🔑 Key Takeaway
- Technical interview questions filter out resume fluff and reveal who can truly deliver.
- Spread questions across rounds to avoid burnout and get clearer signal. Don’t just test syntax—test thinking, context, and follow-up.
- Use targeted prompts depending on your stack and business needs. Testing for the wrong tool is a costly mistake.
- Ask for structure, prioritization, and metric-thinking. The best candidates will think like detectives.
- Communication clarity, curiosity, and contextual awareness separate the good from the irreplaceable.
- Pre-vetted analysts already fluent in your tools mean less onboarding, fewer mis-hires, and faster results.
It’s 15 minutes into the interview.
You’ve got a sharp-looking resume on screen. SQL? Check. Python? Check. Dashboard tools? All accounted for.
But as soon as you ask a technical question, something as simple as “How would you write a query to group user actions by session?”, things start to fall apart. They hesitate. They ramble. And suddenly, you’re not so sure anymore.
This happens all the time.
Data analysts often look great on paper. But the real test is whether they can translate numbers into answers and do it using the tools your team actually runs on.
And if you’re hiring without a technical background yourself, it’s even harder to tell who’s ready and who’s rehearsed.
This guide solves that.
It gives you a vetted list of technical questions for data analysts—covering SQL, Python, Excel, and analytics thinking so you can screen smarter, make faster decisions, and avoid expensive hiring mistakes.
Because a great analyst isn’t the one with the longest resume. It’s the one who asks the right follow-ups, writes clean logic, and helps your team move.
Let’s get into it.
This post is part of our larger series on hiring data talent in 2025. Explore the full guide here on how to hire data analysts.
Why Technical Questions Matter in Data Hiring
A resume can look perfect and still tell you nothing.
You’ll see all the right tools listed: SQL, Python, Excel, Tableau, Looker. But unless you ask the right technical questions for data analysts, you’ll have no idea if they can actually use those tools to solve real problems.
“The first hire looked great. Three weeks in, we realized they couldn’t write a JOIN.”
That’s both frustrating and expensive. Time wasted on onboarding, cleaning up incorrect dashboards, and restarting your hiring process all over again.
Here’s why getting your interview flow right matters:
1. Avoid the Resume Trap
Anyone can say they “know SQL.” But ask them to write a query that joins multiple tables and filters out NULL values, and suddenly you’re in the silence zone.
That’s the resume trap: confidence on paper, but no traction in the real world. Technical questions cut through that fast.
Here’s a complete guide of all you need to know about hiring a data analyst.
2. It’s Not About Memorisation. It’s About How They Think.
You’re not looking for textbook definitions. You’re looking for signal:
- Can they break down a vague problem into clear steps?
- Do they ask follow-up questions before jumping in?
- Can they explain their logic in plain English?
That’s what separates someone who knows the tools from someone who knows how to solve business problems.
3. Ask Based on What You Actually Need
Not every data analyst role is the same.
- Need clean reports? Ask about Excel and dashboards.
- Running A/B tests? Ask about experimentation frameworks.
- Automating reports or cleaning messy data? Focus on Python and SQL.
Tailoring your questions to your workflow is how you avoid bad-fit hires.
4. Structured Interviews Save You From Expensive Mistakes
Having a clear system—one that covers core technical areas, aligns with your tools, and tests real-world scenarios—will save:
- Hours of back-and-forth with candidates
- Weeks of re-training post-hire
- Thousands in misaligned salaries and sunk onboarding costs
This guide gives you the structure. You just have to use it.
SQL Technical Interview Questions for Data Analysts
If there’s one area where resumes over promise, it’s SQL.
Many candidates list it as a strength—but only a few can write queries that answer real business questions. That’s why asking the right SQL technical interview questions for data analysts is non-negotiable.
SQL isn’t just a query language. It’s how analysts retrieve insight, find anomalies, and slice performance data for decision-makers.
To separate skilled candidates from surface-level users, you want to move from basic syntax to applied scenarios.
A. ✅ Start with the Basics
Begin with simple prompts to assess foundational knowledge:
🚩 Red flag: Candidates who struggle here often rely on GUI tools (like Looker or Tableau) without true SQL fluency.
B. 🧠 Move Into Practical Scenarios
After warming up, test whether they can apply SQL to real-world use cases, especially if your role leans on product, ops, or growth analytics.
🚩 Red flag: Long pauses, excessive guesswork, or skipping clarification questions when the prompt is open-ended.
