Associate Analyst · Quality Engineering · Deloitte
I work at the intersection of software testing, backend development and AI-assisted automation — from ETL/data validation on AWS to building small tools that make repetitive engineering work disappear.
The short version of what I do and why it matters.
One role, shipped like a pipeline — every stage passing.
What's in the toolbox, grouped the way I actually reach for it.
A mix of college builds, experiments and automation projects — some polished, some gloriously over-engineered.
Formal training, chronologically — including what's still in progress.
The stuff I end up obsessing over at 2 AM: AI, engineering, testing, backend systems, security and the weird ideas in between.
A rabbit hole that started with a stupidly simple thought: humans also generate answers from patterns, memory and context. So where exactly does the interesting distinction between human reasoning and generative AI begin?
MERN, Python, Java, Android, Web3, AWS, security, AI… the fun part is exploring. The hard part is deciding what deserves enough depth to become an actual skill instead of another GitHub repo.
Kiro, Copilot, LLMs and agentic workflows can make building software ridiculously fast. That makes the boring part more important: verification, boundaries, failure modes and knowing when the generated code is confidently wrong.
ETL testing stops being just “does the script run?†when data moves through Glue, S3 and Athena. You start asking whether the transformation preserved meaning — and that is a much more interesting problem.
// these are field notes from the rabbit holes I actually keep falling into.
Interested in SDET, backend, automation and AI-assisted engineering work. Currently building depth across testing, cloud data workflows and software development.