Coinbase·Data Scientist·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Coinbase data scientist interview with a meaty SQL question built around a user transaction table. The scenario felt very on-brand for a crypto company where cross-region activity actually matters. One question but it had several moving parts, so it took up most of the session.

Questions Asked (1)

Given a user transaction table with adoption dates and transaction records across regions, compute the overall adoption rate and transaction rate for a date range, calculate time-to-first-transaction in days for each adopted user, and identify cross-region transactions where the transacted region differs from the user's first transaction region.

Product Analytics & MetricsData Modeling

Author's notes

This looked manageable at first glance and then I started writing it and realized I had three separate problems duct-taped together.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the definitions of adoption rate and transaction rate, and the date range. Then outline a step-by-step SQL or Python approach: compute adoption rate as the proportion of users who adopted within the range, transaction rate as the proportion of adopted users who transacted, time-to-first-transaction as the difference between first transaction date and adoption date, and cross-region transactions by comparing each transaction's region to the user's first transaction region.

Pro tip: Always confirm the grain of the data and handle edge cases like users with no transactions or multiple first transactions on the same day; also consider timezone and date truncation issues.

1. Clarify definitions and assumptions

Define adoption rate (e.g., users who adopted in the date range / total users in the date range) and transaction rate (e.g., users who transacted / users who adopted). Confirm the date range and how to handle users with no transactions.

2. Compute adoption and transaction rates

Filter users by adoption date within the range, count distinct adopted users, and count distinct users with at least one transaction in the range. Calculate rates as proportions.

3. Calculate time-to-first-transaction

For each adopted user, find the earliest transaction date (if any) and compute the difference in days from adoption date. Handle users with no transactions (e.g., null or exclude).

4. Identify cross-region transactions

Determine each user's first transaction region (based on earliest transaction). Then flag transactions where the region differs from that first region.

5. Validate and present results

Check for data quality issues (e.g., missing regions, duplicate transactions) and summarize findings clearly, possibly with visualizations or tables.

Key Points to Mention

Definition of adoption rate and transaction rate, and how they differ
Handling of users with no transactions (e.g., exclude from time-to-first-transaction)
Use of window functions (e.g., ROW_NUMBER, MIN) to find first transaction and first region
Date functions to compute day differences (e.g., DATEDIFF)
Edge cases: multiple transactions on the same day, timezone considerations, and data completeness
Importance of clarifying the date range and whether it applies to adoption date or transaction date

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