Event Metadata & Natural-Language Analytics Prototype
Situation
Event tracking at ezCater had been built bespoke over years by different teams with no shared metadata layer. There was no centralized schema, no discoverability, and no way for anyone outside engineering to understand what events existed or what they meant.
Breaks could go undiscovered for days or weeks. Senior leaders were regularly pulled into Slack threads to debug event data issues. Product managers had zero discoverability into what was being tracked. Marketing and analytics teams had no reliable way to understand event taxonomy.
I led a team of 7 including Staff Engineers and Engineering Managers on the Events and Messaging Platform team.
Decision
We needed a testable metadata layer for event tracking and out-of-the-box functionality that could spin up easily. I progressed the idea of using Infrastructure-as-Code patterns and collaborated with the staff engineer to develop plans and move in that direction. IaC made a lot of sense for what we needed — a structured metadata registry that could be programmatically validated, queried, and versioned. We greenlit it.
In March 2025, I created a React visualization prototype demonstrating natural-language interfaces for event data — built through conversations with Claude.ai. The prototype showed concrete use cases:
- A PM running funnel analysis by asking questions in plain language
- A marketing analyst doing attribution analysis against structured metadata
- Conversational queries against the full event taxonomy
This was not a dashboard. It was a prototype for an agent that understands event taxonomy. It visualized the ambitious final form of our metadata efforts. This was created before Claude Code had reached anything close to mainstream — and Claude Code later demonstrated the vision was not far-fetched.
[See the Event Streaming Simulator exhibit for the React prototype.]
Risk
The metadata layer added process to event creation in a team that had operated without constraints. There was real risk of adoption resistance. The natural-language prototype was speculative and could have been dismissed as a distraction from platform work.
I accepted both risks because the status quo was unsustainable: unstructured event data was creating escalation load, PM dependency on engineering for basic analytics questions, and silent breakage across the funnel.
Change
The metadata layer gave the platform team a foundation for automated validation and schema enforcement. Event discoverability moved from “ask someone in Slack” to a structured registry.
The natural-language prototype shifted the conversation from “how do we build better dashboards” to “how do we make event data queryable by non-engineers.” [Confirm: whether this prototype influenced subsequent product direction or tooling decisions.]
What This Demonstrates
- Seeing the structural problem: Not just “events break” but “nobody can find or understand events, and that’s why they break silently.”
- Collaborative IaC adoption: Progressed the IaC idea and worked with the staff engineer to plan and execute — not top-down mandate, but guided direction.
- Building ahead of consensus: The natural-language prototype visualized the future before the organization was asking for it.
- Domain ownership as a manager: I was deeply involved in the domain problem, not managing from a distance. The React prototype was a demonstration artifact that showed the vision.