Analyze Returning Borrowers Across Two Days of Logs
Last updated: July 14, 2026
Quick Overview
This question asks the candidate to analyze and compare data on returning borrowers across two days of logs, using SQL or Python to manipulate and extract insights from the data.
Affirm
July 14, 202616
0
226 solved
This question asks the candidate to analyze and compare data on returning borrowers across two days of logs, using SQL or Python to manipulate and extract insights from the data.
In a technical screen for a software engineering role at Affirm, this question is designed to assess the candidate's proficiency in data manipulation and their ability to derive meaningful insights from data. The interviewer is evaluating the candidate's technical skills, familiarity with SQL or Python, and their analytical thinking in solving real-world data problems.
What the Interviewer Expects
- Demonstrates proficiency in SQL or Python for data manipulation.
- Identifies key metrics for analyzing returning borrowers.
- Constructs clear queries or scripts to extract relevant data.
- Interprets results accurately and provides insights.
- Discusses potential implications of the findings.
Key Topics to Cover
How to Approach This
- Clarify the schema and expected output format before writing queries.
- Use CTEs (WITH clauses) to break complex queries into readable steps.
- Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
- Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
- For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
- How would you handle missing data in your analysis?
- Can you explain how you would optimize your SQL queries?
- What other metrics might be useful to analyze alongside returning borrowers?
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Practice SQL ProblemsSample Answer
Approach
To analyze returning borrowers across two days of logs, I would first define what constitutes a returning borrower. Typically, this means a borrower who has taken out a loan on one day and then return...
Data Extraction
Using SQL, I would write a query to extract the necessary information. Here’s a sample SQL query:
SELECT borrower_id, COUNT(*) AS loan_count
FROM loans
WHERE loan_date IN ('2023-10-01', '2023-...
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