Hire Data Engineer for Hotels
Senior, direct, no layers

A hotels business usually has the data it needs, but not in one place. Property management reservations, channel-manager rate and availability data, and booking-engine sessions live in different systems, reports are rebuilt by hand each month, and decisions are made on numbers nobody fully trusts. When direct bookings losing to booking sites keeps happening, the cause is rarely a lack of data; it is that the data never gets collected, cleaned, and presented in a form anyone can rely on.

The engagement covers rooms, suites, dining, events, and experiences as a data problem: what the business measures, where that data lives, and what decisions it should support. The hotel runs on occupancy and rate: rooms sold across channels, housekeeping turned between check-out and check-in, and guests whose stay is decided by the first ten minutes at the desk. Reporting built on that reality gets used in weekly meetings; reporting built on generic metrics gets ignored.

The work is done inside the business's own systems, with definitions agreed before any dashboard is built. A metric means the same thing in every report, because the model defines it once. That discipline is the difference between reporting the team trusts and reporting the team argues with.

The warehouse draws on property management reservations, channel-manager rate and availability data, booking-engine sessions, housekeeping status logs, and guest-review data. Each source is pulled on a schedule, checked for duplicates and missing records, and modelled into tables the business can query, so occupancy and average daily rate by channel, direct-booking share, and housekeeping turnaround times all read from the same definitions instead of separate extracts.

The existing stack here usually means property management system (reservations and rooms), channel manager (rates across booking sites), booking engine (direct bookings), housekeeping apps (room status), and review platforms (guest reputation). Each of those systems holds part of the picture. The build connects them into one modelled layer, so a report draws from the warehouse rather than from five separate exports.

A sensible first project is one pipeline done properly, often property management reservations feeding occupancy and average daily rate by channel, with the team using the resulting report for real decisions before anything else is added. A pipeline that proves itself on one decision earns the trust to carry the rest.

Take property management reservations. Today it probably arrives as an export someone cleans by hand. In the pipeline it is pulled on a schedule, checked for missing or duplicate records, and modelled into the same definitions every report uses, so occupancy and average daily rate by channel is a number the team trusts instead of a spreadsheet they debate.

property management system handles reservations and rooms. channel manager handles rates across booking sites. booking engine handles direct bookings. housekeeping apps handles room status. review platforms handles guest reputation.

What changes in practice is where numbers come from. When property management reservations and channel-manager rate and availability data flow through tested pipelines into one modelled layer, the team stops rebuilding reports and starts acting on them. The time saved is real, but the trust is the actual deliverable.

That also changes how the business argues: disagreements move from whose spreadsheet is right to what the number means and what to do about it, which is where a hotels team's judgement actually adds value. Browse all hire engagements or discuss your data setup directly.