Metadata
- Author: Emmanuel Martin Chave
- Full Title:: Scaling Data Teams: 5 Learnings From BlaBlaCar
- Category:: 🗞️Articles
- Document Tags:: data team topologies, Data team topologies,
- URL:: https://www.blef.fr/r/7ecefe46?m=6d6aa78e-ae1e-424c-b3c3-accaaae0b850
- Finished date:: 2023-07-08
Highlights
Yet another example that shows we need multidisciplinary squads
45 people are divided into 6 teams: 5 multidisciplinary squads, and one platform-oriented team. The squads are composed of Data Analysts, Data Scientists, Software Engineers and Data Engineers. That makes them autonomous on all data projects. (View Highlight)
In 2021, the Data department was organized by technological layers (View Highlight)
expertise. It also created a layered technical stack; interoperability between layers was low (View Highlight)
This model stopped scaling in 2021: Data teams became a bottleneck (View Highlight)
Data teams needed multidisciplinary squads tied to business domains (View Highlight)
We picked the easiest areas to make independent. We factored in known internal moves, accelerating some changes. We created last the hard-to-isolate domains, leveraging experience from our previous moves. (View Highlight)
By molding our teams on business areas, we maintained close links with stakeholders. (View Highlight)
New highlights added 2023-07-08
- What about keeping the expertise for each job? BlaBlaCar wanted to maintain common practices among experts in squads. So we created Chapters (View Highlight)
One transverse team — Data Ops — provides the common infrastructure and services to other squads. For instance, Data Ops builds ingestion patterns consumed by the Data Engineers in the squads. At our current size, this setup works without making Data Ops a bottleneck. As the team grows and matures, some parts start being distributed to squads. (View Highlight)
To avoid creating silos, members from the DataOps team often temporarily join squads (View Highlight)