I build systems around data, infrastructure, backend engineering, and AI tooling.

Currently, I work as a Data Engineer - Financial Data Products (Alternative Finance) at BuySellTips Financial Research, where I work on financial data products, API/data workflows, infrastructure planning, and analytics-focused systems.

Outside work, I like building things closer to the metal: KV stores, distributed storage ideas, lightweight agent harnesses, backend services, cloud pipelines, and tools that make engineering workflows faster.

My key technologies are Golang for backend/distributed systems and Python for data engineering, analytics, automation, and AI workflows.

• Building Golang backend systems and distributed storage experiments
• Working with financial data products, alternative finance datasets, and analytics pipelines
• Using Python for data engineering, automation, ML, and AI workflows
• Designing cloud-native deployment pipelines and production-ready release flows
• Exploring KV-cache databases, storage internals, and Cassandra-inspired architectures
• Building lightweight agentic systems and AI workflow harnesses

• Sider: a KV-cache database inspired by Cassandra-style distributed storage concepts.
• Lite Open Claw: a lightweight agentic harness for experimenting with AI agent workflows.
• Financial data product work involving APIs, data workflows, internal architecture, and AI/AWS infra cost estimation.
• Cloud and release engineering work across builds, deployment pipelines, production checks, and reliability fixes.

• Primary Languages: Go, Python
• Backend: REST APIs, Go services, Node.js, Express
• Data Engineering: Python, financial data workflows, analytics pipelines, Pandas, Jupyter
• AI/ML: Scikit-learn, TensorFlow, PyTorch, agent workflows
• Cloud & DevOps: AWS, Docker, Kubernetes, CI/CD, GitHub Actions, Linux
• Databases: KV stores, PostgreSQL, MySQL, Firebase Firestore
• Frontend When Needed: Next.js, React, Svelte, TypeScript

I’m open to collaborating on:

• Go-based backend systems
• Python data engineering projects
• Financial data products and alternative finance analytics
• Distributed systems, KV stores, and storage engines
• Cloud pipelines, DevOps automation, and deployment workflows
• Lightweight AI agent frameworks and agentic automation tools
• Production AI systems and model deployment workflows

Building data systems, backend infrastructure, and AI tooling for messy real-world problems.