How would you improve reliability and latency for Discover Weekly at global scale

Problem Statement Description

Product context: Spotify is an audio streaming company; its products include music, podcasts, audiobooks, playlists, recommendations, creator tools, subscriptions, and ads.

Spotify’s Discover Weekly is a highly personalized playlist experience that premium subscribers expect to be ready, fresh, and fast every week across global markets. The feature depends on large-scale recommendation pipelines, user listening signals, catalog metadata, ranking systems, playlist generation, storage, and client delivery across mobile, desktop, web, and connected devices.

In this technical PM interview, you are asked to frame how you would improve reliability and latency for Discover Weekly at global scale. Focus on the end-to-end product and technical workflow: from data ingestion and model generation to playlist availability, serving performance, client load time, and user-visible failure modes. The scope includes both backend batch/near-real-time systems and the subscriber-facing experience when the playlist is opened, refreshed, or fails to load.

Your response should show how you reason through requirements, system constraints, trade-offs, dependencies, observability, rollout, and product impact without jumping directly to implementation details. Consider Spotify’s personalization expectations, global traffic patterns, premium subscriber retention, and the need to protect trust in weekly discovery.

The experience should consider:

- What “reliability” and “latency” mean for Discover Weekly across generation, publishing, API serving, and client playback entry points

- Key user journeys, including first open on release day, playlist refresh, offline access, low-connectivity markets, and cross-device consistency

- Critical systems and data dependencies, such as listening history, catalog availability, recommendation models, playlist storage, CDN/API layers, and client caching

- Service-level objectives, error budgets, latency targets, freshness expectations, and user-visible degradation thresholds

- Instrumentation needed to detect failures, slow paths, stale playlists, missing recommendations, regional anomalies, and premium-subscriber impact

- Privacy, data governance, rights availability, and security considerations when using personal listening data and catalog metadata globally

- Rollout strategy, experimentation approach, fallback behavior, incident response, and rollback criteria for reliability or latency improvements

- Product trade-offs between personalization quality, compute cost, freshness, scalability, and fast delivery

The goal is to define a technically credible product approach for making Discover Weekly consistently available and responsive worldwide, while preserving personalization quality and protecting the premium subscriber experience.