← Snowflake Interview Insights
This is the kind of question where you feel fine until you actually try to write it.
Start by clarifying the retention definition (e.g., active in week N after signup) and the data model (events table with user_id, event_timestamp, country). Then outline a query that computes each user's signup week, assigns a week index to each activity week, and uses window functions with PARTITION BY to count distinct retained users per cohort-week-country, finally calculating retention rate. Explain how you avoid double-counting by using COUNT(DISTINCT user_id) and handle late-arriving events by using event timestamps and possibly a lookback window or periodic refresh.
Pro tip: Mention that you would materialize the cohort assignments and use a scheduled query to handle late-arriving data, and that you'd validate the retention rates against a known benchmark to catch double-counting or missing data issues.
Clarify what constitutes retention (e.g., any activity in the week) and how cohorts are defined (e.g., by signup week). Ensure alignment with stakeholders on the metric.
For each user, determine their signup week (first activity week) and for each activity, compute the week index relative to signup. Use DATE_TRUNC and date differences.
Use COUNT(DISTINCT user_id) OVER (PARTITION BY signup_week, week_index, country) to count retained users per cohort-week-country, ensuring each user is counted once per week.
Compute retention rate as retained users / cohort size. For late-arriving events, use event timestamps and consider a lookback window or periodic recomputation to update historical cohorts.
Validate results by checking cohort sizes and retention curves. Discuss trade-offs between accuracy and latency, and how you'd handle edge cases like users with no activity after signup.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
LEFT JOIN from the cohort table to purchases is the obvious move, and I got that right.
Start by clarifying the cohort definition and the grain of the output (cohort week × country × week number). Then build the query in layers: first compute cohort sizes including all users, then aggregate revenue from purchasers, and finally left join and divide to get ARPU while handling zero revenue and nulls.
Pro tip: Explicitly state that you would use a LEFT JOIN from cohort members to revenue and COALESCE the revenue to zero, and mention that you'd validate the query by checking that total revenue matches a separate aggregation and that ARPU is zero for cohorts with no purchasers.
Confirm the cohort definition (e.g., users grouped by first activity week) and the output grain (cohort_week, country, week_number). Ensure that all users in the cohort are counted, regardless of purchases.
Create a CTE that assigns each user to a cohort and country, then aggregates to get the total number of users per cohort and country. This will be the denominator for ARPU.
Create a second CTE that sums purchase revenue per user per week, then joins to the cohort mapping to get revenue by cohort, country, and week number. Only users with purchases will appear here.
Left join the cohort sizes to the revenue aggregation on cohort_week, country, and week_number. Use COALESCE to replace null revenue with 0, then divide revenue by cohort size to get ARPU.
Check that total revenue matches a separate aggregation, and that ARPU is 0 for cohorts with no purchasers. Consider whether to use integer division or cast to float, and handle any timezone or date truncation issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The masking and NA vs zero distinction are easy wins if you remember them.
Start by clarifying the data model and metric definitions, then walk through the app architecture: data ingestion, transformation, and visualization layers. Emphasize how you handle timezone re-bucketing, missing data, and privacy thresholds, and discuss trade-offs between correctness and performance.
Pro tip: Mention that you would validate the timezone re-bucketing logic with unit tests on edge cases (e.g., events near week boundaries) to ensure no user duplication, and use Snowflake's timezone functions for consistency.
Ask about the event data schema, cohort definition, and metric formulas (retention, ARPU). Confirm that 'missing future weeks' means weeks after the current date for a cohort, and that masking cohorts below 50 users is for privacy.
Outline how to compute cohorts and weekly metrics in Snowflake, handling timezone conversion by adjusting event timestamps to the selected UTC offset before bucketing into weeks. Ensure each user is assigned to exactly one week per cohort period.
Use Streamlit widgets for country filter, chart toggle, and UTC offset selector. Generate a retention heatmap and ARPU line chart, applying NA for missing future weeks and masking cohorts with <50 users.
Discuss caching strategies, query optimization (e.g., pre-aggregation), and the impact of timezone re-bucketing on query complexity. Consider using Snowpark or SQL for transformations.
Describe how you would test the timezone logic, missing data handling, and masking. Suggest unit tests for edge cases and validation against known metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Negative week_index was the one I actually had a concrete answer for.
Start by framing data quality checks as essential for trustworthy analytics, then describe two specific checks: cohort size monotonicity and clock skew detection. For each, explain the check, its implementation, and how it prevents downstream errors.
Pro tip: Tie each check to a real-world impact, like avoiding incorrect retention metrics or negative time indices, to show business awareness. Mention that these checks should be automated and alert on failure to maintain data integrity at scale.
Briefly explain why data quality checks are critical in product analytics, especially for cohort-based metrics and time-series analysis.
Explain that cohort sizes should never increase over time; implement a check that compares cohort sizes across weeks and alerts if any later week has a larger size than an earlier week.
Explain that negative week indices indicate clock skew; implement a check that flags any negative values in week index calculations and logs the event for investigation.
Mention that these checks should be automated in the data pipeline, with alerts sent to the data team when violations occur, and possibly integrated with data quality frameworks like Great Expectations.
Discuss how these checks prevent incorrect metrics, such as inflated retention rates or negative time-based features, and outline steps to mitigate issues when detected.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.