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.
Programming Languages
Specialized in Go programming with complementary expertise in system languages and tools
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.
Data Analysis
Pandas, NumPy, Matplotlib for data processing and visualization
Automation
Scripting tools for infrastructure management and task automation
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
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
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
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
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
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
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
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.
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
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
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.