📊 Data & Analytics
These interviews test whether you can turn a vague business question into a specific analysis, whether your SQL is solid, and whether you can explain a finding to someone non-technical. Case-style questions are common even for junior roles.
Common interview questions
Not exact questions you'll be asked — but the shape of what gets asked, and how a strong answer is structured.
Walk me through how you'd investigate a sudden drop in metric X.
Structure beats speed here: check if it's a data/tracking issue first (easy to overlook), segment by time/platform/geography to isolate where the drop is concentrated, then form 2-3 hypotheses and say how you'd test each one.
Write a query to find the second-highest salary in a table.
Mention at least two approaches: a subquery with MAX() excluding the top value, or DENSE_RANK()/ROW_NUMBER() with a window function. Window functions are the more 'senior' answer — use them if comfortable, but a correct subquery is a fine fallback.
How do you handle missing data?
Don't jump straight to 'fill with the mean.' First ask why it's missing (random vs. systematic) — that changes the right approach: drop, impute, or treat 'missing' as its own meaningful category.
Explain a time your analysis changed a decision.
Pick a story with a concrete before/after, even from a class project or personal analysis if you lack work experience — the key is showing the chain from data to insight to action.
How would you explain a p-value to a non-technical manager?
Avoid the textbook definition. Something like: 'it's how likely we'd see a result this strong just by chance, if there was actually no real effect — low means the effect is probably real.' Simple and correct beats precise and confusing.
Your learning path
In order — each step builds on the last.
- 01
SQL joins and window functions cold
INNER/LEFT/RIGHT joins, GROUP BY with HAVING, and at least RANK()/ROW_NUMBER() — these cover the large majority of SQL screening questions.
- 02
One end-to-end analysis project
A single project where you pulled real data, cleaned it, found something non-obvious, and visualized it is worth more than five tutorial notebooks.
- 03
Practice explaining stats simply
Be able to explain p-value, correlation vs. causation, and sample size in one plain sentence each — interviewers often test for this specifically.
4-week study roadmap
A week-by-week plan, not just a topic list. Check items off as you go — your progress is saved on this device.
SQL fundamentals
One real analysis project
Statistics & case practice
Storytelling & mock interviews
Before you walk in: field checklist
The field-specific things worth double-checking you've covered.
Interview-day checklist
The same basics matter whatever role you're interviewing for.
Free resources to study from
Cold mail tips for this field
- —Reference a specific metric or public report from their company if one exists — shows real homework.
- —Attach or link one chart from your own analysis work, not just a resume.
- —Ask a genuine question about their data stack — it signals real interest over a template.
Ready to put this to use?
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