Document-Processing Platform at Scale
High-throughput pipeline • Tier-1 system
The problem
Core logistics workflows — dispatch, billing, and document processing — run on Tier-1 backend systems where prolonged outages directly affect business operations. The document pipeline had to sustain very high daily volume without sacrificing latency.
What I built
Designed and scaled a document-processing platform handling 500K–1M transactions per day. Optimized throughput and latency by analyzing batching, threading, and I/O behavior under real production load — not just in staging.
Why it mattered
Architecture ownership for systems the business cannot afford to have down. Every optimization was measured against production behavior in dispatch and billing, where reliability is revenue.
Resilient Fallback & Replay Pipelines
MinIO • Recovery engineering • Data protection
The problem
A downstream outage exposed recovery gaps — when a dependency failed, there was no clean fallback path and no reliable way to replay what was missed without manual intervention.
What I built
A MinIO-based fallback layer with health-check-driven failback and automated replay pipelines to protect data during service interruptions and restore flow once the downstream recovered.
Why it mattered
Turned a single point of failure into a degradable, recoverable system. The pattern — detect, buffer, replay, fail back — is the same discipline that keeps large-scale streaming and content pipelines reliable under partial failure.
API Gateway Standards — Kong
Security & traffic • Platform standards
The problem
Service integrations had inconsistent authentication, rate limiting, and routing. Each team solved the same edge problems differently, making observability and security harder to reason about.
What I built
Established API gateway standards using Kong for authentication, rate limiting, and traffic routing — improving consistency and observability across integrations. Also primary maintainer for internal and external Layer 7 traffic on Kemp LoadMaster, including data-center migration work: re-IP, health checks, routing changes, and controlled cutovers.
Why it mattered
Gateway standards are platform leverage: one well-governed edge pattern replaces dozens of ad-hoc ones. Modernized auth across services with LDAP, Active Directory, and OAuth2/JWT token flows as part of the same effort.
CI/CD & Developer Platform
Developer experience • Delivery reliability
The problem
Recurring deployment failures, manual steps, and legacy monoliths were slowing delivery across multiple squads. Teams needed self-service paths, not ticket-driven platform help.
What I built
Defined CI/CD pipeline standards and deployment automation patterns adopted across engineering teams. Built internal developer tooling and self-service patterns for deployment workflows. Led monolith-to-microservices decomposition, shifted synchronous workflows to Kafka-based event-driven processing with durable handling and replay, and supported migration from WildFly to Docker and OpenShift. Participated in Kafka broker upgrades with minimal disruption.
Why it mattered
Reduced release friction and avoidable deployment errors while improving fault isolation, deployment velocity, and scaling during high-volume periods. Platform work measured by how much faster and safer other engineers ship.
Skills
Built and honed over time
Skills honed over time, reflecting depth across backend engineering, distributed systems, infrastructure, data, and delivery.
Languages & Web
Databases & Storage
Distributed Systems
Platform & Containers
DevOps & Delivery
Reliability, Security & Traffic
Projects
Independent engineering work
Hands-on systems built outside production environments to make architecture, reliability, and operational behavior directly explorable.
Flagship engineering project
StreamForge Control Plane
Distributed systems • resilience • observability
An interactive reliability lab for event-driven systems. StreamForge lets you generate traffic, inject dependency failures and latency, observe Kafka consumer lag and retry pressure, route exhausted events to a DLQ, restore the dependency, and replay failed work through recovery.
Public demo environment uses disposable state by design; the repository also documents the independently deployable reference architecture.
Engineering Principles
How I design for scale and failure
Architecture is more than diagrams. These are the operating beliefs behind how I design, standardize, and recover systems.
Reliability is designed, not patched
Most outages teach you where the system was optimistic. I design for the failure first — fallback layers, replay, health-driven failback — so an incident becomes a degraded mode, not data loss. Leading Sev-1 response taught me that calm systems come from preparation, not heroics.
Standards are a product
A gateway pattern or a CI/CD template only works if engineers actually adopt it. I treat internal platforms like products: clear defaults, self-service paths, and observability built in. When the right way is also the easy way, consistency stops needing enforcement.
Use events where they create leverage
Shifting appropriate workflows to Kafka-based processing improved durability, replay, decoupling, and scaling during high-volume periods. When asynchronous processing improves resilience or reduces tight coupling, I prefer event-driven patterns over long synchronous dependency chains.
Education
Master of Science
Texas A&M University
2014 – 2016
Let’s Build Reliable Systems
For backend, platform, distributed systems, and architecture opportunities:
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- LocationMemphis, TN