Charles OBrien

• Architect and develop full-stack applications using JavaScript, TypeScript, C# and Python using frameworks such as React, Next.js, Flask, Blazor, and Django, resulting in a 40% reduction in application load times and a 25% increase in user engagement across 5 major projects.
• Spearhead development initiatives as the Lead Engineer for multiple client projects, orchestrating end-to-end technical solutions and driving successful outcomes across diverse industry engagements.
• Design and implement cloud-native microservices architecture, ensuring scalability for distributed systems and utilize container orchestration technologies such as Docker and Kubernetes, achieving a 99.99% uptime for critical services and reducing infrastructure costs by 30%.
• Design, implement, and maintain CI/CD pipelines using Azure DevOps, GitHub, and GitLab, incorporating test-driven development (TDD),reducing deployment time from 2 hours to 15 minutes and increasing successful deployments by 40%.
• Implement and manage cloud infrastructure using Infrastructure as Code (IaC) tools like Terraform adhering to best DevOps practices, leading to a 45% reduction in manual configuration errors and 50% faster environment provisioning times.

• Developed and maintained RESTful APIs, increasing integration efficiency by 40% and reducing partner onboarding time from 2 weeks to 3 days for our core banking platform ecosystem.
• Led client integrations as the Lead Integration Engineer. Orchestrated complex data migrations, architected robust middleware solutions, and implemented scalable microservices to bridge traditional and modern financial ecosystems.
• Implemented automated testing strategies, including unit and integration tests, achieving 95% test coverage and reducing post-deployment issues by 60% for our cloud-native applications.
• Developed and implemented AWS Lambda functions triggered by S3 storage events, automating nightly GL processing report integration and reducing manual data entry by 85%, saving 20 hours per week.
• Designed and implemented real-time communication features using WebSockets for our financial dashboard application, decreasing data latency by 200ms and improving user satisfaction scores by 35%.

• Developed and maintained robust ETL pipelines using Python and SQL, seamlessly integrating data between cloud applications and on-premises systems, processing over 1TB of financial data daily
• Engineered efficient data integration solutions to connect legacy banking systems with modern cloud-based applications, reducing data transfer latency by 40%
• Implemented data quality checks and validation processes using Python libraries like pandas and numpy ensuring 99.9% accuracy in critical financial datasets
• Optimized SQL queries and database performance for on-premises data warehouses, resulting in a 55% improvement in report generation times for regulatory compliance

• Led development of interactive Power BI dashboards, resulting in a 30% increase in data-driven decision making across 5 departments
• Designed and implemented ETL processes using SQL and Python, reducing data preparation time by 40% and improving data accuracy by 25%
• Conducted advanced statistical analyses and predictive modeling, identifying key factors that led to a 15% increase in customer retention
• Collaborated with cross-functional teams to optimize data warehouse architecture, improving query performance by 50% and reducing storage costs by 20%
• Mentored junior analysts in data visualization best practices and SQL optimization, increasing team efficiency by 35% over 6 months

Designed and developed a comprehensive full-stack web application for enterprise data management and analytics. This project showcases proficiency in modern web development technologies, cloud deployment, security best practices, and user-centric design.

Key Features

• Multi-page form system with dynamic dropdowns and confirmation dialogs
• Data gallery with advanced sorting, filtering, searching, and export capabilities
• Customizable dashboard with dark mode and interactive data visualization
• Admin panel for field management, API documentation, and data versioning
• Robust security measures and comprehensive testing suite
• Bulk data import using Python Azure Serverless Function
• Deployment automation using Terraform

Tech Stack

• Frontend: Next.js, React, TypeScript
• Backend: Next.js, TypeScript, Python
• Database: PostgreSQL with Prisma ORM
• Deployment: Azure App Service with O365 Authentication
• CI/CD: GitHub Actions
• Security: Sonarcloud, ESLint, Dependabot, NPM Audit
• Automation: Terraform
• Testing: Playwright (E2E), Jest (Unit/Integration)

Spearheaded a major version control and CI/CD pipeline overhaul, modernizing the development process and enhancing security measures.

