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Marketing Engineering for complex data stacks

We build the data and tracking architecture your marketing runs reliably on.

From GA4, server-side GTM and Consent Mode to BigQuery, BI and activation: we audit, stabilise and automate marketing data stacks. Privacy-aware and auditable end-to-end.

Request an Audit Sprint Start the free website check

What you can expect

  • Your stack is the starting point
  • GA4, GTM, BigQuery & BI included
  • Consent and privacy considered
  • Read-only start available
  • Priorities, not tool dogma

Services

Our expertise.

Six services. Clear scopes. Concrete deliverables. No "let's have a look and see" proposals.

  • Datascale-led

    Measurement & Privacy Engineering

    Clean tracking, adblocker-resistant, GDPR-compliant, and with data quality GA4, Ads and Meta can actually rely on.

    • GA4
    • GTM (Web & Server-Side)
    • Piwik PRO
    • +2 tools
  • ✓ End-to-end implementation

    Data Reliability & Governance

    Automated monitoring, quality checks, and access controls. Data you can rely on. The prerequisite for any AI project: algorithms need clean, error-free training data.

    • Custom Python Monitoring
    • dbt Tests
    • Great Expectations
    • +1 tool
  • ✓ End-to-end implementation

    AI Strategy & Data Readiness

    From EU AI Act to custom LLMs: your data infrastructure for the AI era.

    • Vertex AI
    • Python/R
    • BigQuery ML
    • +1 tool

Full-Cycle services are delivered jointly with our engineering partner Saloid ↗

Compare scopes

Track Record

What we say. How we prove it.

Four recent engagements from enterprise setups. Anonymised until our clients clear logos and names. The metrics are real and verifiable on the first call. Every one of them produced measurable architecture upgrades that hold up for reporting and for the AI work that follows.

  • Electronics · DACH

    30% → 5%

    Direct Traffic by revenue corrected

    Problem
    Nearly 30% of all sessions landed as Direct Traffic. Ads and SEO attribution was effectively blind.
    What we did
    Introduced server-side tracking via stape.io, set up gclid restore, and cleaned the GTM setup.
    Result
    Direct share down from ~30% to 5%; paid and organic channels are correctly attributed again.
    • stape.io
    • GA4
    • gclid restore
    • GTM Server-Side

  • MedTech · Global

    120+ websites

    Data collection standardized globally

    Problem
    120+ sites across 3 regions, no shared DataLayer. Every report started with reverse-engineering.
    What we did
    Rolled out a common DataLayer standard plus GA4 360 architecture; built self-service dashboards for the brand teams.
    Result
    Consistent definitions for every core metric across APAC, EMEA, and AMER; brand teams work the dashboards on their own.
    • GA4 360
    • BigQuery
    • DataLayer
    • Data Studio

  • Hospitality

    +20% conversions

    More signal for paid media

    Problem
    Browser-measured conversions were increasingly lost to tracking protection and cookie restrictions.
    What we did
    Moved Meta CAPI, TikTok Events API, and Google Ads to server-side tracking via stape.io. End-to-end, with match-quality monitoring.
    Result
    +20% reliably measured conversions; bidding algorithms on paid channels got the full signal back.
    • Meta CAPI
    • TikTok Events API
    • Google Ads
    • stape.io

  • Retail · Dealer network

    Excel → automated

    Manual reporting fully replaced

    Problem
    Weekly multi-source reporting via Excel exports. Error-prone, unversioned, a full day of manual work each week.
    What we did
    Introduced Funnel.io as the central ingest layer; modelled every data source; dashboards generated straight from BigQuery.
    Result
    Reporting runs without manual steps; freed-up capacity now flows into analysis instead of data wrangling.
    • Funnel.io
    • BigQuery
    • Data Studio

Logos and client names will follow once approvals come through.

We work Data-Contract-first

The Data Contract Model.

A data contract works like a real contract: it defines what data comes in, what goes out, and who is accountable when something does not add up. We do not stack more tools on top of each other, we guarantee the numbers you decide on hold up.

INPUT
Website Events
page_view, click, purchase
Consent Signals
TCF v2.2, Consent Mode v2
Campaign Data
Ads, Meta, LinkedIn, TikTok
CRM Data
HubSpot, Salesforce, Pipedrive
CONTRACT LAYER
schema.checkout_purchase
v2.4.1 · owner: growth-eng
Naming A purchase is called "Purchase" everywhere.

Same event names across web, app and server. No three truths in three tools.

Validation Bad data never makes it in.

Events that do not match the contract are blocked before they distort reporting.

Ownership Every number has an owner.

Changes are documented and traceable, not announced in a chat thread.

Governance Changes do not break things silently.

You hear about tracking changes upfront, not after the revenue line disappears from the dashboard.

Quality Checks We catch problems before you do.

If event volume drops or conversion rate flips, the system raises a flag automatically.

412 / 416 checks passing 4 warnings caught automatically last build · 38s ago
OUTPUT
Dashboards
Looker · Metabase · Tableau
Numbers you can trust in the meeting.
Audiences
Ads, CRM, Lifecycle
Audiences that actually match.
Automations
Trigger, Alerts, Workflows
Less manual work, fewer errors.
Business Decisions
Forecast · Budget · Roadmap
Decisions on solid ground.

Still considering?

Want to check it yourself first?

Four free tools check your setup in minutes: Analytics Health Check, cookie scan, EU AI Act readiness, and more. No login needed.

Browse the tools

Public Stack · Live

Our own stack runs in public.

Our own website stack is intentionally lean: Plausible Community Edition, self-hosted in the EU, with a public dashboard at /open. It is a transparency example for cookie-free, verifiable analytics, not a default recipe for every client. For client setups, we build pragmatically around the use case: GA4, server-side GTM, Consent Mode, BigQuery, Ads, Meta CAPI, BI or Plausible.

Live dashboard
$ plausible.tail --site=datascale.de --limit=5
  1. 14:32:07 /en/ Chrome · DE
  2. 14:31:58 /en/services/ Safari · AT
  3. 14:31:41 /en/blog/ga4-audit-errors Firefox · CH
  4. 14:30:22 /en/open/ Chrome · DE
  5. 14:29:15 /en/resources/measurement-health-check/ Chrome · DE
Sample recording, the full live dashboard is public at /open/.

Architecture audit

Identify the blind spots in your data architecture.

We audit your tracking, consent, data quality and reporting. You leave with a clear assessment of which decisions your current data can safely support.

Request an Audit Sprint Book a 30-minute intro
  • 90-min deep dive
  • Concrete findings
  • Roadmap proposal