvements and measure outcomes against clear service and product KPIs.
• Experience using SAS VIYA, Databricks applications, Kubernetes experience including experience with user onboarding, migrations, troubleshooting, incident and change management.
• Drive team adoption of
  enterprise-authorized AI-assisted engineering practices
  to improve development throughput and quality (e.g., AI-assisted coding/refactoring, code review support, test generation/acceleration, troubleshooting and root-cause analysis support), while setting validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns.
• Apply deep knowledge of the SDLC toolchain (source control, CI/CD, artifact management, scanning, observability, and enterprise-authorized AI-assisted development/automation capabilities) to increase automation, reduce manual effort, and improve delivery outcomes.

• Experience with full stack development and Dev Ops practices, including service ownership models and reliability metrics (e.g., service level objectives).
• Experience operating data or analytics platforms and related tooling (e.g., SAS, including SAS Viya, Databricks administration, or…