Building flexible data teams to improve product delivery
summary
Panel lays out when to centralize versus embed: choose centralized unless embedded domains are large enough to support career growth, or when you can form a minimum 4–5 people domain team; one org restructures mid‑2021 to a decentralized, platform + center‑of‑competency model. Practical tools and habits: Anomalo for data quality, SQL is essential, legacy Cron/shell pipelines give way to analytics engineering. Roles split into data engineers, analytics engineers, ML engineers and data scientists; beware underpowered AB tests that can take years to hit significance.
AI-written summary · about
chapters (7)
- 0:00Introduction and speakers
- 3:17Data team structures overview
- 7:57Centralized vs embedded models
- 15:25Team roles and skill sets
- 22:50Evolving data team titles
- 26:42Career advice for data newcomers
- 29:30Closing remarks
related talks
55mBuilding The Data Dream Team
38mBuilding Effective Data Teams: Strategies for Success
18mHow to create a high performance data team: lessons learned from the field
29mBuilding successful data teams in the realities of 2023
50mBuilding Your Data Analytics Team - live RunAs Radio episode
22mBuild Smarter, Don't Just Build
23mData science as a team sport
26mScaling engineering organizations