📝 Full Example Prompt (With Follow-Up)
Prompt:
You have two tables:
- users(user_id, signup_date)
- orders(order_id, user_id, order_date, total_amount)
Write a query to return, for each user, the number of orders they placed and their total spend.
Follow-Up Questions:
- How would you modify this to show only users who signed up last month?
- What if you wanted to include users who made zero purchases?
How would you turn this into a cohort analysis by signup month?
These questions test not just syntax but how the analyst thinks through problems, builds iteratively, and explores business outcomes.
Pro tip: If your analyst will be working with marketing, ops, or customer data, SQL is non-negotiable. Prioritize candidates who speak it fluently and can apply it without hand-holding.
Make sure you’re tailoring questions to match what you actually need, whether it’s reporting, experimentation, or automation.
Python Technical Interview Questions for Data Analysts
Python isn’t a must-have for every data role—but if you’re hiring for automation, large-scale reporting, or advanced analysis, it’s where the real difference shows.
Many junior analysts know Excel or SQL. But when you’re building ETL pipelines, cleaning messy data, or automating recurring tasks, Python is what separates a good hire from a great one.
If your analyst will be working in marketing ops, customer analytics, or data engineering workflows, these Python technical interview questions for data analysts will help you vet skill and mindset.
See full guide on the different types of data analysts and when to hire one
When Should You Test Python?
If your analyst will handle:
- Complex data cleaning (e.g., merging messy CRM datasets)
- Custom reporting scripts or scheduled jobs
- Lightweight forecasting or clustering
- ETL processes using tools like Airflow or dbt
Then Python is worth testing especially with libraries like Pandas, NumPy, and Matplotlib/Seaborn.
Skip the trivia about syntax. Focus on practical, problem-solving questions that mimic your workflows.
5 Python Questions That Reveal Real Skill
🚩 Red flag: Candidates who resort to Googling for basic data manipulation or who can’t articulate the steps they’d take—especially when under vague or flexible prompts.
🔎 Sample Prompt (Scoring Insight Included)
Prompt:
“You’re given a CSV with thousands of user actions—page views, purchases, signups—timestamped by user_id. Write Python code to calculate session duration per user and return the top 5 longest sessions.”
What this tests:
- Ability to group data logically
- Time delta calculation
- Clean iteration + Pandas application
Scoring Insight:
Great candidates will use groupby, apply, and timedelta concepts. They’ll also clarify assumptions: “What defines a session? Inactivity threshold?”
Don’t Just Ask Questions, Replicate Real Work
Technical questions aren’t about tricking candidates. They’re about previewing what it’s like to work with them, how they ask questions, solve problems, and write maintainable code.
When you hire pre-vetted data analysts who’ve already used tools like Pandas, NumPy, and reporting automation platforms, you skip the guesswork and the ramp-up.
Up next: let’s look at Excel technical interview questions for data analysts, especially for roles in reporting, finance, and customer support teams.
7 Excel Technical Interview Questions for Data Analysts
Not every data analyst needs to code but every analyst should know how to think in Excel.
In roles where dashboards, executive reporting, and client-facing deliverables matter, Excel is still the lingua franca.
Whether it’s building churn calculators, tracking sales by region, or creating clean, shareable visuals for the C-suite, Excel skills often make or break the hire.
That’s why these Excel technical interview questions for data analysts go beyond formulas. They test how candidates approach real problems—with logic, structure, and speed.
When to Test Excel
Excel is critical when your analyst will be:
- Working with non-technical stakeholders (sales, ops, C-level)
- Creating internal reporting tools
- Supporting customer success, finance, or marketing teams
- Visualising quick wins before formal dashboards are built
✅ Excel Questions That Actually Reveal Skill
Scenario Prompt: Build-Then-Explain
- Prompt:
“Create a simple Excel model that tracks monthly churn. Inputs: total users, churned users, average revenue per user. Output: churn rate, churned revenue, and a basic trend chart.”
- Scoring Insight:
Look for structure: clearly labeled cells, logical flow, no hardcoding. Bonus points for using named ranges or simple automation (e.g., auto-updating charts).
Someone can say they’re “good at Excel.” But when they struggle to build a PivotTable or create a formula that blends logic with business understanding you’ll wish you’d tested first.
Hiring pre-vetted analysts who already build reports, dashboards, and decision tools in Excel means you avoid the awkward realization that your “analyst” still Googles how to use SUMIFS.
