A scaleup added 3 data engineers to catch up. Delivery got slower for 2 months.
Brooks’ Law is 50 years old: adding people to a late project makes it later. Data teams tend to get a harsher version.
A new backend engineer can read the code. A new data engineer inherits tribal knowledge: which pipelines are load-bearing, which dashboards anyone still reads, why the orders table has 3 half-owners. Little of it is written down.
Conway’s Law stacks on top. Pipeline sprawl mirrors the team seams, and every hire adds coordination paths. 3 people have 3. 6 people have 15.
What worked at that scaleup before the next hire: an owner per pipeline, roughly a third of the DAGs deleted (nobody missed them), and the 5 tables that carried the business documented. The next hire shipped in week 2 instead of month 2.
Headcount helps once context is cheap to transfer.
If you added a data engineer next Monday, what would they work on Tuesday?