Hire Data Engineer for Construction
Senior, direct, no layers

A construction business usually has the data it needs, but not in one place. Project management records, estimating and variation data, and site diary entries live in different systems, reports are rebuilt by hand each month, and decisions are made on numbers nobody fully trusts. When variations agreed verbally and never billed 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 residential builds, extensions, renovations, and commercial fit-outs as a data problem: what the business measures, where that data lives, and what decisions it should support. A build is a chain of trades and inspections: groundworks before structure, structure before envelope, inspections at each stage, and a delay in one trade pushing every trade after it. 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: programme slippage by project and trade, variation value against original quote, inspection pass rates, subcontractor reliability and cost, and margin by project type. 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.

Day to day, the business already runs on project management software (programmes and tasks), estimating software (quotes and variations), site diary apps (daily progress and photos), accounting software (subcontractor payments and costs), and health and safety apps (method statements and permits). Pipelines pull from those sources on a schedule, models define the metrics once, and the reporting layer serves every team from the same definitions.

The first build is usually the report the team rebuilds by hand most often, often variation value against original quote. When that report arrives on schedule, correct, and without manual work, the case for pipelining the remaining sources makes itself.

Take estimating and variation data. It is the kind of source every construction business has and nobody quite trusts. Once it flows through a tested pipeline with error logging, variation value against original quote stops depending on whoever happened to build last month's spreadsheet.

project management software handles programmes and tasks. estimating software handles quotes and variations. site diary apps handles daily progress and photos. accounting software handles subcontractor payments and costs. health and safety apps handles method statements and permits.

The measure that matters is use months later. Pipelines still running on schedule, reports still opened weekly, and new sources added without rebuilding the warehouse is the deliverable. Anything that needs its builder to survive was not finished.

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.