Data engineering layer

From raw data to ready models

Stop losing time to hundreds of lines of SQL, tangled schemas and pipelines that break. Get data models designed, cleaned and enriched for marketing and SEO analytics.

Each model starts with a question you already have — which kind of page is winning, which queries are new, where you compete with yourself. The answer is built into your own warehouse as classifications, enrichments and views, and shows up on every report.

The data is the same. The shape is the work.

Search Console gives you one row per URL and nothing that says what kind of URL it is. Every question you actually want to ask — by section, by page type, by intent — starts by rebuilding that context in SQL, every time, in every report.

Same four URLs, before and after Sample data

As it arrives

searchdata_url_impression
url clicks impr.
  • /blog/how-to-choose-a-crm 2,410 68,900
  • /pricing 1,860 24,300
  • /products/crm-starter?utm_source=nl 940 17,500
  • /blog/category/sales 620 31,200

No page type. No group. No intent. To answer “how is the blog doing?” you write the classification yourself — and rewrite it in the next report, and again when the URL scheme changes.

url page_type content_group
  • /blog/how-to-choose-a-crm Blog Informational
  • /pricing Detail Commercial
  • /products/crm-starter?utm_source=nl Detail Commercial
  • /blog/category/sales Listing Informational

Same rows, now carrying the columns the question needs. Group by page_type instead of inferring it — and the URL scheme changing becomes a rule we update, not a report you rebuild.

Why these models and not your own SQL

You could build all of this. The question is whether you want to keep it running for the next three years.

  • One definition per metric

    Standardised once, not per report

    The same word means different things in different platforms — a session, a click, a conversion. Models reconcile them once, so a number carries the same definition no matter which report it lands in.

    See the semantic layer
  • Enrichment, not guesswork

    Rules you approve, run in your warehouse

    Raw URLs become page types, messy query strings become intents. The rules are yours to approve and change — and where a rule can’t carry it, an LLM classifies inside your own warehouse rather than a black box elsewhere.

  • No maintenance on your side

    Rebuilt daily, before anyone looks

    API quotas, timeouts and schema changes are ours to absorb. The models are rebuilt every day, so the tables are current and complete before anyone opens a report.

Built for SEO and content analytics

Pick the model that matches the question you are trying to answer. Each one ships as tables in your own warehouse, not as a report locked inside someone else’s tool.

Working on a question you don’t see here? Tell us what you are trying to learn.

Leave the data engineering to us and get back to strategy.

No custom models to build, no pipelines to babysit. Start with the ones that already exist — and if your question needs a model nobody has built yet, we build that too.

Talk to us

Free discovery call · No commitment — leave with a starting point