Deepan Narayanamoorthy

✓ Built multi-account, multi-region Terraform modules to deliver isolated, production-like AWS environments for safe releases and customer demos, with consistent networking (VPC, NLB, Security Groups) and service topology across stacks. ✓ Migrated the data plane across RDS (AWS DMS), Supabase, Typesense, Auth0 (tenant/app replication), Snowflake (S3 integration), and EFS (AWS DataSync), keeping demo and release paths aligned with the production environment. ✓ Owned production ECS/Fargate deployments, autoscaling, and orchestration at scale; led incident triage and cut release- related downtime by 2 hours via improved alerting and debugging workflows. ✓ Implemented Datadog synthetics and SLI-aligned monitors, alerts, and workflows to support on-call triage; reduced alert noise via routing/tuning; integrated Atlassian Statuspage with Datadog for customer-visible component status during incidents. ✓ Led an organization-wide CI/CD security modernization, replacing long-lived AWS access keys with GitHub OIDC and IAM role assumption across repositories, eliminating static cloud credentials. ✓ Standardized GitHub Actions CI/CD workflows with automated schema migrations and idempotent deployments, reducing recurring pipeline failures and improving deployment reliability. ✓ Designing, implementing, and managing scalable cloud infrastructure using Terraform (IaC) across AWS and Azure platforms. ✓ Building and maintaining automated CI/CD pipelines with GitHub Actions to enable seamless deployments. ✓ Containerizing and deploying production-grade applications with Docker, ensuring reliability, scalability, and high availability. ✓ Automating operational workflows, enhancing system performance, and driving incident response and root cause analysis for continuous improvement.

✓ Designed & implemented scalable cloud infrastructure using Azure ARM templates and AWS CloudFormation, establishing IaC best practices across multi-cloud environments. ✓ Provisioned AWS services (ECS, RDS, Lambda) and Azure resources (Functions, App Services, VMs) for 30+ microservices. ✓ Implemented UI delivery via CloudFront, S3, and Edge Lambda for multiple services for high-performance content distribution. ✓ Designed auto-scaling templates and strategies for AWS Fargate and Azure WebApps, optimizing performance, cutting incidents by 20%. ✓ Developed CI/CD pipelines in Azure DevOps, automating deployments and boosting deployment speed 3x. ✓ Led multiple migration initiatives within Azure and from Azure to AWS, cutting operating costs by $1800 annually. ✓ Designed and implemented a monitoring and logging solutions with CloudWatch and Azure Monitor, enhancing observability and reducing incident response time by 30 mins. ✓ Automated migrating standalone AWS LogGroups into Lambda stacks via pipelines and utilities supporting parallel deployment, trigger optimization, saving devs 1 hr per deployment per release. ✓ Managed daily optimization of AWS/Azure environments, ensuring high availability and performance for production workloads. ✓ Established automated deployment pipelines and configuration management processes, reducing manual configuration errors by 85%. ✓ Orchestrating and monitoring Prod deploymentfor multiple releases, decreased downtime by 3 hours every release. ✓ Provided downtime impact assistance on 10+ instances by implementing blue-green deployments and rollback strategies, enhancing system reliability. ✓ Developed tools for Key Vault alerts, IP compliance checks, Post-Prod sanity and incident reporting, enhancing security, compliance, and monitoring, resolving 50+ vulnerabilities, reduced manual effort by 10-15 hours per iteration.

✓ Developed a Python based QGIS plugin for the Oil and Gas team to forecast and generate potential bore wells for an acreage by leveraging multiple transformation algorithms. ✓ Built and remodelled a range of applications and scripts using Python, VBA and Selenium to accelerate the process of manually preparing pitching materials ✓ Saved around 1000 hours spent by the bankers per year across all projects. ‎

✓ Fine-Tuned the firm's wind engineering based application using R to boost the performance of pre-processing the data generated by wind masts and maintaining them in AWS S3 buckets. ✓ Implemented a stock market based application using R Shiny to mechanise analysis and forecasting of stock data using algorithms such as RSI and MFI. ✓ Spearheaded the Maintenance team that worked on optimising the legacy code base using roxygen2 across various projects utilising techniques like parallelization and vectorization. ‎

✓ Collaborated on a project intended to build an ERP system for the business utilising Python, SQL for back-end and PyQt5 for front-end. ✓ Designed a scalable SQL database schema for robust metadata management and retrieval. ✓ Functionalized the accounts, inventory and ledger management sections of the application. ‎

Relevant Coursework: Cloud Computing, Data Science, Data Structures And Algorithms, Database Management Systems, Operating Systems, Computer Networks