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Robinhood·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Robinhood Data Scientist interview with a SQL question centered on membership period filtering. Pretty straightforward if you're comfortable with date range joins, but the 'Gold' tier filter is a small wrinkle that could trip you up if you're moving fast.

Questions Asked (1)

Q1

Given a customer profile table with membership type and date range, and a transactions table, write a SQL query that returns all transactions made during a customer's active Gold membership period.

Data ModelingProduct Analytics & Metrics
Author's notes

The join on customer_id is obvious, but I almost forgot to filter membership_type to 'Gold' before worrying about the date range.

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Suggested Approach

Start by clarifying the table schemas and the definition of 'active Gold membership period' (e.g., start_date to end_date). Then write a SQL query that joins the transactions table to the customer profile table on customer_id, filtering for membership_type = 'Gold' and transaction_date between the membership start and end dates.

Pro tip: Use a half-open interval [start_date, end_date) to avoid double-counting transactions on the end date, and mention that you'd validate edge cases like overlapping membership periods or null end dates.

1. Clarify requirements and schema

Ask about the exact columns in both tables, the definition of 'active Gold membership period', and how to handle edge cases like overlapping periods or null end dates.

2. Identify the join key and filter conditions

Determine the common key (e.g., customer_id) and the conditions: membership_type = 'Gold' and transaction_date between start_date and end_date.

3. Write the SQL query

Construct a query that selects all transactions from the transactions table, joining to the customer profile table and applying the filters.

4. Consider performance and edge cases

Discuss indexing on join and date columns, and how to handle multiple Gold periods per customer (e.g., using EXISTS or window functions).

5. Validate and explain

Walk through the query logic, test with sample data, and explain how it meets the business requirement.

Key Points to Mention

  • Use of INNER JOIN to combine transactions and customer profile tables on customer_id.
  • Filtering for membership_type = 'Gold' and transaction_date BETWEEN start_date AND end_date.
  • Handling of edge cases: transactions on the end date (use < end_date or <= depending on business rules), null end dates (e.g., COALESCE with CURRENT_DATE), and multiple membership periods.
  • Performance considerations: indexing on customer_id, membership_type, and transaction_date.
  • Alternative approaches: using EXISTS subquery or window functions for overlapping periods.
  • Importance of clarifying the definition of 'active Gold membership period' with the interviewer.

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