Sheigfred Bello
Optimizing cloud performance, automating infrastructure, and ensuring reliability.
About Me
AWS cloud expert with proven success in cost optimization (15–30% savings), automation (IaC), and monitoring (Datadog, CloudWatch). Skilled in Linux/Windows admin, networking, and CI/CD. Experienced across on-prem and cloud with 100% production uptime.
Skills & Technologies
Cloud & AWS
Systems & Infrastructure
DevOps & Automation
Monitoring & Networking
Experience
DevOps Engineer
Coreware 2025 – PresentAWS cost optimization, automated deployments, custom monitoring, 100% uptime.
System Engineer
Ubiquity 2023 – 20259,000+ desktops, 10-site server deployments, AWS solutions, 99.9% uptime.
IT Support
Ubiquity 2022 – 2023IT support for 800+ users, asset lifecycle, team training.
IT Tech Support
Colegio de San Juan de Letran 2022Campus-wide IT support, asset & network management.
Projects
Enterprise SSO Implementation
Implemented comprehensive single sign-on solution using LDAP, SAML, and OpenID protocols.
Windows 10→11 Migration
Large-scale enterprise migration from Windows 10 to Windows 11 across multiple sites.
Linux Server Deployment
Automated deployment and configuration of Linux servers across multiple environments.
AWS Cost Optimization
Achieved 15-30% cost savings through strategic AWS resource optimization and monitoring.
Cross-Domain Authentication
Implemented secure cross-domain authentication system for multi-site enterprise environment.
AWS CloudWatch Monitoring & Alerts
Designed and implemented end-to-end observability for Flask services using CloudWatch metrics, logs, and alarms. Created dashboards for latency, error rates, and infrastructure health. Alerts routed to Slack/Email with actionable context and runbooks.
ECS Service Auto Scaling (Target Tracking)
Implemented ECS service auto scaling with target tracking on CPU/Memory and request-based policies via ALB metrics. Included cooldown tuning, min/max capacity guards, and safe deployments to keep p95 latency within SLO during traffic spikes.
Datadog Monitoring & Alerting
Integrated Datadog APM, Logs, and Metrics for application and container visibility. Built dashboards (RPS, p95, error rate), SLOs, and composite monitors to reduce alert noise. Added log pipelines and tags for fast root-cause analysis.
Case Studies
🌐 How Users Access This Portfolio
Simple, reliable infrastructure powering your experience
User's Device
You type sheigfredbello.com in your browser
DNS Lookup
Browser asks "Where is sheigfredbello.com?"
Route 53
AWS DNS service responds with server location
A Record
Points to public IP: XX.XX.XX.XX
EC2 Instance
AWS virtual server receives your request
Nginx
Web server processes and forwards request
Flask App
Python application generates the page
HTML/CSS/JS
Browser renders this beautiful portfolio!
🛠️ Technology Stack
AWS CloudWatch Monitoring & Alerts
Problem: Flask services had no proactive monitoring; outages were detected late.
Actions: Built dashboards for latency/error rates, created alarms with Slack/Email routing, and wrote runbooks.
Results: MTTR dropped from ~40 min to 12 min, with clear alert context.
ECS Service Auto Scaling
Problem: High traffic spikes caused 504 errors and service instability.
Actions: Implemented target tracking on CPU/memory & ALB request counts. Tuned cooldowns + min/max guardrails.
Results: Reduced error rate by 70% during peak events; kept p95 latency within SLO (≤300ms).
Datadog Monitoring & Alerting
Problem: Alert fatigue and lack of visibility slowed incident response.
Actions: Deployed APM, built RPS/p95/error dashboards, SLOs, and composite monitors. Optimized log pipelines.
Results: Alert noise reduced by 60%, RCA time cut by 50%.