Start by clarifying the table schema and the grain of the data, then outline a SQL query that groups by job_id and uses conditional aggregation with COUNT(DISTINCT CASE WHEN ...) to compute the three metrics. Explain how you would handle edge cases like duplicate activities and ensure the query is efficient for large datasets.
Pro tip: Mention that you would validate the results by checking that unique viewers and appliers are subsets of total activities, and consider whether the funnel metrics should be computed at the job level or also segmented by time or candidate attributes.
Confirm the table structure, the meaning of each column, and whether activities can be duplicated. Ask if the metrics should be calculated over all time or a specific period.
Explain that you will group by job_id and use conditional aggregation to count distinct candidates for views and applies separately, while total activities is a simple count of all rows.
Construct a query using COUNT(*) for total activities and COUNT(DISTINCT CASE WHEN activity_type = 'view' THEN candidate_id END) for unique viewers, similarly for appliers.
Discuss handling NULLs, duplicate rows, and large data volumes. Mention indexing on job_id and activity_type, and possibly using approximate distinct counts if scale is huge.
Suggest sanity checks like ensuring unique viewers and appliers are less than or equal to total activities, and explain how these metrics inform product decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the metric definitions and the business context for excluding zero-view applicants, then walk through the SQL/Pandas logic to recalculate, and finally propose a principled treatment for multi-job applicants with zero views—likely a separate segment or exclusion with clear documentation. Emphasize that the decision should align with the metric's purpose (e.g., measuring engagement vs. conversion).
Pro tip: Proactively mention that zero-view applicants might represent a distinct user segment (e.g., auto-apply or bot traffic) and suggest analyzing their behavior separately before deciding to exclude them, showing you think beyond the immediate ask.
Confirm what 'viewed any job' means (e.g., job detail page view) and why we're excluding zero-view applicants—likely to focus on engaged users. Ask if the goal is to measure conversion or engagement.
Write pseudocode/SQL to filter out applicants with zero views across all jobs, then recompute the metrics (e.g., apply rate, view-to-apply ratio). Show before/after comparison.
Decide whether to exclude them entirely or treat as a separate segment. If excluded, note the impact on sample size and potential bias; if kept, analyze their behavior separately.
Check for data quality issues (e.g., logging errors) and assess if the exclusion changes conclusions. Discuss implications for product decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.