Backend reliability & data correctness

Working with production data

My backend work has included event-driven services, connected-device data, and the queries behind user-facing applications. These systems require attention to both the flow of data and the meaning of the records produced along the way.

I have built services, improved database query paths, and investigated production data defects. I find this work rewarding because it connects code behavior to a concrete question: can someone rely on the information the application is showing them?

My contribution

In reliability work, I traced a data-processing defect to its cause and shipped a forward fix. I separately built recovery tooling for regenerating derived records. These were different pieces of engineering: correcting new processing and providing a controlled way to address historical data.

The tooling included explicit execution modes, bounded processing, progress tracking, and duplicate checks. My contribution was the implementation of that capability; completion of a recovery run is a separate operational outcome that requires its own verification.

I have also improved MySQL query performance through composite indexing and schema tuning. That work required looking at actual access patterns and making targeted changes to how the application retrieved data.

What I take from it

I try to make operational intent visible in the tools I build. An engineer should be able to understand the scope of a run, inspect what happened, and decide whether it is appropriate to continue. Those properties matter when a tool is used under time pressure.

Production debugging has also shaped how I approach new systems. I pay attention to the distinction between incoming data and derived output, and to how a service will explain its own state when something goes wrong. Clear boundaries and useful diagnostics make later investigations more tractable.