15+ Years of Experience

9Gunner

Software Engineer & Infrastructure Specialist

Distinguished technology professional with 15+ years of experience architecting mission-critical systems for enterprise environments. Renowned for designing highly scalable, fault-tolerant infrastructures that support millions of transactions. Adept at optimizing system performance, reducing technical debt, and implementing industry best practices across the technology stack. Combines deep technical expertise with strategic vision to deliver exceptional results.

Go Expert
Database Architect
Cloud Specialist
DevOps Engineer
9G

Programming Languages

Specialized in Go programming with complementary expertise in system languages and tools

Golang (Go)

Concurrent Programming

Mastered advanced concurrency patterns including worker pools, fan-in/fan-out pipelines, and bounded parallelism. Deep expertise in leveraging goroutines, channels, and sync primitives to build high-throughput, low-latency distributed systems that efficiently utilize system resources while maintaining data consistency.

API Development

Engineered resilient, high-performance RESTful and gRPC APIs with sophisticated middleware handling for authentication, rate limiting, circuit breaking, and comprehensive observability. Implemented idiomatic Go patterns for clean architecture with clear separation of concerns, facilitating maintainability and scalability.

Advanced Performance Engineering

Expertise in sophisticated performance optimization techniques including escape analysis, mechanical sympathy, CPU cache-friendly data structures, and memory pooling. Applied custom pprof instrumentation to identify bottlenecks and implemented lock-free algorithms for critical paths, achieving 10-100x throughput improvements in high-scale production environments.

Enterprise Microservices Architecture

Architected sophisticated microservices ecosystems with advanced patterns like CQRS, event sourcing, and sagas for distributed transaction management. Implemented comprehensive observability using distributed tracing (OpenTelemetry), structured logging with correlation IDs, and custom metrics for business-level KPIs, enabling real-time operational insights across the service mesh.

Standard Library
Gin Web Framework
GORM
Echo Framework
gRPC
Context Package
Testing
Profiling
Python

Data Analysis

Pandas, NumPy, Matplotlib for data processing and visualization

Automation

Scripting tools for infrastructure management and task automation

SQL

Complex Queries

Designing efficient queries for data extraction and analysis

Database Optimization

Performance tuning, indexing, and query optimization

Database Technologies

Advanced expertise in mission-critical database systems with a focus on high-performance, scalability, and resilience

PostgreSQL
Enterprise-Grade Relational Database Management

Advanced Performance Engineering

Architected high-throughput PostgreSQL clusters handling billions of transactions daily. Expert in query optimization using execution plans, index design strategies, vacuum tuning, and memory configuration. Implemented parallel query execution, partitioning schemes, and materialized views for real-time analytics workloads with sub-second response times.

Advanced Features

JSON data types, full-text search, partitioning, and complex triggers

MongoDB
Enterprise-Scale NoSQL Data Platform

Multi-Model Data Architecture

Designed sophisticated data models implementing domain-driven design principles, with strategic decisions on embedding vs. referencing based on access patterns and consistency requirements. Implemented advanced sharding strategies using hashed and ranged sharding keys to distribute workloads evenly across large-scale MongoDB clusters.

Aggregation Framework

Complex data transformations and analytics using MongoDBMongoDB'sapos;s powerful pipeline

Redis
High-Performance Distributed In-Memory Data Store

Advanced Caching Architecture

Designed multi-tier caching solutions combining Redis with CDNs and application-level caches to achieve sub-millisecond latencies at global scale. Implemented sophisticated cache invalidation strategies using Redis Pub/Sub, achieving near real-time consistency while maintaining 99.99% cache hit rates and significantly reducing database load during traffic spikes.

Data Structures

Using advanced Redis data structures (Streams, Sets, Sorted Sets, HyperLogLog) for complex use cases

Elasticsearch
Distributed Search Engine

Full-Text Search

Implemented complex search functionality with analyzers, tokenizers, and custom scoring

Data Modeling

Optimized mappings and index designs for diverse data use cases

Cloud & Infrastructure

Experience designing and implementing scalable, resilient infrastructure solutions

Cloud Platforms
Enterprise-Grade Multi-Cloud Architecture

AWS

Expert with EC2, ECS, Lambda, S3, RDS, DynamoDB, CloudFormation

AWS Architecture

Designed enterprise-grade cloud infrastructures implementing HA/DR patterns, multi-region deployment strategies, and least-privilege security frameworks. Expert in designing event-driven microservices using Lambda, SQS, EventBridge, and DynamoDB streams with comprehensive observability via CloudWatch, X-Ray, and third-party APM tools.

