Hire Data Engineer for Food Delivery
Senior, direct, no layers

A food delivery business usually has the data it needs, but not in one place. Order and delivery records, courier GPS and timing data, and restaurant prep-time data live in different systems, reports are rebuilt by hand each month, and decisions are made on numbers nobody fully trusts. When kitchens overloaded at peak 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 meals delivered from partner restaurants or dark kitchens as a data problem: what the business measures, where that data lives, and what decisions it should support. The night runs on minutes: orders accepted, kitchens fired, couriers dispatched, and a late delivery that costs the next order too. 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 reports that matter are the ones the team already rebuilds by hand: on-time delivery rates by area and hour, kitchen prep-time reliability, courier utilisation and wait times, repeat-order rates, and refund causes and costs. Each is rebuilt once from modelled data and then refreshed on a schedule, so the weekly meeting starts from numbers everyone trusts instead of a debate about whose spreadsheet is current.

Most food delivery businesses already run on ordering platform (menus and orders), dispatch software (couriers and ETAs), restaurant portals (partner menus and prep times), payment systems (orders and refunds), and support software (order issues). The pipeline work starts from those systems as sources, not by replacing them, and lands the cleaned data in a warehouse the business owns, with dashboards on top for the team.

The first build is usually the report the team rebuilds by hand most often, often kitchen prep-time reliability. When that report arrives on schedule, correct, and without manual work, the case for pipelining the remaining sources makes itself.

Take courier GPS and timing data. It is the kind of source every food delivery business has and nobody quite trusts. Once it flows through a tested pipeline with error logging, kitchen prep-time reliability stops depending on whoever happened to build last month's spreadsheet.

ordering platform handles menus and orders. dispatch software handles couriers and ETAs. restaurant portals handles partner menus and prep times. payment systems handles orders and refunds. support software handles order issues.

The realistic outcome is a weekly meeting run on trusted numbers: on-time delivery rates by area and hour and kitchen prep-time reliability arrive on schedule, definitions are no longer debated, and kitchens overloaded at peak becomes visible early enough to act on. That is the standard this engagement is measured against, not a dashboard launch.

Over time the warehouse becomes an asset of its own: every new question starts from modelled data instead of a fresh export, so analysis gets faster the longer the system runs. Browse all hire engagements or discuss your data setup directly.