Verified: Celine Liu, Uber's former Global Analytics Lead, wrote this guide. Celine has conducted 100+ interviews across analytics, operations, and strategic roles.
Data analyst interviews aren’t just about knowing how to write a SQL query or draw a bar chart.
They test your ability to transform messy, ambiguous data into clear, actionable business decisions.
Top tech companies like Meta, Amazon, and Google consistently look for analysts who can:
Think critically about business problems,
Communicate clearly with cross-functional teams,
Use tools like SQL, Excel, dashboards, and statistics to uncover insights.
You’re not being hired to build machine learning models but to drive real impact. This means showing that you can move fast with scrappy tools, reason about the business, and explain what the numbers mean, not just what they are.
Let’s walk through the stages, skills, and strategies top-performing candidates use to succeed.
Data Analyst Interview Process
Most top-tier tech companies follow a structured loop when hiring data analysts.
Each stage is designed to test a different dimension of your skills, from technical competency to business acumen and communication.
Recruiter Screen
This is typically a casual introductory call, but don’t let your guard down—technical questions can sneak in.
Expect to be asked about your SQL proficiency or even be tested with a quick concept question, like the difference between RANK() and DENSE_RANK().
Sample questions:
Tell me about yourself.
Why do you want to work at this company?
What tools have you used (SQL, Excel, Python, BI tools)?
What are your long-term career goals?
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Tip: Prepare to talk about your technical toolkit just in case.
Hiring Manager Screen
This 30-minute call is highly variable. Some managers will focus on soft skills and collaboration style, while others will jump into a live business or SQL problem.
You'll get either a live case interview or a take-home. In both formats, the goal is to evaluate your business thinking, prioritization, and communication.
Example prompts:
“Sales dropped 25%. How would you investigate?”
“What metrics would you use to understand churn?”
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Tip: It’s not just about answering the question. It’s about structuring your analysis, aligning with business goals, and showing your stakeholder perspective.
Take-Home Case Study
The take-home case study is often the deciding round at companies like Uber, Shopify, and TikTok. This format gives you 4 to 7 days to analyze a real or simulated dataset and present insights to a panel.
You’ll be evaluated on:
Analytical rigor
Structured thinking
Data-driven recommendations
Communication and visual storytelling
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Tip: Prepare a presentation with clear takeaways, anticipate follow-up questions, and make your slides business-friendly.
Behavioral & Cultural Fit
Behavioral rounds test your ability to navigate ambiguity, collaborate with others, and embody the company’s values.
Expect questions about conflict resolution, data failures, team dynamics, and influencing without authority.
Example prompts:
“Describe a time when you faced conflicting stakeholder requests.”
“Tell me about a data project that went wrong. What did you learn?”
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Tip: Use the STAR or PACE framework to craft concise, impactful stories showing soft skills and business awareness.
Technical Skills
SQL
SQL appears in nearly every round. Recruiters and take-home case studies constantly evaluate your ability to write efficient, interpretable queries.
Common test formats:
Asynchronous browser-based challenges
Live problem-solving in interviews
Take-home exploration using SQL
Conceptual questions ("What's the difference between RANK and DENSE_RANK?")
Key topics:
Joins, subqueries, CTEs
Window functions (RANK, LEAD, LAG, etc.)
Performance optimization and readability
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Tip: Always explain your logic, constraints, and trade-offs.
Excel & Google Sheets
Excel and Google Sheets are still common tools in analyst interviews, especially for ad hoc tasks and take-homes. Employers look for clean layouts, formula clarity, and auditable structure.
Must-know:
Pivot tables
VLOOKUP, XLOOKUP, INDEX-MATCH
Filtering, sorting, and conditional logic
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Tip: Never hard-code outputs. Link everything through formulas.
These are some core data manipulation skills:
Skill
What it tests
Common mistake
Example
Sorting & Filtering
Can you isolate top performers or narrow data by condition?
Sorting without preserving data integrity e.g., breaking row alignment when sorting
“Sort revenue DESC to find top 5 customers by region.”
Removing Duplicates
Can you clean data while preserving the right info?
Removing rows without checking if all fields are truly duplicates
“Remove duplicate orders by order_id, but keep the latest date.”
Handling Missing Values
Can you handle NULLs and blanks in calculations and visuals?
Allowing blanks to skew analysis or misinterpreting missing as zero
=IF(ISBLANK(B2), 0, B2) to prevent blank impact
Text Manipulation
Can you clean and extract info from messy text?
Not normalizing case or trimming invisible characters that break joins or filters
Extract domain from email using RIGHT + FIND
Data Validation
Can you prevent bad data from entering your sheet?
