Yubo operates a social discovery app centered on direct connection over algorithmic feeds, running millions of active users on a data-intensive infrastructure stack (Kafka, Kafka Streams, MongoDB, Elasticsearch, dbt, Airflow). The project list—A/B experimentation, growth experiments, backend foundations, design system scaling—reveals a platform optimizing for retention and conversion at scale. Pain points around data pipeline throughput, interaction tracking reliability, and churn reduction map directly to these initiatives, signaling heavy investment in analytics-driven product iteration.
Yubo is a social discovery platform designed for Gen Z to form genuine connections without likes, follows, or algorithmic ranking. The app operates at scale with millions of active users across iOS, Android, and web. The company is structured around engineering, product, and data teams, with hiring concentrated in senior and lead roles in France and the United Kingdom. Safety and user protection are stated as core operational principles, reflected in the platform's design and community governance approach.
Yubo's infrastructure centers on Kafka (including Kafka Streams and Connect) for event streaming, MongoDB and Couchbase for data storage, and Elasticsearch for search. Analytics runs on dbt, Airflow, Trino, and Looker, with Datadog and Grafana for observability. Mobile surfaces use iOS and Android SDKs with Firebase integration.
Active projects span A/B experimentation, growth optimization, backend foundations, design system scaling across mobile and web, and analytics workflow improvements. Internal initiatives include observability tooling and automation to reduce bottlenecks.
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