# SERVICE 02 / 04
Data you can trust.
Pipelines that don't break
Tracking leaks to third-party domains. Consent rejection eats 30–45% of your data. Your analysts have stopped trusting the warehouse and started rebuilding it in Excel. We replace the broken parts — server-side tracking, a consent layer that actually works, data quality gates — so Monday morning's numbers are ones you can bet on.
70%
Faster time-to-insight
Zero
Cookie banners needed
4–8 wk
Typical pipeline fix
Audit-grade
Data quality
# THE-PROBLEM
Why this matters
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.
Gartner estimates that poor data quality costs organizations an average of $12.9 million per year. Most of that cost isn't in the tools — it's in the decisions made on bad data before anyone notices.
// SOUND FAMILIAR?
- 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
# IS-IT-FOR-YOU
Where we fit — and where we don't.
// best fit for
- You've lost measurable data to ad blockers and consent rejection
- Multiple teams report different numbers from the same warehouse
- Consent management is drifting toward a legal risk
- You want to keep tracking and analytics on EU infrastructure
// not ideal for
- Greenfield analytics setups with no data or stack in place yet
- Pure marketing-attribution projects — not our focus
- Organizations committed to keeping client-side GTM as the long-term plan
# HOW-WE-WORK
Four steps, no ceremony.
Audit
Full tracking and pipeline audit. Every tag, consent flow, event, and data path documented. We find what's broken before we touch anything.
Architecture design
Server-side tracking, consent layer (OneTrust/Usercentrics), clean tagging architecture. Privacy-first from the ground up.
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.
Quality & governance
Automated quality gates, documentation, and handover. Your team can maintain and extend the pipeline without us.
First named release usually ships around week 6 — mid-way through production engineering, not at the end.
// typical anchors: architecture audit from €5,900 net (fixed) · fixed-scope builds from €12,000 net · fractional lead from €1,200 net/day
# THE-STACK
Production-tested tools, not a wish list.
# DEFINITION
What is data engineering & analytics consulting?
Data engineering consulting covers pipeline architecture, server-side tracking implementation, and data quality frameworks that make enterprise analytics trustworthy and audit-ready. We build this with consent management (OneTrust, Usercentrics), ETL/ELT pipelines, and GDPR-compliant data flows.
# FAQ
Common questions.
How long does a data engineering engagement typically take?
Most projects run 8–12 weeks. We start with a 1-week data audit, then build incrementally with weekly deliverables. You see working pipelines within the first 3 weeks.Can you fix our existing pipelines or do we need to start over?
We almost always fix and extend. Full rebuilds are rare and usually unnecessary. We audit what works, replace what's broken, and add quality gates so problems don't recur.Do you work with our existing data warehouse (Snowflake, BigQuery, Redshift)?
Yes. We're warehouse-agnostic and have shipped production work on all three. We won't recommend a migration unless there's a clear cost or capability reason.
// RELATED WORK
Proof, not promises.
// FROM THE JOURNAL
The long-form version.
When the dbt build passes but the dashboards still lie
A green dbt build doesn't mean your metrics are right. How to add semantic data-quality gates in dbt and Snowflake, and one trusted layer for Power BI.
Client-side GA4 is leaking 30% of your data. Here's the fix.
Client-side GA4 leaks 25-40% of pageviews to ad blockers and DACH consent rejections. Server-side tracking closes the gap — and improves GDPR posture.
Ready to put data engineering into production?
A 30-min call. We'll bring an architecture sketch and a rough number.
book-call// or write: hello@saloid.com · gräfelfing · de