Data mesh is an approach to organizing data, formalized by Zhamak Dehghani in 2019, that gives responsibility for data to each business domain rather than to a single central data team.
It rests on four principles. Each domain owns and maintains its data. That data is treated as a product, with an owner, documentation and quality commitments. A shared self-service platform saves each team from rebuilding everything. Finally, federated governance sets the shared rules (security, formats, interoperability).
Data mesh addresses the bottleneck of the central data team, swamped with requests about data it knows poorly. In practice, it is tooled with data contracts, a data catalog and an internal data platform.
It is first and foremost an organizational change. It makes sense in large companies with many domains, much less in a company with a single data team.
Why it matters when hiring
Data mesh is mostly hired for at data lead, data architect or VP Data level, because the topic is organizational. In an interview, ask what failed: a real practitioner talks about team resistance, platform cost and poorly split domains. A purely theoretical speech about the four principles is a weak signal.