Case & Analytics Thinking Questions
Tool fluency gets you halfway. Analytical thinking is what separates a competent data analyst from a game-changer.
That’s why case-style prompts; open-ended, business-first questions—are essential in any technical interview. They show how a candidate frames problems, asks smart follow-ups, and zeroes in on what matters.
These aren’t about the “right” answer. They’re about clarity, logic, and business sense.
What Do These Questions Reveal?
Case and thinking questions test:
- How an analyst prioritizes and scopes a problem
- Whether they understand business context
- How they approach ambiguous data
- Whether they look at just numbers—or insights
You’re not looking for buzzwords. You’re looking for structured thought, useful assumptions, and proactive curiosity.
Sample Case Prompts (By Function)
✅ Pro Tip: Structure Wins
The best answers follow a version of this thinking framework:
- Clarify the goal (What’s the business trying to solve?)
- Define success metrics (Which KPIs matter here?)
- Segment the problem (By user, product, geography, time?)
- List data sources (CRM, product logs, NPS surveys?)
Propose next steps (What would you analyze first?)
Hiring someone who can write a JOIN is useful. Hiring someone who can connect the dots between metrics? That’s where real ROI lives.
If you’d rather skip the guesswork, pre-vetted analysts from Talent Hackers are already trained to think this way.
Wrap-Up: How to Build a Reliable Interview Flow
A great interview goes beyond testing knowledge. It reveals how a data analyst thinks, communicates, and responds to ambiguity, which matters more than a perfect SELECT query.
But here’s where many hiring managers go wrong: they treat interviews like final exams. Too many questions, too much pressure, and no connection to real day-to-day tools.
Let’s fix that.
1. Spread Questions Across Rounds
Don’t cram every technical scenario into one session. Break your process into stages:
- Round 1: Light intro, 1–2 tool-based questions (e.g., SQL)
- Round 2: Deeper dive into analytics thinking and communication
- Final Round: Culture fit, stakeholder simulation, live problem walkthrough
This format protects your time and the candidate’s, while giving you better signal at every step.
2. Test Real Tools, Not Just Concepts
Use at least one prompt involving the actual stack your analyst will use—Excel, Tableau, Pandas, Metabase, etc. You’ll spot red flags early if a candidate can’t navigate your environment.
Pro Tip: If you rely on VLOOKUP and dashboards, test for that. Not machine learning trivia.
3. Look for Thinking, Not Just Right Answers
Your goal isn’t to find a walking textbook—it’s to hire someone who:
- Asks clarifying questions
- Structures their approach
- Explains decisions in plain English
- Doesn’t panic when faced with ambiguity
The best candidates will narrate their process out loud, which shows confidence and collaboration skills.
4. Build a Simple Interview Scorecard
What matters most to your team? SQL logic? Communication? Problem framing?
Create a basic scorecard (even in Excel) to rate each area from 1 to 5. This keeps feedback structured and avoids “gut feeling” bias when comparing candidates.
Check out this blog article to see how you can even turn these into a scored exercise for clearer comparison.
💡 Or Skip the Guesswork Entirely…
At Talent Hackers, every data analyst is pre-vetted across technical, soft skill, and communication benchmarks.
That means less interviewing, less rehiring—and more getting stuff done.
👉 Get your interview kit or explore top offshore data analysts
Frequently Answered Questions
1. What are good technical questions to ask a data analyst?
Great questions test SQL logic, Python fluency, Excel use, and real-world analytics thinking. Ask open-ended prompts that mirror your business needs—like identifying churn drivers or cleaning a messy dataset.
2. What SQL skills should a data analyst have?
At minimum, they should be fluent in SELECT, JOIN, GROUP BY, and subqueries. Strong analysts also know how to build product funnels, analyze cohorts, and optimize for query efficiency.
3. Should I test Python in a data analyst interview?
Yes—if your role involves data wrangling, automation, or modeling. Focus on Pandas, NumPy, and script-writing, not language trivia.
4. How do I assess Excel skills in a data analyst?
Use scenario-based tasks like building a churn calculator, applying PivotTables, or using nested formulas. Focus on how they structure the sheet, not just formula recall.
5. What makes a data analyst good at problem solving?
Look for structured thinking, the ability to define metrics, prioritization under pressure, and curiosity-driven follow-up questions. Analysts who think like detectives bring the most business value.
