Hire Data Engineer for Media Agencies
Senior, direct, no layers

A media agencies business usually has the data it needs, but not in one place. Buying-platform placement data, audience and reach data, and campaign performance feeds live in different systems, reports are rebuilt by hand each month, and decisions are made on numbers nobody fully trusts. When plans built on last year's audiences 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 media planning, buying, and campaign measurement as a data problem: what the business measures, where that data lives, and what decisions it should support. The agency runs on plans and placements: audiences defined, inventory bought, campaigns flighted, and results that must be reported before the client asks. 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: reach and frequency by audience, cost per outcome by channel, pacing against flight budgets, client retention by service, and margin per campaign. 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.

The existing stack here usually means media planning tools (plans and audiences), buying platforms (placements), analytics (campaign results), CRM (clients and pipeline), and reporting tools (client dashboards). 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.

The first build is usually the report the team rebuilds by hand most often, often cost per outcome by channel. When that report arrives on schedule, correct, and without manual work, the case for pipelining the remaining sources makes itself.

Take audience and reach data. It is the kind of source every media agencies business has and nobody quite trusts. Once it flows through a tested pipeline with error logging, cost per outcome by channel stops depending on whoever happened to build last month's spreadsheet.

media planning tools handles plans and audiences. buying platforms handles placements. analytics handles campaign results. CRM handles clients and pipeline. reporting tools handles client dashboards.

What changes in practice is where numbers come from. When buying-platform placement data and audience and reach 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.

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.