United States
Executive platform and analytics leader focused on building secure, compliant, and intelligent enterprise systems in regulated environments. I bring 10+ years of experience across cloud platforms, engineering operations, enterprise analytics, and business transformation, supported by an MBA in Finance and a growing specialization in Artificial Intelligence and Machine Learning. My work sits at the convergence of technology strategy, quantitative reasoning, platform scalability, and data-driven decision systems, with a focus on designing solutions that are operationally resilient, economically disciplined, and strategically useful. I have led cloud modernization, platform engineering, reliability, and analytics transformation initiatives that delivered 99.9–99.99% availability, improved delivery velocity by 35–50%, and reduced infrastructure spend by 20–30% through disciplined architecture, automation, and FinOps. At the same time, I have built analytical frameworks using Python, SQL, R, ETL, Power BI, and Tableau to improve forecasting, KPI visibility, and executive decision-making. My technical interests increasingly center on predictive modeling, optimization, uncertainty-aware analytics, intelligent decision support, and AI-enabled enterprise platforms. As a prospective D.Eng. in AI/ML candidate at GWU for Fall 2026, I am intentionally building a professional path that bridges engineering leadership, applied AI/ML, enterprise analytics, and financial strategy. Core strengths: AI/ML, Predictive Analytics, LLM, NLP, Data Science, Forecasting, Financial & Data Analytics, Python, R, SQL, Power BI, Tableau, AWS, Kubernetes (EKS), Terraform, CI/CD, SRE, DevOps, FinOps, Platform Reliability, Automation, KPI & Decision Support Models
I operate at the intersection of finance, artificial intelligence, and enterprise strategy, combining senior business analysis with VP-level AI strategy to transform complex data into decision-grade insight. I hold an MBA with a focus in Finance and am a Doctor of Engineering in Artificial Intelligence and Machine Learning applicant, focused on applied AI/ML, predictive intelligence, FP&A, risk analytics, forecasting, and optimization. Using Python, SQL, R, ETL, Excel, Power BI, and Tableau, I structure complex data, build analytical models, and create executive-ready reporting ecosystems. My work includes regression, forecasting, clustering, simulation, optimization, and ML-oriented analytics to uncover patterns, improve planning accuracy, and identify value-creation opportunities. My finance foundation includes NPV, IRR, WACC, CAPM, DuPont analysis, and scenario valuation to evaluate investments, stress-test assumptions, and guide resource allocation. I also design KPI dashboards and automated BI pipelines that strengthen executive visibility while reducing reporting workloads by approximately 30%. I translate technical findings into strategic recommendations for senior leaders, connecting data science methods with business outcomes across finance, operations, vendor management, and transformation, including $50,000 in annual savings through VAN cost restructuring. My long-term focus is building AI-enabled decision systems that are predictive, interpretable, financially rigorous, and operationally relevant. Core skills: AI/ML, predictive modeling, forecasting, regression analysis, optimization, decision intelligence, data analytics, quantitative finance, feature engineering, FP&A, risk analysis, strategy, Power BI, Tableau, SQL, Python, R, Excel, ETL, cloud transformation.
Led the design and operation of production-grade, cloud-native platforms enabling data-intensive and AI/ML workloads across distributed environments. Operated at the intersection of Site Reliability Engineering, Data Science enablement, and cloud architecture, with a focus on automation, observability, security, and cost efficiency. Architected and maintained Kubernetes (AWS EKS) platforms supporting analytics services and ML-adjacent workloads, implementing ingress routing (NGINX), node lifecycle management, and horizontal scaling. Improved platform availability to 99.95%+ and reduced MTTR by ~40% through SRE best practices, proactive monitoring, and automated remediation using Amazon CloudWatch and SNS. Built, scaled CI/CD pipelines (Jenkins, GitHub, AWS tooling) for application and infrastructure releases, including Blue/Green deployments, reducing deployment-related incidents by ~35% and enabling faster, safer releases. Standardized environments using Infrastructure as Code (Terraform, CloudFormation) and Ansible, cutting manual configuration effort by ~50%. Enabled data science and ML teams by delivering reliable, production-ready infrastructure and developing Python-based data analysis and feature preparation tools (Pandas, NumPy), supporting model experimentation, operational analytics aligned with MLOps principles. Led on-prem to cloud migration initiatives, modernizing legacy systems using AWS Migration Hub, CloudEndure, DMS, Direct Connect, VPN, improving scalability and reducing infrastructure overhead. Implemented security and compliance guardrails with AWS SCPs and AWS Config, supporting regulated environments. Applied FinOps practices, including instance right-sizing, Reserved Instances, and S3 lifecycle policies, achieving 20–30% cloud cost optimization without performance degradation. Partnered with engineering, data, product, and client stakeholders to deliver secure, scalable, AI-ready platforms supporting enterprise and regulated use cases.