Key Features

• Successfully migrated non-version controlled local repositories to a new on-premises Azure DevOps instance, improving code management and collaboration.
• Designed and implemented new CI/CD pipelines incorporating advanced vulnerability package scanning and dependency management tools, including JFrog X-Ray.
• Integrated language-specific built-in audit and vulnerability scanners (NPM-Audit for JavaScript/TypeScript, dotnet list package --vulnerable for C#, safety check for Python) alongside Snyk for comprehensive multi-language scanning.
• Developed a custom solution to programmatically create work items in Azure DevOps from JFrog X-Ray Audit output, prioritizing issues by criticality for efficient resolution.

Tech Stack

• Frontend: C#, JavaScript, Blazor
• Backend: C#, .NET 
• Database: Microsoft SQL Server
• Deployment: Windows IIS Web Server
• CI/CD: Azure DevOps
• Security: NPM Audit, Dotnet List --Vulnerable, JFrog X-Ray
• Testing: XUnit, NUnit, Mocha

Developed a Python-based Web Dashboard application that automates the parsing of OSCAL-formatted XML files to create a comprehensive Business Intelligence analytics dashboard. This project was integrated into an existing FedRAMP web application, enhancing its capabilities with advanced data visualization and risk analysis features.

Key Features

• Automated parsing of OSCAL XML files for efficient data extraction.
• Interactive BI dashboard presenting aggregated risk and vulnerability data.
• Drill-down functionality for detailed analysis of risk levels and vulnerability statuses.
• CVE vulnerability tracking with aging and risk level assessment.
• FedRAMP guideline adherence recommendations for risk and vulnerability management.

Tech Stack

• Frontend: Python (Dash)
• Backend: Python (Flask)
• Data Processing: Custom Python scripts for OSCAL XML parsing
• Testing: pytest, pytest-cov
• Integration: Existing FedRAMP web application

Contributed to a complex GRC (Governance, Risk, and Compliance) application integration and image hardening solution, focusing on advanced security measures and data management:

Key Features

• Implemented secure network architecture using VPCs and Bastion Jumphosts to enhance system security.
• Performed container hardening to meet stringent standards using tools such as Anchore Grype, Trivy, Wiz, and Tenable Nessus.
• Designed and developed ETL pipelines to process vulnerability scanning reports from CI/CD pipelines, transforming raw data into a structured format for secure storage in an AWS data warehouse.
• Created a data model that condensed CI/CD reporting data into manageable tables for import into Regscale, facilitating efficient management and reporting.

The ETL process handled a wide range of vulnerability data, including:

• Basic vulnerability information (affected packages, CVE numbers, CVSS scores, severity ratings)
• Detailed vulnerability descriptions and attack vectors
• Temporal information and remediation details
• References to official advisories and related vulnerabilities
• Scanning metadata and assignee information
• Custom tagging and dependency analysis

Tech Stack:

• Languages: Python
• Database: Microsoft SQL Server
• Security: Nessus Tenable, Trivy and Grype
• Testing: Pytest, Postman

Developed a high-performance WebSocket client in C# as a critical component of a larger financial reconciliation system. This client was integral to processing and verifying Electronic Funds Transfer (EFT) transactions from numerous merchants.

Key Features

• Implemented a robust C# WebSocket client capable of handling hundreds of concurrent connections.
• Designed the client to efficiently listen for and process real-time event messages from merchant systems.
• Integrated the client into a larger tool that aggregated and reconciled EFT transactions throughout the day.
• Enabled end-of-day processing verification, ensuring accurate totaling and reconciliation of all transactions.

Tech Stack

• Languages: C#
• Back-End: C#, .NET Core
• Database: PostgreSQL
• CI/CD: Gitlab CI
• Testing: XUnit

Led and developed an automated ETL (Extract, Transform, Load) pipeline to seamlessly integrate legacy on-premises banking software with Concur's cloud-based expense management system. This project significantly streamlined the Accounts Payable process, eliminating hours of daily manual work.

Key Features

• Automated data extraction from legacy banking applications
• Data transformation to align with Concur's required format
• API integration for bidirectional data flow between legacy systems and Concur
• Reverse transformation of approved expense data for legacy system compatibility
• Fully automated file transfers and movement, eliminating manual intervention

Tech Stack

• Languages: Python, PowerShell
• Data Processing: Custom Python scripts
• Testing: Pytest, Postman
• Systems: Legacy on-premises banking software, Concur cloud instance