The leadership capability
As Senior Manager for Walmart’s Streaming Engines organization, I was accountable for the operational health of a cloud-native and hybrid portfolio that grew to more than 3,500 workloads. Its public-cloud footprint included roughly 70 Kubernetes clusters—approximately 35 in GKE and 35 in Azure—while OpenStack supported Kubernetes capacity on bare-metal infrastructure. Together, these environments represented approximately $26 million in annual infrastructure.
For five years, I served as the team’s subject-matter expert for operational dashboards, alerting, golden signals, and runtime-data analysis. I wrote PromQL, built and reviewed Grafana views, examined workload and infrastructure behavior, and helped decide which signals should page an engineer, inform a service-level objective, or become an input to capacity and roadmap decisions.
Operational capabilities across the platform lifecycle
The operating scope extended from fleet architecture and service health to production recovery and investment decisions. Technical evidence was consistently connected to customer impact, operating risk, cost, organizational capacity, and roadmap priority.
GCP and GKE operations
Kubernetes-based streaming data planes operated across approximately 35 GKE clusters. Cloud Logging and Cloud Monitoring supplied Google Cloud system, audit, application-log, and node-metric context, correlated through a shared Prometheus, Grafana, Mimir, and Splunk layer.
Platform services and team scope
Spark Streaming jobs, Kafka Connect deployments, controlled Flink workloads, Kubernetes infrastructure integrations, deployment and GitOps paths, observability, alerting, recovery automation, upgrades, access controls, and production support.
Monitoring and service health
Golden signals, fleet and cluster health, workload metrics, tenant attribution, SLO inputs, alert severity, local-versus-global collection, telemetry retention, cardinality, scrape behavior, and dashboard-to-log correlation.
Production support
24×7 on-call coverage, PagerDuty and xMatters routing, Slack-based investigation, support-ticket trends, runbooks, vulnerability remediation, recovery planning, and deciding when operational demand required roadmap work.
Incident response
Establishing the impact window, identifying affected clusters and workloads, separating initiating events from downstream symptoms, testing competing hypotheses, coordinating mitigation, communicating risk, and turning RCAs into accountable systemic changes.
Leadership decisions
Runtime evidence informed decisions to prioritize reliability over features, correct chargeback, identify infrastructure savings, evaluate vendor exposure, clarify control-plane and data-plane responsibilities, and advise executives on platform investment.
The full observability path
The operating model connected metrics, logs, traces, profiles, tickets, and incident reviews rather than treating them as separate tools. Prometheus collected platform and workload metrics close to Kubernetes clusters. Mimir supplied a global metrics layer; Grafana and PromQL supported fleet, capacity, adoption, and cost views; and Splunk supported centralized log search.
GKE Cloud Logging and Cloud Monitoring supplied Google Cloud system, audit, application-log, and system-metric context. Azure Log Analytics and Application Insights provided corresponding Azure views. Fluent Bit, node exporter, kube-state-metrics, JMX exporters, Kafka Connect and Spark/Flink telemetry, Coroot, eBPF signals, and JVM profiling enabled investigation from fleet health down to a single workload.
Local resilience with global visibility
Local Prometheus collection preserved detection close to workloads when a network or global dependency was impaired. Central aggregation enabled fleet-wide rules and governance across GCP, Azure, and OpenStack-backed environments. Grafana links carried cluster, namespace, pod, application, connector, tenant, and time-range context directly into Splunk searches, reducing the work required to reconstruct an investigation.
Tools and services used
These were parts of the operating environment and investigation workflow—not a technology-logo inventory and not a claim that I personally implemented every component.
Google Cloud
Google Kubernetes Engine (GKE), Cloud Logging, Cloud Monitoring, Google Cloud system and audit telemetry.
Azure
Azure Kubernetes Service (AKS), Azure Log Analytics, Application Insights, and cloud-level node and platform signals.
Metrics and visualization
Prometheus, PromQL, Mimir, Grafana, golden signals, platform APIs, and cost and capacity views.
Logs and collection
Splunk, Fluent Bit, OpenTelemetry, node exporter, kube-state-metrics, JMX exporters, and Kubernetes events.
Runtime analysis
Spark and Flink metrics and event logs, Kafka Connect metrics, Coroot, eBPF signals, JVM profiling, and workload tools.
Operations and dependencies
PagerDuty, xMatters, Slack, email routing, ArgoCD and GitOps, Vault, Akeyless, persistent volumes, and OpenStack-backed bare metal.
Telemetry as a management system
I used observability to answer more than whether a service was healthy. The same evidence could expose unused capacity, an inaccurate chargeback model, licensing dependency, unclear ownership, or a roadmap risk.
Reliability
Availability, latency, errors, saturation, pages, incidents, recovery time, and dependency health.
Economics
Compute, memory, disk, network, licenses, telemetry storage, utilization, and attributed tenant cost.
Customer impact
Affected workloads, tenants, business processes, support demand, and confidence in financial signals.
Organizational health
On-call load, engineering toil, repeated diagnosis, ownership boundaries, staffing capacity, and burnout risk.
Specialized practice
Agentic lifecycle governance
From design through production
See how I extend observability into task evaluation, tool-call accuracy, hallucination detection, agent traces, cost control, promotion, and rollback—grounded in the MakeItOurs architecture.
Explore the operating framework →Cross-layer incident leadership
Production events could originate in a streaming job, connector, Kubernetes node, persistent disk, secrets-management dependency, cloud maintenance event, upgrade, or shared control plane. I worked from impact toward cause: establish the incident window, find the first abnormal signal, correlate Kubernetes events and application logs, test competing hypotheses, coordinate recovery, and build a unified RCA timeline.
The objective was not merely to publish an RCA. Repeated problems needed to produce a better alert, recovery automation, runbook, upgrade control, clearer RACI, or accountable product-backlog item.
Evidence in practice
These examples are not separate projects. They show how the same observability capability informed decisions across the published leadership initiatives.
Inventory and vendor economics
Live Kafka Connect inventory
Production telemetry and connector configuration established actual licensing exposure, feature-parity requirements, and migration scope.
See Kafka Platform Transformation →Platform investment · 2019
Kafka versus Event Hubs
Approximately one year of runtime data tested a proposed platform replacement against observed demand and comparative economics.
See the 2019 decision analysis →Capacity and FinOps
Kubernetes bin packing
Prometheus data identified unused capacity and supported a roadmap decision that improved infrastructure efficiency.
See measured platform results →Operating accountability
Reliability, chargeback, and ownership
Telemetry informed alert reduction, incident controls, trusted cost allocation, and clearer control-plane and data-plane responsibilities.
See the portfolio operating model →SRE management perspective
The cost to operate includes infrastructure, reliability, human, customer, and change costs. Metrics, logs, traces, and profiles provide the evidence; runbooks, automation, architecture, staffing, and prioritization are the response. My approach is to observe the complete system, connect technical signals to customer and financial impact, restore service decisively, and make every repeated operational problem cheaper to detect, diagnose, and prevent.
Continue the conversation
Review my LinkedIn profile or contact contact@bengesoftwarellc.com.