Data Engineering
One set of numbers everyone agrees on, instead of four dashboards that disagree.
What this includes
- Reporting pipelines and scheduled aggregation
- Analytics and event tracking design
- Data warehouse setup for small teams
- Dashboards built on defined metrics
The meeting where two reports disagree
Sales says one number, finance says another, and the next forty minutes go to arguing about the data instead of deciding anything. The cause is almost never a broken tool. It is that nobody wrote down what the metric means.
Where we start
- Define each metric in words first: what counts, what does not, and over what period
- Build one pipeline that produces it, on a schedule, with the calculation in one place
- Point every dashboard at that, so there is nothing left to disagree about
- Keep the raw data, so a definition can change without losing history
Small teams do not need a warehouse the size of a bank's
We size this to your actual volume. Most businesses we work with need a scheduled job and a well-designed table, not a platform with a monthly licence.
Reporting that only your team sees belongs under internal systems.
Questions we get asked
Do we need a data warehouse?
Probably not yet. Below a few million rows, a well-indexed table in your existing database and a nightly job will do the same job for a fraction of the cost and complexity.
Can you fix our existing tracking?
Yes, and the first step is an audit of what currently fires, what double-counts and what was never installed. Most tracking problems are duplicates and missing events, not missing tools.
Solutions
Internal CRM
Customer history that belongs to the company, not to whoever's phone it happened on.
Internal Systems
The unglamorous tools your team lives in all day. Worth building properly.
Case Studies
A Ticketing Backend Where Slow Checkout Was Costing Sales
Slow API responses were pushing buyers to abandon carts. Database work and caching fixed the cause, not the symptom.