How would you track clicks on a new 'Export Data' button?

WHAT THIS TESTS: This question evaluates whether you treat product analytics as a first-class system or a hacky add-on. The interviewer wants to see end-to-end thinking across schema design, network reliability, data privacy, and queryability. Senior engineers should demonstrate awareness of batching, retry semantics, consent management, and how analytics data actually gets used by product managers and data scientists.

A GOOD ANSWER COVERS: First, schema design. Propose a typed event like ExportDataClicked with a version field, timestamp in ISO 8601, and a standard envelope containing event_name, event_version, user_id, session_id, and device_id. Second, client instrumentation. Attach a click listener that validates the payload against the schema before enqueueing it locally. Use navigator.sendBeacon for unload resilience, or an in-memory queue that flushes every five seconds or when it reaches fifty events. Third, transport and delivery. Mention retry with exponential backoff for 4xx and 5xx errors, deduplication using a client-generated event_id, and handling offline state. Fourth, metadata essentials. Include user_id or anonymous_id, client_timestamp and server_timestamp, app_version, os_name, browser_name, feature_flags active at click time, and the current URL path. Fifth, privacy and compliance. Gate collection behind a consent manager, scrub PII from query parameters and payloads, and respect doNotTrack or global privacy control signals. Sixth, storage and access. Land events in a columnar store or data lake partitioned by date, making them queryable in SQL or a product analytics tool like Amplitude or Mixpanel.

COMMON WRONG ANSWERS: Saying you would just add an onClick handler that fires a fetch request to an analytics endpoint. This ignores data loss during navigation, mobile backgrounding, or flaky networks. Another red flag is omitting timestamp discussion or suggesting you only send client time without recording server ingestion time for drift correction. Proposing to track every user without mentioning consent, sampling, or data retention policies is also a serious gap at senior level. Finally, failing to include feature flags or app version makes it impossible for the product manager to segment the data.

LIKELY FOLLOW-UPS: How would you handle a spike of ten thousand clicks per second without dropping events or bankrupting your ingestion budget? How do you reconcile events when a user clicks from a web client but the export actually completes on a backend worker? What is your strategy for backfilling or replaying events if you discover a schema bug? How do you prevent duplicate counts if the user double-clicks or the retry logic fires twice?

ONE CONCRETE EXAMPLE: Imagine a SaaS dashboard where the Export Data button lives on the Reports page. Your schema defines ExportDataClicked v1 with properties including report_type, file_format_preference, and active_subscription_tier. The frontend SDK enqueues the event on click, flushes the queue every three seconds or on pagehide using sendBeacon, and includes a UUIDv4 event_id. The ingestion service writes to a Kafka topic, then a consumer lands the data in Snowflake partitioned by event_date. The product manager can then write a query grouping by report_type and subscription_tier to see which features drive exports, while the engineering team monitors end-to-end latency and drop rate via a dashboard showing beacon success versus fallback XHR retry counts.