Over-relying on visual checks; inconsistent inputs like "CA", "ca", or "Calif"
Use drop-down lists to enforce region standardization
You should know how to think about solving problems with data:
Define the problem
Select the right data sources
Clean and structure your dataset
Analyze with segmentations, cohorts, and trends
Interpret findings using the AIM approach (Analysis → Insight → Meaningful Action)
Communicate results with charts, docs, or dashboards
Statistics & Experimentation
For many analyst roles, especially those touching product or marketing, you're expected to be comfortable designing and interpreting experiments even if there's no formal “statistics round.”
Where it shows up:
A/B test case questions (e.g., "Was this experiment successful?")
Take-home scenarios involving test results
Conceptual questions about p-values, confidence intervals, or variance
Metrics interpretation: "CTR went up, but conversion didn’t — why?"
Hiring teams use take-home case studies to assess:
Your data analysis skills
Your ability to generate data-backed recommendations
How well you communicate technical ideas to a cross-functional audience
Your awareness of business impact and trade-offs
6-Step Framework
Deconstruct the prompt: Don’t skim. Define the core problem and determine what “success” looks like. Understand the KPIs or strategic goal you're solving for.
Strategize your approach: Pick a framework (e.g., funnel analysis, segmentation). Choose relevant metrics and hypotheses to explore.
Analyze the data: Clean, explore, and test hypotheses using SQL, Excel, or Python.
Synthesize insights and make recommendations: Don’t just show charts. Deliver crisp, data-backed conclusions that speak to business goals.
Communicate visually and clearly: Build a clean slide deck or doc. Use storytelling to explain the “so what” of your findings.
Refine and prepare for Q&A: Practice presenting, anticipate stakeholder questions, and stress-test your assumptions.
Types of Prompts
Defined: “Analyze this dataset and tell us the top 3 causes of churn.”
Open-ended: “How would you improve engagement on our platform?”
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Tip: When a dataset isn’t provided, demonstrate your resourcefulness by sourcing external benchmarks or constructing a reasoned framework.
Evaluation Rubric
Your presentation should demonstrate:
Structured thinking with clear assumptions and scope
Analytical rigor with clean formulas and visible logic
Business relevance of your insights
Visual storytelling with impactful charts and callouts
Iteration readiness — anticipating questions and showing humility
Peer mocks: Simulate interviews with a friend or Exponent community member
Coach reviews: For high-stakes roles, work with a coach to get targeted feedback
Watch examples: Review top-scoring responses and critique their approach
Seeking Feedback
Did I structure my response clearly?
Did I explain assumptions and trade-offs?
Did I tie the answer back to business outcomes?
Did I pause and clarify before jumping in?
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Tip: Record yourself. You’ll spot filler words, unclear transitions, or over-reliance on jargon. And you’ll notice patterns you can clean up before the real interview.
Interview Prep Plan
The best candidates don’t just prepare hard. They prepare strategically.
Personalize Your Prep
Read the job description carefully. Focus your time where the role is most important: SQL, dashboards, or business cases. Don’t study everything equally.
Start with your weakest area. If SQL scares you, prioritize that first. If you’re great at Python but struggle to structure business cases, dive into mock cases and watch sample interviews.
Layer your practice.
Week 1–2: Review frameworks, watch expert mocks, and complete drills.
Week 3: Practice full mock interviews (live or recorded).
Week 4: Focus on timing, polish, and interview readiness.
We recommend:
One focused interview question per day.
Track your answers and self-review or get peer feedback.
Use AI or a friend as a mock interviewer to simulate pressure.
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Tip: Don’t just look up answers. Practice responding out loud. Structure your thinking. Clarify assumptions. Interpret results like a business partner.
Interview Readiness Checklist
Use this quick checklist to make sure you’re covering all bases:
Area
Ready?
SQL Practice (joins, CTEs)
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Excel & Google Sheets
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Dashboarding & KPIs
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A/B Test & Statistics
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Behavioral Story Bank
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Take-Home Case Framework
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3+ Mock Interviews Completed
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Remember, this guide offers a high-level overview designed to get you started. It’s not an exhaustive resource. It's a starting point.
Check out our other resources to help you on your journey:
Ace your data analytics interviews—whether you're targeting product, marketing, or business analyst roles. Tackle real take-home case studies, sharpen your technical and dashboarding skills, and get strategies from interviewers at top tech companies and startups.
Learn how to succeed in behavioral interviews by mastering what interviewers look for and how to structure your answers. You’ll explore proven frameworks like STAR and the Triangle Method, build your story bank, and watch expert examples of strong responses to real questions.