Defined, enforced Git branching, tagging, and naming standards, reducing merge conflicts by ~40% and improving release velocity across multi-team environments. Designed and deployed secure web infrastructure using Apache and NGINX integrated with AWS ELB and SSL/TLS, enforcing HTTP→HTTPS and supporting 99.9%+ application availability. Hardened AWS network security by configuring Security Groups with least-privilege access, limiting exposure to approved subnets and IPs and reducing security findings by ~30%. Led resolution of complex Git merge and rebase conflicts, partnering with development leads to cut integration delays by ~25% and stabilize production releases. Standardized SCM and CI/CD practices, automating build and release workflows and reducing manual deployment effort by ~50%. Built and scaled Jenkins master/agent infrastructure, enabling parallel builds and reducing build times by ~35%. Collaborated with cross-vendor project managers and engineering teams. Designed repeatable release processes for new applications, improving deployment consistency and audit readiness in regulated environments. Analyzed cloud infrastructure performance and cost, delivering optimization recommendations that achieved 15–25% cost savings without reliability trade-offs. Implemented AWS VPC peering across multiple accounts to enable secure inter-account communication and service routing. Performed Linux production administration, including upgrades, patching, monitoring, and incident resolution with minimal downtime. Automated CI/CD pipelines using Ansible, Jenkins, Nexus, SonarQube, Maven, Terraform, and Git, improving deployment frequency and code quality. Authored Infrastructure as Code artifacts (Ansible playbooks, Terraform configs, Kubernetes manifests) to enable reproducible, scalable environments. Provisioned and managed AWS infrastructure at scale, including EKS, EC2, IAM, VPCs, and S3, supporting data-intensive and ML-adjacent workloads.
Engineered and operated cloud-native and hybrid database platforms across AWS RDS, Amazon Aurora, Azure SQL Database, and Oracle Cloud Infrastructure (OCI), supporting mission-critical workloads with 99.95–99.99% uptime. Proactively monitored and optimized RDBMS performance, improving average query latency by 30–45% through execution plan analysis, indexing strategies, and workload tuning. Designed and enforced database security models (users, roles, schemas, IAM-integrated access), supporting compliance and audit requirements common to FinTech and Big Pharma environments. Installed, configured, patched, and upgraded Oracle and SQL Server instances across on-prem and cloud platforms, reducing configuration drift by ~40%. Partnered with application and data engineering teams to plan and deploy schema changes across RDS, Aurora, Azure SQL, and OCI, cutting release-related incidents by ~25%. Implemented automated backup, restore, and recovery strategies using native cloud services, achieving >99% backup success rates and meeting defined RPO/RTO targets. Performed advanced query and concurrency optimization, reducing long-running queries by up to 50% and decreasing blocking and deadlock-related wait times by ~35%. Designed and maintained database objects (tables, views, stored procedures, functions, user-defined types) to support scalable data models and analytics use cases. Implemented and supported High Availability and Disaster Recovery (HA/DR) solutions, including RDS Multi-AZ, Aurora replicas, Azure SQL geo-replication, and OCI standby databases, improving resilience and recovery readiness. Monitored database growth, capacity, and performance trends, delivering actionable reports and optimization recommendations to engineering and leadership teams.
Situation Operated in regulated, data-driven environments requiring high financial accuracy, secure Linux systems, and high-performance database applications supporting reporting and operations. Task Improve financial reporting accuracy and timeliness, automate Linux operations, and deliver secure, optimized PL/SQL solutions that meet compliance and scalability requirements. Action Prepared and validated monthly, quarterly, and annual financial statements, strengthening controls and reconciliation workflows. Performed budgeting, forecasting, and variance analysis, partnering with stakeholders to interpret drivers and trends. Designed and maintained Linux-based applications; administered systems, monitored performance, and implemented security hardening. Automated deployments and recurring tasks using Bash and Python, standardizing environments and reducing manual effort. Designed, optimized, and maintained PL/SQL procedures, functions, and packages for financial and operational databases. Tuned SQL using execution plans and indexing; collaborated with DBAs and engineers to resolve performance bottlenecks. Used Git for version control and maintained audit-ready technical and financial documentation. Result Improved financial reporting accuracy by ~20–25% and reduced close-cycle rework, supporting audit readiness (FinTech/Pharma). Cut manual processing time by ~30–40% through automation of Linux operations and deployments. Increased report and query performance by ~35–45% via PL/SQL and SQL tuning, improving data availability for decision-making. Reduced incident resolution time by ~25% through proactive monitoring and standardized system configurations.