This felt like a warmup but it actually took me a second to think through.
Start by clarifying the requirements: the raw event table should be append-only and capture all subscription-related events with timestamps. Then design a schema that includes a unique event ID, user ID, event type, event timestamp, and relevant attributes (e.g., plan ID, status). Finally, explain how to derive current status (latest event per user) and historical state (using window functions or SCD Type 2).
Pro tip: Emphasize that the raw event table should be immutable and that derived tables (like current status or history) should be built on top, ensuring auditability and reprocessing capability.
Ask about event types (e.g., subscribe, upgrade, cancel), data volume, and latency requirements. Confirm that the raw table is append-only and that we need both current and historical views.
Propose columns: event_id (unique), user_id, event_type, event_timestamp, plan_id, status, and any other relevant attributes. Ensure it captures all state changes.
Explain how to get the latest event per user using a window function (e.g., ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_timestamp DESC)) and filter for the most recent record.
Describe using window functions to create intervals (valid_from, valid_to) for each state, or using a slowly changing dimension (SCD) Type 2 approach to track changes over time.
Mention partitioning by date, indexing on user_id and event_timestamp, and potential materialized views for performance. Also discuss handling late-arriving events and idempotency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The re-signup edge case is where people trip up and I almost did too.
Start by clarifying the schema and defining 'latest' (e.g., by event timestamp or ingestion time). Then use a window function like ROW_NUMBER() partitioned by user and ordered by time descending to pick the most recent subscription event per user, or use a correlated subquery/aggregation. Handle the re-subscription case by ensuring the ordering captures the full history, not just the first or last event type.
Pro tip: Mention that you'd validate the logic with edge cases like users with multiple cancellations and re-subscriptions, and consider performance implications (e.g., indexing on user_id and timestamp) for large datasets.
Ask about the table structure, event types (signup, cancel, renew), and how to determine 'latest' (timestamp, sequence ID). Confirm whether status is derived from the latest event or a separate status field.
Decide between window functions (ROW_NUMBER, RANK) or aggregation with MAX(timestamp) and a self-join. For Python, consider pandas groupby with idxmax or sort_values + drop_duplicates.
Implement the chosen method: e.g., SELECT user_id, status FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_time DESC) AS rn FROM subscriptions) WHERE rn = 1. In pandas: df.sort_values('event_time').groupby('user_id').tail(1).
Test with users who have multiple signups/cancellations, missing timestamps, or ties. Ensure the logic picks the correct latest event even if a user cancelled and then re-subscribed.
Discuss indexing, partitioning, or using incremental processing for large data. Validate results by comparing with a manual check on a sample.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Code review questions are sneaky because you're not writing anything, you're just talking, and it's easy to miss stuff.
Start by restating the snippet's purpose and then systematically evaluate it for correctness, robustness, and experiment validity. Prioritize improvements that address assignment consistency, logging, and edge cases, and explain how each change impacts the experiment's integrity and business metrics.
Pro tip: Frame your improvements in terms of trade-offs (e.g., simplicity vs. correctness) and mention how you would validate the changes with unit tests and A/B test monitoring. This shows you think like a data scientist who owns the experiment end-to-end.
Briefly summarize what the code does: assigns users to variants and delivers a free-trial offer. Identify the key components: randomization, assignment storage, and offer delivery.
Look for common pitfalls: non-deterministic assignment, lack of logging, missing edge cases (e.g., new users, repeat visits), and absence of safeguards against bias or errors.
Suggest at least three specific changes, such as using a deterministic hash for assignment, adding logging for exposure and conversion, and implementing fallback or error handling.
For each improvement, describe how it enhances experiment validity, user experience, or maintainability, and note any trade-offs (e.g., added complexity).
Recommend ways to test the changes (unit tests, simulation) and monitor the experiment (dashboards, alerts) to ensure ongoing correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.