About
I’m a Data Science engineer focused on building data pipelines, database systems, and AI-powered applications. I’m particularly interested in what happens behind the scenes — how data is collected, transformed, stored, accessed, and eventually turned into something useful.
My primary focus is Data Engineering and Databases. I enjoy designing and building systems around PostgreSQL, Neon, Supabase, and MinIO, while using tools like Docker, GitHub, FastAPI, and LlamaIndex to build scalable and maintainable workflows. From ETL pipelines and data warehouses to vector databases and RAG systems, I like working on the infrastructure that makes modern applications reliable.
I’m not someone who likes learning technologies just for the sake of adding them to a stack. I learn by building real systems. I experiment with different architectures, connect tools together, deploy projects, run into problems, and keep digging until I understand what is actually happening underneath.
Hackathons have been a big part of that process. They’ve pushed me to take ideas from “this could work” to a working product under tight deadlines, while forcing me to make practical decisions about architecture, databases, APIs, deployment, and AI.
Beyond individual projects, I’m interested in the bigger picture of how data-intensive and intelligent systems are designed at scale — from reliable pipelines and well-structured databases to the AI systems that consume and learn from that data.
Right now, I’m focused on becoming a strong Data/AI Engineer who can design the foundation, build the pipeline, structure the data, and turn that foundation into intelligent products.
I don’t just work with data. I build the systems that make data useful.
I’m a Data Science engineer focused on building data pipelines, database systems, and AI-powered applications. I’m particularly interested in what happens behind the scenes — how data is collected, transformed, stored, accessed, and eventually turned into something useful.
My primary focus is Data Engineering and Databases. I enjoy designing and building systems around PostgreSQL, Neon, Supabase, and MinIO, while using tools like Docker, GitHub, FastAPI, and LlamaIndex to build scalable and maintainable workflows. From ETL pipelines and data warehouses to vector databases and RAG systems, I like working on the infrastructure that makes modern applications reliable.
I’m not someone who likes learning technologies just for the sake of adding them to a stack. I learn by building real systems. I experiment with different architectures, connect tools together, deploy projects, run into problems, and keep digging until I understand what is actually happening underneath.
Hackathons have been a big part of that process. They’ve pushed me to take ideas from “this could work” to a working product under tight deadlines, while forcing me to make practical decisions about architecture, databases, APIs, deployment, and AI.
Beyond individual projects, I’m interested in the bigger picture of how data-intensive and intelligent systems are designed at scale — from reliable pipelines and well-structured databases to the AI systems that consume and learn from that data.
Right now, I’m focused on becoming a strong Data/AI Engineer who can design the foundation, build the pipeline, structure the data, and turn that foundation into intelligent products.
I don’t just work with data. I build the systems that make data useful.
I’m a Data Science engineer focused on building data pipelines, database systems, and AI-powered applications. I’m particularly interested in what happens behind the scenes — how data is collected, transformed, stored, accessed, and eventually turned into something useful.
My primary focus is Data Engineering and Databases. I enjoy designing and building systems around PostgreSQL, Neon, Supabase, and MinIO, while using tools like Docker, GitHub, FastAPI, and LlamaIndex to build scalable and maintainable workflows. From ETL pipelines and data warehouses to vector databases and RAG systems, I like working on the infrastructure that makes modern applications reliable.
I’m not someone who likes learning technologies just for the sake of adding them to a stack. I learn by building real systems. I experiment with different architectures, connect tools together, deploy projects, run into problems, and keep digging until I understand what is actually happening underneath.
Hackathons have been a big part of that process. They’ve pushed me to take ideas from “this could work” to a working product under tight deadlines, while forcing me to make practical decisions about architecture, databases, APIs, deployment, and AI.
Beyond individual projects, I’m interested in the bigger picture of how data-intensive and intelligent systems are designed at scale — from reliable pipelines and well-structured databases to the AI systems that consume and learn from that data.
Right now, I’m focused on becoming a strong Data/AI Engineer who can design the foundation, build the pipeline, structure the data, and turn that foundation into intelligent products.
I don’t just work with data. I build the systems that make data useful.
I’m a Data Science engineer focused on building data pipelines, database systems, and AI-powered applications. I’m particularly interested in what happens behind the scenes — how data is collected, transformed, stored, accessed, and eventually turned into something useful.
My primary focus is Data Engineering and Databases. I enjoy designing and building systems around PostgreSQL, Neon, Supabase, and MinIO, while using tools like Docker, GitHub, FastAPI, and LlamaIndex to build scalable and maintainable workflows. From ETL pipelines and data warehouses to vector databases and RAG systems, I like working on the infrastructure that makes modern applications reliable.
I’m not someone who likes learning technologies just for the sake of adding them to a stack. I learn by building real systems. I experiment with different architectures, connect tools together, deploy projects, run into problems, and keep digging until I understand what is actually happening underneath.
Hackathons have been a big part of that process. They’ve pushed me to take ideas from “this could work” to a working product under tight deadlines, while forcing me to make practical decisions about architecture, databases, APIs, deployment, and AI.
Beyond individual projects, I’m interested in the bigger picture of how data-intensive and intelligent systems are designed at scale — from reliable pipelines and well-structured databases to the AI systems that consume and learn from that data.
Right now, I’m focused on becoming a strong Data/AI Engineer who can design the foundation, build the pipeline, structure the data, and turn that foundation into intelligent products.
I don’t just work with data. I build the systems that make data useful.
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I believe great engineering is not about adding unnecessary complexity, but about building systems that solve problems efficiently, scale reliably, and remain easy to understand.
Purpose Over complexity
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I enjoy understanding how different components interact within a system — from backend architecture and APIs to automation pipelines and user experience — to create solutions that feel complete and dependable.
System Thinking
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Every project I work on is an opportunity to experiment, learn faster, and refine my engineering mindset through practical implementation and continuous iteration.
Build. Learn. Improve.
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I focus on writing clean, maintainable, and scalable solutions that balance performance, usability, and long-term adaptability rather than short-term fixes.
Scalable Foundations
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My curiosity for AI systems, automation, cybersecurity, and modern technologies constantly drives me to explore ideas beyond the classroom.
Curiosity Driven