Describe How You Use SQL in Data Science Work
Company: Airbnb
Role: Data Scientist
Category: Data Manipulation (SQL/Python)
Difficulty: medium
Interview Round: HR Screen
How do you use SQL in your day-to-day data-science work? Describe the kinds of problems you solve, the complexity of queries you can own, and the checks you perform before trusting a result. Include one concrete example tied to a decision.
### Constraints & Assumptions
- This is a recruiter screen, not a live SQL exercise.
- Use only examples and SQL features you have genuinely worked with.
- Keep proprietary table names, data volumes, and business results confidential.
### Clarifying Questions to Ask
- Does this role use SQL mainly for product analysis, experimentation, reporting, or feature development?
- Is the team looking for independent query ownership or collaboration with analytics engineering?
### What a Strong Answer Covers
- The analytical tasks SQL supports and the data grain at which you usually work.
- Relevant query patterns such as joins, conditional aggregation, window functions, and cohort logic.
- Validation of row counts, join cardinality, nulls, time windows, and metric definitions.
- One example in which the SQL output informed a product or business decision.
### Follow-up Questions
1. How do you diagnose a join that unexpectedly increases row counts?
2. When do you move work from SQL into Python?
3. How do you make an analysis reproducible for another analyst?
Quick Answer: Prepare to explain how you use SQL in day-to-day data science work, from defining analysis populations and joining data to building experiment or cohort metrics. Emphasize query depth, row-grain checks, join cardinality, null handling, reproducibility, and the decision informed by the result.