Shipped in
production.

WHAT I BUILT

• ▸The index schema and its semantic configuration. The rule I applied: searchable only where the text is genuinely searched, filterable where something scopes a query, facetable only where the UI offers a breakdown. Every capability costs index size, so you don't switch them all on.
• ▸The pipeline that pulls comments from the analytics store, enriches each one with its club and question context, deduplicates them, and pushes them into the index.
• ▸Batched uploads for the historical backfill: around a thousand documents per batch, three batches running concurrently. Enough to move a lot of data, not so much that the failures become hard to reason about.
• ▸Retry with exponential backoff, and rollback when it keeps failing: the partial documents are deleted from the index and the comment's indexed-at marker is left unset, so a later run picks it up again. A response is never left half-indexed.
• ▸A scheduled function keeping new comments searchable, plus the subscription and trial-quota layer and the APIs behind it.

WHAT I BUILT

• ▸Three extractors, one per booking system. Before writing any mapping code I hit each vendor's endpoint and read the raw payload. They didn't even agree on the field name for nine- versus eighteen-hole rounds.
• ▸One staging table they all normalise into: club, provider, hole count, played-at, and a processed marker. The vendors differ at the edge; past that table nothing downstream knows or cares which system a round came from.
• ▸Each club processed inside its own try/catch, with any error recorded as data and retried on the next run. In a multi-tenant batch job, an exception is data, not a stop signal. One club's expired credential shouldn't end the day for the other two hundred.
• ▸The transformation: group the staged rounds by club and by month, split nine- from eighteen-hole onto separate metrics, create the yearly container record if the year has rolled over, then either create the fact or add to the value already there.
• ▸A structured run log recording every outcome, including "there was nothing to process". Running unattended, doing nothing silently had to be a state someone could actually see.

WHAT I BUILT

• ▸Made clubs geographically searchable in the first place. The account record held an address but no coordinates, so I added a GeoJSON location and used MongoDB's geospatial query to find clubs within a radius, so the database does the distance work instead of fetching everything and filtering in code.
• ▸The domain model: selection mode as an explicit enum (radius or hand-picked list), plus facility-type filters and min/max limits on both the generated list and what a respondent may answer.
• ▸A sync service keeping each club's comparison list correct across four different triggers, adding clubs as they come into range and soft-deleting those that leave.
• ▸The configuration API, and the question editor in the internal admin platform.
• ▸The answer transformation: each response lands as fact rows, and the dashboard groups by club and sums at query time. Nothing stores a per-club total; the distribution chart is computed when someone opens the report.

WHAT I BUILT

• ▸The member churn-risk table with risk categories and contact-member workflows, KPI cards, and the revenue-at-risk and risk-distribution views.
• ▸Every number an insight refers to: churn-rate deltas, comparisons, whatever the story needs. That comes from the backend already computed. My side is the frontend: display it, and write the base prompt that hands those finished numbers to the model, so the model never does arithmetic, only phrasing.
• ▸The account ID taken from an httpOnly cookie and re-validated server-side on every request, never read from the request body.
• ▸The insight card streamed separately, so waiting on the model doesn't block the KPIs and tables from rendering.
• ▸My first production work in Next.js: App Router, React 19, TypeScript and TanStack Query/Table on top of the existing .NET backend.

WHAT I BUILT

• ▸Propagation of survey changes from a parent account down to its children.
• ▸A three-way diff (the parent before, the parent after, and the child's current state), the same shape as a git merge. Comparing only the two current versions can't tell "never diverged" from "deliberately customised"; bringing the common ancestor in can.
• ▸Children that have diverged are excluded from the push by default and surfaced to an admin instead, with force-overwrite as a separate, deliberate action.