About

I’m a Data Full-Stack Engineer with a background in business, and that combination deeply shapes how I think about technology.

I didn’t come to engineering just to build systems — I came to solve problems that matter.
My business foundation trained me to think in terms of value, cost, risk, and impact.
Engineering became the medium through which I turn those considerations into scalable, reliable systems.

I work across the full lifecycle of data and software systems, from ingestion and transformation to analytics, APIs, and production deployment. I care not only about whether a system works, but whether it makes sense to build and operate it in the real world.

My Engineering Mindset

• I approach engineering problems with a business-first perspective
• I optimize for impact and sustainability, not just technical elegance
• I believe most engineering challenges are trade-off problems, not tool problems
• I value clarity over cleverness, and simplicity over abstraction
• I design systems assuming they will fail, and plan for recovery early
• I use AI to amplify thinking, not to outsource responsibility

What I Do

🧠 Data Engineering

I design data pipelines by starting with the business questions they need to answer.
Schema design, data freshness, and transformations are driven by how data will be consumed — not just how it’s processed.

I focus on building pipelines that are:

• Observable and debuggable
• Cost-aware and scalable
• Reliable under real production constraints

🧩 Software Engineering

I treat code as a long-term communication tool, not just a delivery mechanism.
Readable, maintainable code that survives team changes matters more to me than clever abstractions.

My focus areas include:

• Clean and testable code
• Thoughtful system design
• APIs and internal tools that bridge data and applications
• Managing technical debt intentionally

☁️ Cloud & Distributed Systems

With a business background, I pay close attention to cost structures and operational complexity in cloud systems.

I design cloud-native architectures that scale economically, not just technically, and prefer pragmatic solutions over over-engineering.

🚀 DevOps & Production

I believe engineering isn’t done when code is merged — it’s done when systems run reliably in production.

I work on:

• Containerized workloads and automated deployments
• CI/CD pipelines for both data and application code
• Handling real-world failures: retries, backfills, incidents, and recovery

🤖 AI-Assisted Engineering

I use AI as a thinking partner and productivity amplifier.
While AI helps me explore ideas faster, I remain accountable for correctness, design decisions, and trade-offs.

I evaluate AI through a business lens: Does it reduce cost, speed up delivery, or improve decision quality?

If you find the content helpful, feel free to share it with others or provide feedback. You can also connect with me on LinkedIn or contribute to the GitHub repository.

Thank you for visiting, and I hope you enjoy the content!