Area 01MARKETING DATA ENGINEERING
Marketing data in one model. Ready for reporting.
I build BigQuery warehouses that bring marketing sources into documented models, with scheduled loads and validated tables for dashboards and analysis. It is the least visible part of reporting, and the one that holds it up.
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Typical sources: paid media, web analytics, and CRM.
01THE 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.
Scattered sources
Different structures, histories, and metric definitions.
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.
02WHAT I BUILD
A foundation that can be queried, reloaded, and explained.
Every table has a reason to exist: it feeds a report or an analysis with defined sources, data rules, and level of detail.
- 01
Source integrations & loads
Each source connected, with its historical coverage and refresh frequency documented.
- 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 each use case.
- 04
Quality controls & operations
Validation checks, issue logs, and documentation for reviewing updates and handling exceptions.
Validated with data, not just a working connection.
Before I consider a table done, I check coverage, quality rules, and differences from the source.
03INTERACTIVE DEMO
A reliable foundation, one record at a time.
Follow the data from the incoming file to the table the marketing team will use. Then put its checks to the test.
INTERACTIVE DEMO
A reliable foundation, one record at a time.
Follow the data from the incoming file to the table the marketing team will use. Then put its checks to the test.
The file adds up to USD 5,200.Valid spend is USD 3,000.
Two records arrived twice. See how their copies are identified before they inflate the report.
Repeated amount: USD 2,200
6 records received
CSV / 06- 01Google AdsBrand searchUSD 1,200
- 02Meta AdsProspectingUSD 1,000
- 03Google AdsGeneric searchUSD 500
- 04LinkedIn AdsFund informationUSD 300
- 05Google AdsBrand searchUSD 1,200Copy of 01
- 06Meta AdsProspectingUSD 1,000Copy of 02
Sample data. All amounts are expressed in USD before rows are compared.
04HOW I WORK
A clear process. Numbers that reconcile or are explained.
Three steps. The data model, the controls, and how the workflow runs are all documented.
- 01
Define and connect
I review sources, accounts, historical coverage, and update frequency. I configure extraction and keep every record traceable to its source.
- 02
Build and validate
I organize the tables, document metrics and check results against each source.
- 03
Document and run
The result: reporting-ready tables, automated checks and a guide for running the workflow and reviewing exceptions.
05Labs
Labs in this area
Tools that show this area’s method, with fictional or sample data.
06CORE TOOLS
The tools I work with.
BigQuery and SQL for the model, Python for loads and checks, and Data Studio to review the output.
- SQL
- Python
- BigQuery
- Google Cloud
07TECHNICAL QUESTIONS
Questions about the method.
Before building, I write down the sources, available history, level of detail, refresh frequency, and validation criteria.
Each table has a key defined by its grain: platform, account, campaign, and day. Loads merge on that key, so a reload creates no new rows and a correction updates the existing record. That is what the demo shows.
It stays out of the reporting table and is logged as an exception, with its reason. It is not turned into zero or silently dropped: it is reviewed, fixed at the source, and picked up by the next load.
I compare totals by period, account, and metric against the source, using the same time zone and currency. Expected differences, such as attribution windows or late-arriving data, are documented rather than forced to match.
It works for a quick view, but every report ends up repeating the same logic and definitions drift apart. With an intermediate model in BigQuery, metrics are calculated once, history is kept, and every dashboard or analysis starts from the same tables.
08CONTACT
Interested in the topic?
If you work on data modeling, loads, or data quality in marketing, I’d like to compare approaches.