What is marketing data engineering?
Marketing data engineering is building the plumbing that moves data from your tools into one place where it can be trusted and reported on. It covers extracting, cleaning, joining and scheduling that data.
Most growing businesses hit the same wall. Someone downloads exports from Google Ads, Meta, GA4 and the CRM every Monday, pastes them into a spreadsheet, and fixes the formulas that broke. It eats hours and errors creep in. A pipeline does that work on a schedule, the same way every time.
Should we start with Google Sheets or a data warehouse?
Start with Google Sheets if your data is small and your questions are simple. Move to a warehouse like BigQuery when Sheets becomes slow, fragile, or unable to hold your history.
| Stage | Good fit | Signs you've outgrown it |
|---|---|---|
| Google Sheets | A few sources, weekly reporting, small team | Slow files, broken formulas, row limits |
| Sheets + Apps Script | Scheduled pulls and simple automations | Scripts time out, logic gets hard to follow |
| BigQuery warehouse | Many sources, long history, joins with CRM data | Rarely outgrown at small-business scale |
We have built inventory workbooks and Apps Script automations for clients, including report drafts saved for a person to review before sending. Spreadsheets are a fine starting point when they are built with structure.
What is the difference between ETL and ELT?
ETL transforms data before loading it into storage. ELT loads raw data first and transforms it inside the warehouse. For most marketing work today, ELT into BigQuery is the simpler choice.
Loading raw data first means you keep the original records. If a business rule changes, like how you define a qualified lead, you rewrite the SQL and rerun it. Nothing has to be pulled again from the source. We write transformations in SQL, use Python where SQL is awkward, and keep the logic in version control so changes are reviewable.
What data sources can you connect?
We connect the systems that hold your marketing and revenue data, using native connectors, third-party tools or custom code through APIs.
- Google Ads, Local Services Ads, Meta Ads and LinkedIn.
- GA4, including the BigQuery export, and Search Console.
- CRMs such as GoHighLevel and field-service platforms.
- WooCommerce orders, products and inventory.
- Call tracking, email platforms and Google Sheets.
Once the data lands in one place, it feeds Looker Studio dashboards through marketing analytics and revenue reporting through marketing attribution.
How do you keep data pipelines reliable?
We build pipelines to fail loudly and recover cleanly. A dashboard that silently stops updating is worse than no dashboard.
- Scheduled runs with alerts when a job fails or data looks wrong.
- Checks for duplicates, missing days and sudden swings.
- Documented tables, so anyone can see where a number came from.
- Access controls that limit who sees customer data.
Clean, well-documented data is also the starting point for data science work like forecasting and lead scoring, and for AI automation that acts on your data.
How is data engineering priced?
Data engineering is handled by our Engineering team. Pipelines and warehouse builds are quoted per project after we review your sources and reporting needs, and ongoing support can be added after launch. Warehouse and connector costs are billed by those providers and are usually modest at small-business scale. We work with teams across Houston, Texas, and the US.