Wells Fargo is seeking a Senior Software Engineer.
In This Role, You Will
Lead moderately complex initiatives and deliverables within technical domain environments
Contribute to large scale planning of strategies
Design, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments
Review moderately complex technical challenges that require an in-depth evaluation of technologies and procedures
Resolve moderately complex issues and lead a team to meet existing client needs or potential new clients needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements
Collaborate and consult with peers, colleagues, and mid-level managers to resolve technical challenges and achieve goals
Lead projects and act as an escalation point, provide guidance and direction to less experienced staff
Required Qualifications
4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Job Expectation
Performance Engineering Expertise
LoadRunner expert: HTTP/HTML, Web Services, Java Vuser.
Deep understanding of Java performance tuning, GC behavior, JVM internals, thread/heap analysis.
Deep understanding of .NET performance tuning, CLR internals, thread pool behavior, async/await performance characteristics, memory/GC modes (Server/Workstation), and heap analysis.
Solid understanding of microservices patterns, REST APIs, asynchronous workflows, Kafka streaming semantics (e.g., consumer lag, backpressure), and large-scale batch processing.
Design and execute load, stress, soak, breakpoint, failover, and Chaos tests.
Influence System Design for Performance: Evaluate architecture, data flows, caching, concurrency models, and REST/Kafka integration patterns to design for scale before code is written.
Deep-Dive Engineering Diagnostics: Use GC logs, thread/heap dumps, profilers, and JVM/OS internals to root-cause latency, memory leaks, deadlocks, and throughput bottlenecks across distributed services.
Leverages AI to automate creation of performance test assets (scripts, data, scaffolding) and accelerate root‑cause analysis by correlating observability signals and generating decision‑ready insights.
Cloud‑Native & Container Platforms
Hands-on with Kubernetes/OpenShift for performance testing microservices.
Analyzes pod-level CPU/memory, HPA responsiveness, and autoscaling dynamics.
Evaluates container limits/requests, node saturation, and cluster-level constraints impacting performance.