Walmart Business application supporting the creation and maintenance of business customer accounts, including login, account access, and related account-management workflows.
Supported core account-management capabilities required for Walmart Business customers to create, access, and maintain their business accounts.
Reduced Kubernetes pod CPU allocation by approximately 25% after analyzing real production workload behavior.
Supported production troubleshooting and issue resolution for B2B Account Management and related Walmart Business applications.
Worked across unit, contract, E2E, regression, functional, and performance validation.
Business customers need reliable account-management capabilities before they can use the broader Walmart Business platform.
B2B Account Management supports foundational workflows for creating and maintaining business accounts, logging in, accessing account information, and managing account-related experiences.
Because these capabilities sit early in the customer journey, reliability issues can prevent users from accessing downstream Walmart Business functionality altogether.
Supports workflows required to establish Walmart Business customer accounts.
Supports account-access and login-related customer journeys.
Enables ongoing management of business-account information and related settings.
Provides account capabilities consumed by the broader Walmart Business ecosystem.
Contributed to the Java-based B2B Account Management application.
Supported production issues affecting account-management and related B2B customer journeys.
Worked across unit, contract, E2E, regression, functional, and performance testing.
Participated in production-readiness and operational work for high-traffic periods.
Used Splunk and Grafana to investigate application behavior and production workload patterns.
Profiled production workloads and identified over-provisioned Kubernetes CPU capacity.
Drove an approximately 25% reduction in Kubernetes pod CPU allocation based on observed production utilization.
Participated in troubleshooting and operational coordination across B2B applications and dependent teams.
Used Splunk and Grafana to examine production workload behavior, including traffic during higher-utilization periods.
Compared actual application demand with the CPU capacity allocated to Kubernetes pods.
Found that CPU resources were over-provisioned relative to observed production usage.
Reduced Kubernetes pod CPU allocation by approximately 25% based on production data.
This was one targeted optimization within the broader B2B Account Management application, rather than a standalone infrastructure project.
Worked with unit, contract, and E2E testing to validate application behavior across service boundaries.
Participated in functional, regression, and performance validation for B2B application changes.
Contributed to FY26 Holiday Readiness activities and operational playbooks covering application dependencies and production scenarios.
B2B Account Management was also part of the broader Walmart Business production ecosystem I supported during on-call rotations.
Used logs, monitoring, and application-state analysis to isolate production issues.
Worked with parallel B2B teams when incidents crossed application or service boundaries.
Many on-call issues were associated with B2B-CA and neighboring team applications, rather than being isolated to one project.
Rather than reducing Kubernetes resources based on estimates, I first analyzed real production workload behavior using Splunk and Grafana. The data showed excess CPU allocation, allowing me to drive an approximately 25% pod CPU reduction based on observed application demand.
The application integrated with Walmart's existing authentication and login infrastructure; I did not own the underlying enterprise authentication platform.
My work was at the application workload and resource-configuration level, not Kubernetes cluster administration.
Production support frequently required coordination across B2B-CA and parallel Walmart Business applications.
My contribution: Java application engineering, testing and production readiness, production troubleshooting, observability-driven investigation, and application-level Kubernetes capacity optimization.