#181 Learnings from BlaBlaCar's Early Data Mesh Journey: Positive Transformation for the People and the Organization - Interview w/ Kineret Kimhi

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Kineret's LinkedIn: https://www.linkedin.com/in/kineret-kimhi/Kineret's Blog Post 'Do’s and Don’ts of Data Mesh': https://medium.com/blablacar/dos-and-don-ts-of-data-mesh-e093f1662c2dIn this episode, Scott interviewed Kineret Kimhi, Analytics Lead at BlaBlaCar.Some key takeaways/thoughts from Kineret's point of view:!Interesting Decision!: BlaBlaCar reorganized their data organization but did not fully decentralize by embedding people into domains. Instead, they kept a central team but combined multiple functions into a squad around domains - a key domain might have a data engineer, data analyst, data scientist, and a software engineer.!Scott Mantra Too!: Sharing your experience - data mesh or otherwise - early and often with the broader data community means better and quicker feedback, not just internal experience. It's okay to be vulnerable about what didn't go well, you can get better info and help save others the same pain.?Crucial?: It's very important that when you split up your teams from functional data role teams, people keep in contact with functional role peers. If not, it can be very lonely as the only data engineer inside a domain. There is a significant turnover risk and a risk to not having scalable learning and knowledge transfer of data work if not handled well.Data mesh will lead to a lot of potential changes to people's ways of working, especially with each other. Don't shy away from that, people need to know you aren't forgetting they need career development and that you'll support them as they learn and get...