Scattered sources
Different structures, histories, and metric definitions.
MARKETING DATA ENGINEERING
Your marketing data warehouse.Ready for reporting.
I build or improve your BigQuery warehouse to centralize the marketing sources your team relies on and reduce manual report preparation. You get documented data models, scheduled updates, and validated tables ready for dashboards and analysis.
For agencies and marketing teams.
YOUR REPORTING FOUNDATION
YOUR SOURCES
01Google Ads
02Meta Ads
03GA4 / CRM
BigQueryClean · Model · Validate
Documented model
REPORTING TABLES
Campaigns
Performance
01 / THE PROBLEM
Too much time goes into preparing the data.
When campaign, CRM, and analytics data live in different platforms and files, every report starts with cleaning, joining, and checking the same information again.
Manual preparation
Files and transformations have to be rebuilt or adjusted every reporting cycle.
Hard-to-explain differences
Numbers are difficult to trust when the source and calculation logic are unclear.
02 / WHAT’S INCLUDED
A data foundation your team can actually use.
The implementation is built around a defined reporting or analysis use case, with clear sources, data rules, and outputs.
Validated with data, not just a working connection.
We review coverage, quality rules, and differences from the source before handover.
01
Source integrations & loads
Connections to the agreed sources, with documented historical coverage and refresh frequency.
02
Warehouse & data model
Tables, identifiers, and relationships organized so the data can be queried at a consistent level of detail.
03
Analysis & reporting tables
Tables or views with documented dimensions, metrics, and calculation rules for the defined use case.
04
Quality controls & handover
Validation checks, issue records, and documentation for reviewing updates and handling exceptions.
The core service delivers the data foundation. Full dashboard design, presentations, and automated distribution are scoped separately when needed.
03 / INTERACTIVE DEMO
See what happens when the data arrives again.
Explore a synthetic data example and see how repeated loads, updates, and exceptions affect the reporting output.
5 stages · approximately 12 seconds
Interactive simulation using synthetic data. It does not connect to real accounts or process client data.
Google AdsMeta AdsLinkedIn Ads
- Ingest01
- Transform02
- Validate03
- Load04
- Result05
Comparable metrics use the same period, currency, and documented definitions.
Reporting table
One row per platform, account, campaign, and day.
| Source / campaign | Spend | Clicks | Status |
|---|---|---|---|
| Google AdsG-01 · Brand search | USD 1,200 | 1,200 | Valid |
| Meta AdsM-01 · Prospecting | USD 1,000 | 2,000 | Valid |
| Google AdsG-01 · Generic search | USD 500 | 450 | Valid |
| LinkedIn AdsL-01 · Fund information | USD 300 | 150 | Valid |
| Validated total | USD 3,000 | 3,800 | 4 rows |
- Input records
- 6
- Valid records
- 4
- Duplicates
- 2
- Exceptions
- 0
- Reporting rows
- 4
Validated example. Replay the load or simulate a change.
04 / HOW I WORK
A clear process. A usable data foundation.
We start with a specific use case and leave the data model, controls, and handover documented.
- 01
Define and connect
We review your sources, accounts, historical coverage, and update frequency. I configure extraction and keep the source traceable.
- 02
Build and validate
I organize the tables, document metrics and check results against the agreed sources.
- 03
Deliver and enable
I deliver reporting-ready tables, validation checks and a guide to run the workflow and review exceptions.
05 / CORE TOOLS
Real tools. Matched to your environment.
The exact stack depends on the sources, existing environment, and agreed scope.
- Google Ads
- Meta
- GA4
- BigQuery
- SQL
- Python
- Looker Studio
- dbt / Dataform
06 / BEFORE WE START
Scope and common questions.
Before implementation, we agree on the sources, available history, required outputs, refresh frequency, and acceptance criteria.
01Can you work with my existing warehouse?
Yes. We first review what already exists and decide what should be retained, fixed, or extended. A specific use case does not require rebuilding the entire environment.
02Does this include finished dashboards or reports?
The core scope delivers the warehouse and reporting-ready tables. Full dashboard design, presentations, and automated delivery can be added as a separate reporting automation scope.
03What do you need to get started?
I need to understand your sources, the reporting or analysis need, and the current process. Required access is defined after the scope is reviewed; you do not need to share credentials in the initial conversation.
04How are the budget and timeline determined?
They depend on the number and accessibility of sources, available history, refresh frequency, model complexity, and required outputs. The proposal separates implementation work from platform and ongoing support costs.
HAVE A DATA PROJECT?
Let’s talk.
Tell me what your team needs.
Share the sources you use, the reporting problem you want to solve, and what is getting in the way. We can discuss whether this service is a fit and define the next step.
Talk onLinkedIn