This one took me longer than it should have.
Start by clarifying the data model and attribution logic, then outline a SQL-based solution using window functions and joins to enforce the time-bounded windows. Finally, compute daily counts and conversion rates, and discuss how to handle edge cases like multiple touches and late-arriving data.
Pro tip: Mention that you would validate the funnel by checking for negative or >100% conversion rates, which can occur if attribution windows overlap or if users have multiple events. Also, consider using a user-level deduplication strategy to avoid double-counting.
Ask about the event tables (impressions, clicks, signups, subscriptions) and their schemas, including timestamps and user IDs. Confirm the attribution windows and whether they are inclusive or exclusive.
For each stage, determine how to link events within the time window. For example, a click is attributed to an impression if it occurs within 1 day after the impression and belongs to the same user.
Use self-joins or window functions to find the first qualifying event at each stage. For instance, for each impression, find the earliest click within 1 day; then for each click, find the earliest signup within 3 days, etc.
Group by date (based on the impression date) and count distinct users or events at each stage. Compute conversion rates as the ratio of counts between consecutive stages.
Address multiple touches (e.g., use first-touch or last-touch attribution), late-arriving data, and timezone considerations. Validate results by checking for anomalies like conversion rates exceeding 100%.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Median in SQL always makes me pause because you can't just AVG().
Start by clarifying the schema and defining the cohort: users who signed up in August 2025, with signup date and acquisition channel derived from campaign_id. Then write a SQL query that joins signups to the first watch_start event per user, computes the time difference in minutes, and uses a window function or percentile_cont to get the median per signup date and channel. Finally, validate the results and discuss any data quality or interpretation caveats.
Pro tip: Mention that you would check for users with no watch_start events and decide whether to exclude them or treat their time as infinite, as this can significantly affect the median. Also, note that using the first watch_start per user avoids double-counting and ensures the metric reflects initial engagement.
Confirm the definition of 'first watch_start' (earliest event per user), the signup date range (August 1-31, 2025), and how to classify acquisition channel (paid if campaign_id is not null, organic otherwise). Identify the relevant tables and join keys.
Filter users who signed up in August 2025, join to their first watch_start event, and calculate the time difference in minutes between signup and first watch_start. Ensure you handle users with no watch_start appropriately.
Group by signup date and acquisition channel, then compute the median time using a percentile function (e.g., PERCENTILE_CONT(0.5) in SQL) or a window function. Be mindful of small sample sizes per group.
Check for outliers, missing data, and whether the median is stable across groups. Consider if the metric aligns with business expectations and discuss any limitations, such as censoring for users who haven't watched yet.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is basically a fraud detection filter.
Start by clarifying the schema and definitions (e.g., how CTR and distinct users are calculated, time zone for 'last 7 days'). Then outline a SQL query that filters campaigns by date, impressions >= 100, CTR > 80%, and distinct users <= 5, and returns the required fields. Finally, discuss how to validate results and interpret them in a fraud detection context.
Pro tip: Mention that you would also check for other anomalies like unusually high impression-to-click ratios or suspicious user agents to strengthen the fraud detection, showing you think beyond the immediate query.
Confirm the definitions of CTR, distinct users, and the time window (e.g., last 7 days from today, using which time zone). Identify the relevant tables and columns (e.g., ad_events, campaigns).
Write a query that filters events from the last 7 days, aggregates by campaign, calculates CTR and distinct users, and applies the conditions: CTR > 80%, impressions >= 100, distinct_users <= 5.
Check for edge cases (e.g., division by zero, NULLs) and consider if the thresholds are appropriate. Interpret the findings: these campaigns likely indicate click fraud or bot activity.
Present the suspicious campaigns with metrics, and suggest further investigation (e.g., analyzing user behavior, IP addresses) and potential actions (e.g., pausing campaigns).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
LAG() with a partition and a timestamp diff check.
Use a window function to compare each event's timestamp with the previous event's timestamp for the same (user_id, event_type, show_id) combination. Flag events that are more than 5 seconds after the previous event (or are the first event) as the start of a new deduplication group, then select only those flagged events. This yields the earliest timestamp in each 5-second window.
Pro tip: Explicitly state your assumption about the 5-second window: whether it's relative to the previous event (gap-based) or a fixed tumbling window. In most deduplication contexts, a gap-based approach is expected, but clarifying shows you understand the nuance and avoids ambiguity.
Use a window function to partition by (user_id, event_type, show_id) and order by timestamp ascending. This groups related events and establishes chronological order.
Calculate the difference between the current event's timestamp and the previous event's timestamp within each partition, using LAG or a similar function.
Mark an event as a 'keeper' if it is the first event in the partition (previous timestamp is NULL) or if the time difference exceeds 5 seconds. This identifies the earliest event in each 5-second window.
Select only the rows where the flag is true, producing the deduplicated events with the earliest timestamp per window.
Encapsulate the logic in a common table expression (CTE) named deduplicated_events for clarity and reusability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The bonus question and it genuinely stumped me for a bit.
Start by clarifying the data model and definitions: watch_start events, unique viewers, and the 28-day rolling window. Then outline a SQL-based solution using window functions to compute daily unique viewers per show, followed by a rolling sum over the 28-day window. Finally, rank shows on the target date and apply tie-breaking logic.
Pro tip: Mention that you would validate the rolling window logic with a small test case and consider performance implications of large datasets, such as using partitioning and indexing. Also, proactively discuss how to handle edge cases like shows with no views on certain days.
Confirm what constitutes a unique viewer (e.g., distinct user_id per day per show) and the exact rolling window (28 days including current day). Also clarify the target date and tie-breaking rule.
Write a query to aggregate watch_start events by date and show, counting distinct viewers. Ensure date range covers August and September 2025, plus 27 days prior for the rolling window.
Use a window function to sum daily unique viewers over the preceding 28 days for each show. Be careful to avoid double-counting viewers across days if the metric is truly unique over the window.
Filter to the target date, rank shows by rolling metric descending, and break ties by daily unique viewers descending. Return top 3.
Test with sample data, consider performance optimizations, and discuss alternative approaches (e.g., approximate distinct counts) if scale is an issue.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.