Data Engineering for Hospitality

We model dimensionally because analysts have to be able to answer a question without asking an engineer first. That is the whole point of a warehouse.

In hospitality, demand swings hard by season and guests expect an instant multilingual response. That single fact reshapes how data engineering has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is housekeeping scheduling, usually integrated against POS. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.