Skip to main content
02 Service

Saloid's Data Engineering & Analytics practice builds server-side tracking, consent implementations (OneTrust, Usercentrics), ETL/ELT pipelines, and data quality frameworks that are GDPR-native and audit-ready.

Data you can trust. Pipelines that don't break.

Fix your data pipeline

The problem

Data is everywhere and trusted nowhere. Tracking leaks to third parties. Pipelines break on Mondays. Analysts don't trust the warehouse. Nobody knows which numbers are right.

Symptoms

  • Dashboards show different numbers depending on who pulls them and when
  • Consent management is a checkbox exercise, not a real implementation
  • Pipeline failures discovered by end users, not by automated monitors
  • Third-party scripts making calls to domains you never approved
  • Data team spends 80% of their time cleaning, 20% thinking

Our approach

01

Audit

Full tracking and pipeline audit. Every tag, consent flow, event, and data path documented. We find what's broken before we touch anything.

02

Architecture design

Server-side tracking, consent layer (OneTrust/Usercentrics), clean tagging architecture. Privacy-first from the ground up.

03

Pipeline build

ETL/ELT with data quality checks at every stage. dbt tests, freshness monitors, Great Expectations assertions. When something breaks, you know in minutes.

04

Quality & governance

Automated quality gates, documentation, and handover. Your team can maintain and extend the pipeline without us.

Outcomes

70%

Faster time-to-insight

Zero

Cookie banners needed

4–8 wk

Typical pipeline fix

Audit-grade

Data quality

Tech stack

GA4 / Server-side GTMOneTrustUsercentricsFunnel.ioSparkAWS GlueSnowflakedbtFivetranGreat ExpectationsAirflow

Frequently asked

What is server-side tracking and why is it better for GDPR? +
Server-side tracking runs analytics on your own server instead of Google's JavaScript in visitors' browsers. We set up GA4 server-side on EU infrastructure — data stays on EU soil, no cookie consent needed for analytics, pages load faster.
Can you fix existing pipelines without rebuilding? +
Usually. We audit, find the failure points — bad joins, missing tests, schema drift — and fix in place. Full rebuilds are a last resort. Most fixes take 4–8 weeks.
How do you ensure data quality? +
Automated checks at every stage — dbt tests, Great Expectations assertions, source freshness checks. When something breaks, you know in minutes, not when a VP asks.

Ready to fix your data stack?

20 minutes. No slides. We'll tell you what we'd do, what it costs, and whether you actually need us.