NOC Analyst | Observability Engineer | SRE Enthusiast
📍 Nairobi County, Kenya
Welcome to my GitHub! I'm a Network Operations Center (NOC) Analyst with 5+ years in financial services, specializing in observability, monitoring, and reliability engineering. I transform complex system data into actionable insights using modern monitoring stacks, automation, and SRE principles to ensure high-availability infrastructure.
- System Observability: Building comprehensive monitoring solutions with ELK, Zabbix, and Grafana
- Site Reliability Engineering: Implementing SLOs, error budgets, and automation for infrastructure resilience
- Security Operations: Integrating security monitoring into observability pipelines
- Infrastructure as Code: Automating deployment and monitoring with Python and Bash scripting
Observability & Monitoring
Data Analytics & ML
Infrastructure & Automation
Network & Security
- Docker roadmap for RHEL environments: secure installs, SELinux considerations, and hardened defaults
- Covers images, networking, storage, logging, and baseline CI/CD patterns
- Practical checklists and commands for ops-ready RHEL container setups
- Trade analytics and import recommendation insights for the Kenyan market
- Data exploration, visualization, and signal extraction for decision support
- Modular notebooks/pipelines for reproducible analysis
- DevOps/SRE interview prep kit with curated Q&A, scenarios, and checklists
- Focus on observability, reliability, automation, and incident playbooks
- AI-augmented notes for faster review and retention
- Python-based detector for unused/orphaned files across 20+ file types
- CI-friendly JSON output, configurable rules, and heuristics for suspicious files
- Streamlit uptime/SLA analytics with Z-score + MAD spike detection
- Pushes SLA reports back to Zabbix; smart caching and history/trends auto-switching
- 99.8% average uptime across monitored services (tracked via custom Zabbix analytics)
- 12 minutes Mean Time to Detection (MTTD) using adaptive spike detection algorithms
- 22 minutes Mean Time to Resolution (MTTR) with automated correlation analysis
- 65% reduction in manual operational tasks through Python automation
- Zero false positive alerts through intelligent filtering and MAD-based anomaly detection
- Production-grade SLA monitoring with automated daily uptime reporting to stakeholders
| Certification | Badge | Status |
|---|---|---|
| ISC2 Certified in Cybersecurity (CC) |  | ✅ Active |
| CompTIA Linux+ |  | ✅ Active |
| CISCO Cybersecurity Essentials | | ✅ Active |
| AWS Solutions Architect |  | 🎯 In Progress |
- Advanced Analytics: Implementing Z-score and MAD algorithms for infrastructure anomaly detection
- SLA Engineering: Building comprehensive uptime calculation engines with automated reporting
- API Integration: Developing robust API clients with OAuth2, caching, and rate limiting
- Statistical Analysis: Cross-host correlation matrices for infrastructure pattern identification
- Performance Optimization: Auto-switching between Zabbix history/trends for optimal query performance
- Production Systems: Deploying enterprise-grade monitoring dashboards with 14-day log retention
- Code Quality Automation: Building dead code detection tools for automated repository maintenance
I'm passionate about advanced observability engineering, statistical monitoring algorithms, and production-grade SLA systems. Whether you're interested in anomaly detection, automated uptime reporting, or building enterprise monitoring dashboards, I'd love to share insights and collaborate!
🔗 LinkedIn: josephkibaki
📧 Email: kibaki.joseph1@gmail.com
💬 Open to: Mentoring, Knowledge Sharing, SRE Discussions
"Observability is not about collecting data—it's about understanding your systems well enough to ask the right questions when things go wrong."
⚡ Building reliable systems, one metric at a time ⚡
