DevOps and Cloud Engineer

Architected and deployed an observability platform using Zabbix, Prometheus, Grafana, Helm, and Terraform across more than 500 infrastructure and application components, improving monitoring coverage by 90%. Established SLI/SLO frameworks, distributed tracing standards, and Gatus health checks for over 20 services, reducing troubleshooting time by 45% and accelerating incident resolution by 40%.

Built and maintained more than 100 Grafana dashboards, alerting policies, and monitoring resources as Terraform code. Implemented centralized tracing with Jaeger, OpenTelemetry Collector, and Elasticsearch, reducing MTTR by 50%; optimized alerting to reduce alert fatigue by 60%; and helped drive a 30% reduction in production incidents through blameless post-mortems, RCA practices, and operational runbooks.

Migrated 95% of application servers from VMware to the Nutanix private cloud, reducing downtime by 40%. Provisioned infrastructure as code using Terraform and GitHub-integrated workflows, saving more than 15 hours per week of manual operations.

Designed enterprise monitoring solutions with Grafana and the ELK Stack, reducing operational downtime by 75%. Automated deployment and data workflows with Python, SQL, and Logstash, and architected scalable microservice systems with Kong API gateway integration.

Automated more than 15 Databricks production pipelines, achieving a 99%+ success rate across over 500,000 daily records. Implemented GitHub branching, pull request, and code-review standards to improve engineering quality.

Built Python and SQL automation scripts that reduced data processing time by 40%. Modernized legacy VBA infrastructure and eliminated identified compliance security vulnerabilities.

Deliver data analysis, statistical methods, and Python sessions for graduate-level students. Support more than 20 students annually and contribute to a reported 90% pass rate.

Provide instructional assistance in analytics-related coursework and help students develop practical skills in Python and quantitative analysis.

GPA: 4.00/4.00. Relevant studies include predictive analytics, machine learning, deep learning, big data analysis with SQL, regression analysis, Python for machine learning and data mining, and database management concepts.

GPA: 3.65/4.00.