The ROW_NUMBER angle is where I fumbled a bit.
Start by writing two separate CTEs that use ROW_NUMBER() partitioned by the natural key and ordered by the timestamp descending, then filter to rn=1. After presenting the SQL, explain that deduping on natural keys like event_id is safer because it preserves the intended uniqueness constraint and avoids accidentally collapsing distinct events that share the same user and timestamp.
Pro tip: Mention that in production you'd also consider using QUALIFY (if supported) or a MERGE for incremental deduping, and that you'd validate the dedupe logic by checking for duplicate counts before and after.
Use ROW_NUMBER() OVER (PARTITION BY event_id ORDER BY ingested_at DESC) and filter to rn=1 to keep the latest ingested row per event_id.
Use ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY updated_at DESC) and filter to rn=1 to keep the most recently updated row per order_id.
Argue that event_id is the true unique identifier for an event, so deduping on it ensures we keep one record per actual event. Using (u_id, event_ts) assumes uniqueness that may not hold and can drop legitimate distinct events.
Acknowledge that natural keys may have duplicates due to ingestion issues, but that's exactly what deduping should fix. Also mention that if event_id is not unique, you need to investigate data quality.
Conclude that deduping on natural keys is safer because it aligns with the business definition of uniqueness and avoids unintended data loss.
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Pretty mechanical once the dedup CTEs are in place, but I forgot to filter out is_test = 1 users on my first pass and they caught it.
Start by aggregating events and orders per user for date D, then join these aggregates to a filtered user dimension that excludes test users. For orders, first determine the latest status per order, then filter to only paid, shipped, or completed statuses before aggregating counts and amounts. Finally, combine the metrics into a single row per user.
Pro tip: Explicitly handle users with no events or orders by using LEFT JOINs and COALESCE to avoid dropping them, and clarify that 'latest status' should be based on the most recent status update timestamp, not order creation time.
Select user_id from the deduped users CTE where is_test = false (or equivalent flag). This defines the population for the final output.
From the deduped events CTE, filter to date D, then compute first_event_ts (MIN event timestamp) and events_cnt (COUNT of events) per user.
From the deduped orders CTE, use a window function (e.g., ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY status_updated_at DESC)) to get the latest status per order.
Filter the latest-status orders to only those with status IN ('paid', 'shipped', 'completed'), then compute paid_orders_cnt (COUNT) and paid_orders_amt (SUM of amount) per user for date D.
LEFT JOIN the non-test users with the event and order aggregates on user_id, using COALESCE to replace NULLs with 0 for counts and amounts, and output one row per user.
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This was the part I actually felt okay about.
Start by explaining the late-arriving data problem and the need for incremental processing. Describe a rolling window approach that recomputes the last 3 days (D-2 to D) on each run date, then merges only the target partition (e.g., D-2) to avoid full table scans. Provide SQL using MERGE or INSERT OVERWRITE with partition pruning, and emphasize idempotency by ensuring the merge condition matches on the primary key and the operation is deterministic.
Pro tip: Mention that you would use a MERGE statement with a deterministic condition and that you would also consider using INSERT OVERWRITE for the specific partition if the data is immutable. Highlight that idempotency is achieved because rerunning the same logic on the same data produces the same result, and the merge only affects the target partition.
Clarify that records for date D can arrive up to 2 days late, so partitions for D-2, D-1, and D may change. The goal is to incrementally update only the affected partition (D-2) on each run date.
On each run date, recompute the last 3 days (D-2, D-1, D) from the source to capture late data, but only merge the target partition D-2 into the final table. This limits the write scope and maintains performance.
Use a CTE to recompute the rolling window, then apply a MERGE or INSERT OVERWRITE to update only the target partition. Ensure the merge condition uses a unique key and partition filter.
Show that rerunning the same job on the same run date produces the same result because the merge is deterministic and only affects the target partition. If using INSERT OVERWRITE, it replaces the partition entirely, ensuring idempotency.
Mention that MERGE is more flexible for updates/deletes but may be slower; INSERT OVERWRITE is faster for full partition replacement. Consider partitioning strategy and data volume.
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Five felt like a lot to rattle off under pressure.
Start by acknowledging that edge cases are inevitable in production ETL and that defensive design is key. Then, for each edge case, briefly describe the scenario and the specific technique you'd use to handle it, emphasizing idempotency, observability, and data quality. Finally, tie it back to Stripe's context of financial data accuracy and reliability.
Pro tip: Prioritize edge cases by business impact and likelihood, and mention how you'd monitor and alert on them. This shows you think beyond just handling them to ensuring they don't silently corrupt data.
List at least five edge cases, such as partial writes, schema drift, null timestamps, DST issues, and replayed backfills. Prioritize them based on potential impact on data integrity and business metrics.
For each edge case, explain the defensive technique you'd use, such as idempotent writes, schema validation, default timestamp handling, timezone-aware processing, and backfill versioning.
Highlight how techniques like idempotent writes and monitoring ensure data consistency and early detection of issues. Mention logging, metrics, and alerting for each edge case.
Relate the edge cases and techniques to Stripe's need for accurate financial reporting and real-time analytics, showing awareness of the domain.
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Validation queries are something I always underestimate in interviews.
First, clarify the schema and business logic: identify the latest status per order (e.g., using a window function on order_id ordered by updated_at), and understand the grain of daily_user_metrics. Then write two separate queries: one that aggregates orders_raw to match the fact table's grain and compares counts and sums, and another that checks for duplicate event_ids within the date D partition using GROUP BY and HAVING COUNT(*) > 1.
Pro tip: Always validate reconciliation queries against a known-good date first to ensure your logic is correct before applying to the target date; this prevents false alarms and builds trust with stakeholders.
Ask about the table structures, especially how to determine the latest status (e.g., timestamp column) and the grain of daily_user_metrics (e.g., per user per day). Confirm what 'date D' means for each table.
Use a CTE to get the latest status per order for date D, then aggregate counts and sums. Compare these aggregates to the daily_user_metrics fact table for the same date, highlighting any mismatches.
Query the partition for date D, group by event_id, and filter for groups with count greater than 1. This identifies any duplicate event_ids that should not exist.
Mention handling of NULLs, timezone differences, and late-arriving data. Also note that using window functions or EXISTS can be more efficient than self-joins for large datasets.
Describe what actions to take if discrepancies are found, such as investigating upstream data pipelines or backfilling missing data, and how to communicate findings to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.