I build software that ships, not slideware.

The client's old tool generated invoices as a synchronous, blocking process — large batches would time out and silently fail. I redesigned the schema around tenants and moved generation to an async queue with retry logic and per-tenant billing rules.

Invoice generation dropped from an average of 40 seconds to under 15, and batch failures went from a weekly occurrence to zero in the first three months post-launch.

Inspectors work in basements, rural sites, and dead zones. The app needed to feel identical online or offline. I built a local-first SQLite store with a custom conflict-resolution layer that merges field edits by timestamp and section ownership.

Zero data loss reported across 12,000+ monthly inspections, even with inspectors going hours without signal.

The analytics team spent a full day each week stitching together exports from six separate tools. I built dbt models on top of a central warehouse and scheduled Airflow jobs to keep everything fresh automatically.

Manual reporting dropped to near zero — the team now spends that time on analysis instead of data wrangling.

Tickets were triaged manually by a rotating on-call engineer, which meant slow first response and inconsistent routing. I built a pipeline that embeds each incoming ticket, retrieves similar historical tickets and their resolutions via vector search, and uses an LLM to classify intent, priority, and the right team — with a human-in-the-loop review step for low-confidence cases.

First-pass routing stayed accurate enough that most tickets never needed manual re-routing, cutting the on-call engineer's triage load by roughly a third, with the confidence-scoring step keeping the model's mistakes visible and correctable instead of silent.

Every device event was written straight to the primary database, so traffic spikes turned into lock contention and silently dropped readings. I moved ingestion behind a partitioned event log with consumer groups that batch writes into a time-series store, and made backpressure explicit — a slow consumer degrades throughput instead of losing data.

Peak bursts are absorbed without dropping events, and because consumers replay from an offset, recovering from a bad deploy became a rewind rather than a database restore.

Releases were a manual, single-region process — someone SSH'd into boxes and ran scripts by hand. I rebuilt the pipeline around Terraform-managed infrastructure and GitHub Actions, with blue-green deploys across regions and automatic rollback on failed health checks.

Deploys went from a nerve-wracking manual process to a routine automated one — a bad deploy triggers an automatic rollback instead of a middle-of-the-night page.