DevOps Engineer with Cloud & CI/CD Expertise

Naveen Reddy DevOps Engineer

NJ, USA +1-732-***-**** ***********************@*****.*** LinkedIn SUMMARY

DevOps Engineer with nearly 4 years of experience automating CI/CD pipelines, managing AWS, Azure, and GCP environments, and deploying applications with Docker and Kubernetes. Proficient in Terraform, Jenkins, and monitoring tools including CloudWatch, Prometheus, Grafana, and ELK. Enhanced deployment success by 30%, accelerated release cycles 40%, and decreased MTTR 25%. Expertise in IAM, RBAC, and Vault, delivering secure, reliable, and scalable enterprise systems. TECHNICAL SKILLS

Cloud Platforms: AWS (EC2, S3, Lambda, VPC, IAM), Azure (VM, Functions, Storage, Key Vault), GCP (Compute Engine, Cloud Storage, IAM)

CI/CD & Automation: GitHub Actions, GitLab CI/CD, Jenkins basics, Terraform, CloudFormation, Ansible, Bash, Python, YAML Containers & Orchestration: Docker (build/run/push), Kubernetes basics (pods, deployments, services, Helm charts) Monitoring & Logging: AWS CloudWatch, Azure Monitor, Prometheus basics, Grafana basics, ELK/EFK fundamentals Version Control & Collaboration: Git/GitHub workflows (branching, pull requests), Agile/Scrum basics, Jira, Confluence Security & Networking: IAM/RBAC basics, Secret Management (Vault, AWS Secrets Manager), TCP/IP, DNS, Load Balancer basics, Firewall concepts

Soft Skills: Problem-solving, debugging pipelines, team collaboration, following standard DevOps practices PROFESSIONAL EXPERIENCE

Datadog, USA Jan 2025 - Current

DevOps Engineer

Lessened deployment failures 30% by automating CI/CD pipelines with GitHub Actions and GitLab CI/CD.

Amplified infrastructure scalability 45% by creating reusable Terraform and CloudFormation modules for AWS and Azure.

Accelerated release cycles 40% by containerizing applications with Docker and deploying to Kubernetes clusters using Helm charts.

Boosted system resilience by implementing multi-AZ failover strategies, ensuring 99.95% uptime for production workloads.

Lowered mean time to resolution (MTTR) 25% through centralized monitoring with CloudWatch, Prometheus, Grafana, and ELK.

Strengthened compliance by enforcing IAM policies, RBAC roles, and VPC segmentation, eliminating unauthorized access incidents.

Escalated development velocity by collaborating with engineering teams to integrate automated testing within pipelines, catching 20% more defects pre-production.

Freshworks, India Sep 2020 - Aug 2023

DevOps Engineer

Shortened deployment cycle time 35% by building Jenkins and GitLab CI/CD pipelines for multiple product teams.

Achieved 20% cost savings by optimizing multi-cloud infrastructure across AWS, Azure, and GCP.

Eliminated manual errors by standardizing infrastructure deployments with reusable Terraform and YAML templates.

Lowered troubleshooting time 35% by integrating centralized logging with ELK/EFK and Azure Monitor.

Heightened application reliability by implementing auto-scaling groups and load balancers for SaaS workloads.

Enhanced security posture by configuring firewall rules, DNS security, and secret management with Vault.

Refined developer productivity by introducing containerized dev/test environments with Docker, reducing environment setup time 40%.

EDUCATION

Master of Science in Information Technology Management Systems May 2025 St. Francis College, NY, USA

CERTIFICATIONS

Splunk Certified Cloud Metric User Dynatrace Associate Certification Microsoft Azure Fundamentals (AZ-900) The Ultimate DevOps Bootcamp (Kode Kloud) PROJECT

Complaint Redressal System Mar 2022 - Jan 2023

Automated deployments of Spring Boot and Angular application using Jenkins CI/CD pipelines and Docker containers, cutting release cycle time 35% and improving deployment reliability across environments.

Scaled application on Kubernetes with MySQL backend, implementing load balancing and backup automation, which increased system uptime to 99.9% and improved data availability.

Integrated centralized monitoring and logging with ELK stack, reducing mean time to resolution (MTTR) 25% and enabling faster root-cause analysis for production issues.

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