Content rendering at Uber scale

Uber’s communications platform needed to author and render real-time personalized content across its lines of business. Over two roles, I worked on three connected constraints: personalization setup, rendering latency, and attribute reliability.

Treating them as a platform portfolio—not one monolithic project—made it possible to sequence product enablement, hot-path performance, and storage reliability without conflating their results.

As a Software Engineer I, I led a unified personalization engine and designed the data model and integrations that reduced repeated enablement work across authoring tools.

As a Software Engineer II, I led the rendering investigation, set performance targets, designed the new path, profiled the hot spots, and coordinated implementation with the FeedManager team.

Recurring attribute-store failures affected hundreds of templates and made privacy features harder to ship. I helped redesign the storage boundary and migrate it from deprecated Cassandra infrastructure to Docstore.

The resulting platform added anonymization support, table lifecycles, and more reliable runtime behavior while reducing the operational burden carried by each consuming product.

Static-content p99 fell from 140 milliseconds to 7 milliseconds and dynamic-content p99 from 210 milliseconds to 35 milliseconds while serving 10 million impressions each day.

Across the related platform work, attribute onboarding fell from one week to 30 minutes, the platform rendered one to two billion expressions daily, and the storage redesign prevented roughly seven million rendering failures.