About
Highly accomplished Cloud Engineer with 5 years of experience specializing in designing and implementing scalable, cloud-native data pipelines across Azure, AWS, and GCP. Proven expertise in optimizing infrastructure costs, enhancing data quality, and automating CI/CD workflows, consistently delivering high-impact, data-driven solutions through strong cross-functional collaboration.
Skills
Cloud Platforms
AWS (S3, EC2, Redshift, Glue, Lambda), Azure (Data Factory, Synapse, Databricks, Monitor), GCP (Big Query, Dataflow, Pub/Sub, Cloud Composer), Azure Monitor, CloudWatch.
Data Engineering/ETL
Apache Spark, Databricks, Hadoop, NiFi, Airflow, Informatica, Talend, SSIS, Snowflake, Big Query, Redshift, SQL Server, Oracle, MongoDB, Teradata, Presto, ETL Pipeline Development, Data Validation, Data Pipeline Automation.
DevOps/Automation
Terraform, Jenkins, Kubernetes, Docker, Git, PyTest, CI/CD, DataOps, CloudFormation, Autosys, Ansible.
BI & Analytics
Power BI, Tableau, ELK Stack, Apache Superset, DBeaver, Toad, Google Data Studio.
Security & Governance
IAM Policies, Audit Logs, Secret Manager, HIPAA Compliance.
Programming
Python, SQL, Bash, PySpark, R.
Machine Learning
ML Pipelines, Azure Machine Learning, MLflow.
Soft Skills
Collaboration, Communication, Time Management, Critical Thinking, Problem Solving.
Work
Seattle, Washington, US
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Highlights
• Led development and management of Azure VMs, AWS EC2 instances, and storage accounts, successfully migrating on-premise servers to cloud platforms. This initiative reduced manual provisioning efforts by approximately 40% while maintaining high availability via Azure availability sets and AWS Auto Scaling groups.
• Configured and optimized Azure Web Apps, App Services, Application Insights, alongside AWS Elastic Beanstalk and CloudWatch. Automated disaster recovery processes using Azure Site Recovery and AWS Backup, leveraging Azure Automation and AWS Lambda, which decreased manual oversight by 35%.
• Managed the overall Azure and AWS infrastructure portfolio, including Azure web roles, EC2 instances, RDS, Azure SQL databases, storage solutions, Azure Active Directory, and IAM roles. These efforts enhanced resource utilization and reduced manual access management tasks by 25%.
• Led migration of multiple data centers to cloud environments using AWS DMS and Azure migration tools, which cut manual data transfer and configuration labor by half. Employed Log Analytics and CloudWatch for real-time monitoring and quick resolution of issues.
• Developed and maintained Infrastructure as Code (IaC) using Terraform, automating cloud deployments with PowerShell, AWS CLI, and Terraform scripts. This approach shortened deployment times by 60% and ensured consistent environment setups.
• Implemented Nagios monitoring tools integrated with cloud platforms, reducing manual troubleshooting efforts by 35%. Coordinated issue tracking via JIRA and Confluence, improving incident management efficiency by 20%.
• Deployed and managed Kubernetes clusters on Azure (AKS) and AWS (EKS) using Helm and Terraform charts, ensuring consistent builds and cutting manual configuration errors by 50%.
• Applied DevSecOps best practices with SonarQube, Trivy, and AWS Security Hub, automating static code analysis and vulnerability scanning. These measures reduced manual security reviews by 45% and strengthened compliance with cloud security policies
Huntersville, North Carolina, US
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Summary
Automated CI/CD delivery pipelines and managed cloud migrations on Azure and AWS, transitioning systems from on-premises to cloud environments.
Highlights
Automated CI/CD delivery pipelines using Jenkins, Docker, and Kubernetes to streamline deployments and enable microservices architecture.
Managed comprehensive cloud migrations on Azure and AWS, successfully transitioning from on-premises to resilient cloud environments.
Administered web applications and services on Azure and AWS, implementing monitoring with Application Insights and CloudWatch to enhance service reliability.
Developed containerized deployments with Docker, increasing deployment efficiency by 20% and optimizing resource utilization.
Provisioned and managed infrastructure as code with Terraform across AWS, Azure, and GCP, ensuring resource consistency and security.
Bengaluru, Karnataka, India
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Summary
Automated system patching and managed incident processes, improving reliability and service quality through DevOps practices.
Highlights
Automated monthly system patching with Jenkins, reducing manual effort by 40% and significantly improving system reliability.
Developed custom Jenkins pipelines for patch scheduling, testing, and validation, ensuring zero downtime during critical deployments.
Integrated Git version control with Ansible automation, decreasing configuration errors by 15% across various systems.
Managed incident and change management processes, improving response time by 20% and enhancing overall service quality.
Monitored patch deployment status using Jenkins, delivering real-time reporting and compliance documentation for audits.
Education
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Masters of Science
Data Science
Grade: GPA: 3.9
Courses
Completed advanced coursework in big data analytics (Spark, data mining, cloud computing, statistical programming (Python, R), data visualization (Tableau, Power BI)).
Built and deployed multiple data science pipelines integrating data engineering, predictive modeling, and business-focused visualization.