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Observability, Quality, Cost & Security1 / 3

1 min lesson

Data observability & quality

Show why this lesson detail matters: "The job description names it directly: build observability, alerting, SLAs/SLOs and operational standards across the platform."

Step 1 of 3

The job description names it directly: build observability, alerting, SLAs/SLOs and operational standards across the platform. In the loop that line becomes a probe - when a column goes null at 3am, do you find out from a dashboard or from an analyst's angry Slack?

Cursor ingests billions of product-telemetry events a day and a slice of that feeds agent and model work. Bad data there is not a cosmetic bug. A silently broken bronze table can poison a gold metric a VP reads or skew a feature an agent learns from. So the interviewer wants a mental model of what can go wrong, not a list of vendor names.

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Advanced table

Freshness and volume catch the obvious outages

Dimension
Freshness
Question it answers
Did new data land on time?
Concrete check at Cursor scale
Bronze telemetry partition for the current hour exists and is < 15 min late
Dimension
Volume
Question it answers
Did the right amount arrive?
Concrete check at Cursor scale
Event count within ±20% of the same hour last week; zero rows = page
Dimension
Schema
Question it answers
Did the shape change underneath us?
Concrete check at Cursor scale
New/dropped columns, type drift, an enum that grew a value the parser ignores
Dimension
Distribution
Question it answers
Are the values plausible?
Concrete check at Cursor scale
Null rate on user_id, p99 of latency_ms, ratio of a category that suddenly flips
Dimension
Lineage
Question it answers
What breaks if this breaks?
Concrete check at Cursor scale
Map from a bronze asset to every silver/gold/serving consumer downstream

Freshness and volume catch the obvious outages; schema and distribution catch the quiet corruptions that hurt most.