Capital One·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Capital One data scientist round, basically one big SQL problem that took the whole session. The question was about reconstructing lost revenue from a database outage and it was way more involved than I expected going in.

Questions Asked (1)

A database outage prevented premium membership registrations from being recorded for a three-month window. Given tables for intended enrollments, actual registrations, and the outage dates, write a single PostgreSQL query that returns total revenue lost and a breakdown by how many months each user missed, using calendar month boundaries and handling NULLs for users who never registered.

Data ModelingProduct Analytics & MetricsAlgorithms & Data Structures

Author's notes

This wrecked me a little.

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AI HintsAI Generated

Suggested Approach

First, clarify the table schemas and the exact definition of 'months missed' (e.g., full calendar months between intended enrollment and actual registration, or months within the outage window). Then, construct a query that joins intended enrollments with actual registrations and the outage period, calculates the number of missed months using date functions, and aggregates revenue lost by grouping on that count. Use COALESCE to handle NULL registrations and ensure calendar month boundaries are respected.

Pro tip: Explicitly state your assumptions about the data model and edge cases (e.g., users who registered before the outage, partial months) before writing the query—this shows you think like a data scientist who validates requirements, not just a coder.

1. Clarify requirements and schema

Ask about the table structures, the definition of 'months missed' (e.g., full calendar months between intended and actual registration, or months within the outage), and how revenue per month is calculated. Confirm whether the outage dates are inclusive and how to handle users who registered outside the outage.

2. Identify the relevant data subset

Filter intended enrollments to those that occurred during or before the outage and whose actual registration is either NULL or after the outage. This ensures you only consider users affected by the outage.

3. Calculate months missed per user

For each affected user, compute the number of calendar months they missed. Use date functions like DATE_TRUNC and AGE, and handle NULL registrations by treating them as the end of the outage or current date, depending on business rules.

4. Aggregate revenue lost and breakdown

Join with a revenue-per-month assumption (or derive from intended enrollments) to calculate total revenue lost per user, then group by the number of months missed to get the breakdown. Use COALESCE to replace NULLs with 0 where appropriate.

5. Validate and present results

Sanity-check the output: ensure total revenue lost equals the sum of the breakdown, and that month counts are non-negative integers. Explain how the query handles edge cases like users who never registered.

Key Points to Mention

Use of COALESCE or LEFT JOIN to handle NULL registrations for users who never registered.
Calendar month boundaries: use DATE_TRUNC('month', ...) and consider partial months (e.g., if a user missed 2.5 months, decide whether to round up or down based on business rules).
Definition of 'months missed': whether it's the difference between intended enrollment date and actual registration date, or the number of full months within the outage window.
Revenue calculation: how to derive revenue lost per month (e.g., from a separate table or a constant premium fee).
Filtering: only include users whose intended enrollment fell within the outage period or who were affected by the outage.
Aggregation: use GROUP BY on the months missed count and SUM revenue lost, ensuring the breakdown sums to the total.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.