A*****n
About Candidate
Full-Stack Engineer with hands-on production experience building and deploying scalable web applications, backend microservices, and cloud-native systems. Proven ability to design clean, secure APIs, develop performant front-to-back solutions, and take products from concept to production. Experienced across .NET/C#, Python, RESTful services, databases, and cloud infrastructure, with a strong focus on reliability, maintainability, and real business impact. Comfortable owning features end-to-end and contributing effectively across the stack.
Nationality
Location
Education
Computer Systems Engineering with a strong focus on software systems, embedded development, and applied computing. Hands-on experience across Python, Java, MATLAB, embedded systems (Arduino), FPGA development (Vitis), system design, and low-level hardware–software integration. Built practical systems combining software, data processing, and hardware components.
Work & Experience
Worked on production enterprise systems supporting a Nasdaq-backed fintech platform managing $27T+ in assets. Designed and operated data warehouses, ETL/ELT pipelines, analytics services, and cloud-deployed applications. Contributed to performance optimization, metadata management, MIS repositories, and search analytics used by internal teams. Deployed containerized services on Azure, supported CI/CD pipelines, and owned features end-to-end in a production environment with 99.5% uptime SLAs.
Built and deployed end-to-end data and machine learning pipelines for IoT-driven energy optimization systems. Engineered batch and streaming ingestion, automated feature engineering, and data lake architectures. Deployed ML models via REST APIs, implemented monitoring and retraining workflows, and delivered real-time analytics dashboards. Solutions directly supported operational efficiency improvements and predictive maintenance use cases in production.
Developed a full analytics platform integrating data from CRM, marketing, and operational systems. Built ETL pipelines, data models, and automated BI dashboards to support business decision-making. Implemented data quality checks and reporting workflows that improved visibility into performance metrics, enabling cost reduction and lead growth through data-driven insights.
