Building enterprise-grade distributed systems Ahmed E. Soliman
Software Engineer / Architect — FinTech & Data Platform Engineering
Years of Experience
Projects Completed
LinkedIn Followers
GitHub Commits
About Me
Experienced Software Engineer and Architect in financial technology with 9+ years building and operating enterprise-grade distributed data platforms. Deep expertise in Apache Spark job orchestration, Kubernetes-native deployments across multi-cluster environments, and end-to-end CI/CD pipeline automation at BNY (Bank of New York Mellon).
Proven track record designing real-time and batch data ingestion systems processing high-volume financial datasets. Skilled in containerized microservice architecture using Docker, Helm, and cloud-native tooling, with strong proficiency in Elasticsearch and data lake technologies (Iceberg, Nessie). Passionate about AI/ML innovation, currently building AI-powered agents and exploring LLM-driven workflow automation.
What I Do
Platform Engineering & Data Processing
Designing and implementing core components of global data platforms including Spark processing engines, analytical loaders, event-driven services, and data ingestion frameworks.
Frontend Development
Creating responsive, interactive user interfaces with React, TypeScript, and modern CSS frameworks for seamless user experiences.
DevOps & CI/CD Automation
Building unified CI/CD pipelines with GitLab, automating Docker image workflows, managing Helm chart releases, and configuring Kubernetes RBAC across multi-cluster environments.
Data Engineering & Architecture
Operating distributed Spark workloads, building Elasticsearch indices, managing Apache Iceberg tables and Nessie catalogs, and designing data lake infrastructure.
AI/ML & Innovation
Building AI agent integrations using enterprise AI platform SDKs, creating JWT-authenticated agents, and exploring LLM-driven workflow automation for intelligent pipeline monitoring.
Security & Vulnerability Management
Implementing security best practices, ensuring compliance with industry standards, and mitigating vulnerabilities in cloud-native environments.
My Resume
Education
Master's Degree — M.S. Computer Science
Arizona State UniversityAdvanced graduate studies in Computer Science.
Bachelor's Degree — B.S. Computer Science
City University of New York (CUNY)Relevant coursework: Advanced Programming Techniques (Honors), Data Structures (Honors), Cryptography and Crypto Analysis, Discrete Structures and Applications to CS, Analytic Geometry and Calculus I & II.
Experience
Software Engineer — Full Stack Engineer
BNY (Bank of New York Mellon)Designing and implementing core components of a Global Data Platform including Spark processing engine, Vertica analytical loader, and event-driven services. Operating distributed Spark workloads across multi-cluster Kubernetes environments. Building unified CI/CD pipelines, automating Docker workflows, and developing AI agent integrations.
Software Engineer
Previous RoleDeveloped full-stack web applications and RESTful APIs with modern frontend frameworks. Built backend services handling authentication, data persistence, and third-party API integrations. Designed production websites focused on performance, accessibility, and SEO.
Certifications
React Native + Hooks Course
2022IBM Blockchain Essentials V2
2021Cybersecurity Essentials (Cisco)
2021Networking Essentials (Cisco)
2021Data Science Foundations (IBM)
2021Advanced Google Analytics
2019Digital Sales Certification
2019Google Analytics
2019My Projects
Distributed Task Queue
High-throughput distributed task processing system built with Python, Redis, and RabbitMQ handling 100K+ messages/sec.
Real-Time Analytics Dashboard
Interactive analytics platform with React, D3.js, and WebSocket streaming. Node.js backend with PostgreSQL and ClickHouse.
K8s Deployment Platform
Internal developer platform for Kubernetes deployments with GitOps workflows, Terraform IaC, and automated rollback mechanisms.
Dev CLI Toolkit
Open-source command-line toolkit for developers. Code scaffolding, project templates, and automated workflow management. 2K+ GitHub stars.
ML Data Pipeline
End-to-end machine learning pipeline with Apache Spark, Airflow, and MLflow. Processes 10TB+ of data for model training and inference.