Google Cloud

GCE, GKE, Cloud Functions, Cloud Storage, BigQuery

Azure

Virtual Machines, AKS, CosmosDB, Blob Storage, Azure Functions

Infrastructure as Code
Automated Provisioning & Configuration

Terraform

Multi-cloud infrastructure provisioning with modules, workspaces, and remote state

CloudFormation

AWS-native infrastructure definition with nested stacks and cross-stack references

Ansible

Configuration management and application deployment automation

Containerization
Containerized Application Deployment

Docker

Container image building, multi-stage builds, optimization, and management

Kubernetes

Container orchestration with deployments, services, ingress, and custom controllers

Advanced Kubernetes Expertise

Architected production-grade Kubernetes platforms with custom CRDs and operators, implemented GitOps with Flux/ArgoCD, and designed highly available control planes with etcd clustering. Expertise in Istio service mesh for advanced traffic management, mTLS, and distributed tracing. Implemented comprehensive security posture with OPA/Gatekeeper, image scanning, and RBAC hardening.

Observability & SRE
Monitoring, Logging & Site Reliability

Monitoring Stack

Prometheus, Grafana, AlertManager, and custom exporters

Logging

ELK Stack (Elasticsearch, Logstash, Kibana), Fluentd, Loki

Distributed Tracing

Jaeger, Zipkin, and OpenTelemetry for application performance monitoring

CI/CD & DevOps Practices

Continuous Integration

  • Argo CD
  • GitLab CI/CD
  • Jenkins

Deployment Strategies

  • Blue/Green Deployments
  • Canary Releases
  • Rolling Updates

Security & Compliance

  • Infrastructure Security
  • CI/CD Pipeline Security
  • Container Security

Case Study

Building a high-throughput backend system that handled 300,000 requests per second

Scalable Backend Architecture
300,000 Requests Per Second: Engineering a High-Performance System

The Challenge

I was tasked with designing and implementing a backend system capable of handling an unprecedented scale for a financial services platform. The requirements were demanding: process 300,000 transactions per second with sub-10ms latency, maintain 99.999% uptime, and ensure data consistency across a distributed architecture. The system needed to validate complex business rules, interact with multiple legacy systems, and scale horizontally during peak loads without service degradation.

Technical Approach

I implemented a service-mesh architecture using Go as the primary language for its exceptional concurrency model and performance characteristics. The solution incorporated:

  • A custom-built, lock-free connection pool for database connections that reduced contention and eliminated connection acquisition latency
  • An optimized binary protocol for internal service communication, reducing serialization/deserialization overhead by 78% compared to JSON
  • Strategically designed data partitioning that eliminated cross-node transactions for 99.7% of requests, significantly reducing distributed transaction overhead
  • Custom memory pooling for high-frequency object allocations, reducing GC pressure and eliminating tail latencies caused by garbage collection pauses
  • A sophisticated circuit breaker implementation with adaptive failure detection that prevented cascading failures during partial outages

Overcoming Key Challenges

Several critical challenges emerged during development:

  • Database Bottlenecks: Initial load tests revealed that our database became a bottleneck at around 80,000 QPS. I implemented a multi-tiered caching strategy with Redis as L1 cache and an in-memory LMAX Disruptor pattern for write operations, reducing database load by 92%.
  • Connection Management: TCP connection exhaustion occurred during load spikes. I developed a custom connection multiplexer that maintained persistent connections to downstream services, reducing connection establishment overhead and improving resilience.
  • Observability Challenges: Traditional logging proved inadequate at this scale. I implemented a sampling-based distributed tracing system using OpenTelemetry with custom sampling logic that captured detailed traces for anomalous requests while maintaining a statistical baseline for normal traffic.

Results & Business Impact

The system went live after six months of development and rigorous testing, delivering exceptional results:

  • Successfully handled sustained loads of 300,000 requests per second with 99th percentile latency of 8.3ms
  • Achieved 99.998% uptime over the first year of operation, exceeding the SLA target
  • Reduced infrastructure costs by 64% compared to the initial capacity planning estimates thanks to the efficiency of the implementation
  • Enabled the business to launch three new product lines that relied on real-time data processing capabilities
  • Provided the foundation for a major competitive advantage, allowing the company to process transactions 27× faster than the industry average

This project demonstrated that with thoughtful architecture, performance-focused engineering practices, and a deep understanding of distributed systems principles, it is possible to build systems that operate reliably at scales previously thought to require much larger engineering teams and budgets.