Data analyst interviews are unusually broad: they test technical skill (SQL, statistics, Excel), tool fluency (visualization, sometimes Python or R), and — crucially — your ability to turn data into a clear story a non-technical stakeholder can act on. That communication layer is what separates analysts who get hired. Here are the data analyst interview questions that actually get asked.
SQL (the core technical filter)
- Joins, GROUP BY, HAVING, and window functions (our SQL guide).
- Find the second-highest value; rank within groups.
- Write a query to find month-over-month growth.
- How do you handle NULLs and duplicates?
Statistics & analytics
- Mean vs median vs mode — and when each is misleading.
- What is standard deviation and variance?
- Correlation vs causation — give an example.
- What is a p-value and statistical significance (at a high level)?
- How do you detect and handle outliers?
Excel & visualization
- VLOOKUP/XLOOKUP, pivot tables, INDEX-MATCH.
- Which chart for which question (and which charts to avoid).
- How do you design a dashboard a non-technical exec can read in 10 seconds?
Case studies (where it's won)
Expect an open analytical prompt: "Our daily active users dropped 15% last week — how would you investigate?" They want a structured approach: clarify the metric, segment the data, form hypotheses, identify what you'd query, and communicate findings clearly. This is as much a communication test as an analytical one.
How to prepare
The case-study and "explain this insight" rounds are spoken and structured — exactly the part that trips up technically strong candidates. Practise walking through an analysis out loud. Greenroom runs spoken interviews that push on your reasoning and communication with feedback. Pair it with our SQL and explain-your-project guides.
Frequently asked questions
What questions are asked in a data analyst interview?
Data analyst interviews test SQL (joins, GROUP BY, window functions, growth queries, handling NULLs and duplicates), statistics (mean vs median, standard deviation, correlation vs causation, p-values, outliers), Excel and visualization (pivot tables, lookups, choosing the right chart, dashboard design), and open analytical case studies where you investigate a metric change and communicate findings.
How important is SQL for a data analyst interview?
SQL is the core technical filter for almost every data analyst role — you'll be asked to write joins, aggregations, window functions and queries like second-highest value, ranking within groups, and month-over-month growth. Strong, fluent SQL is often the single biggest determinant of whether you pass the technical screen.
What is a data analyst case study interview?
A case study gives you an open analytical prompt — for example 'daily active users dropped 15% last week, how would you investigate?' Interviewers want a structured approach: clarify the metric, segment the data, form hypotheses, state what you'd query, and communicate findings clearly. It tests analytical reasoning and communication as much as technical skill.
How should I prepare for a data analyst interview?
Build fluent SQL and solid applied statistics, practise Excel and visualization, and rehearse open case studies. Crucially, practise explaining insights simply out loud, since the case-study and 'explain this finding' rounds are spoken and trip up technically strong candidates. A voice-based mock interview that pushes on your reasoning and communication helps a lot.