Data Analyst Interview Questions

Data Analyst Interview Questions

Practice 29 real Data Analyst interview questions for 2026 — Data Analyst interview questions drawn from actual interviews with detailed solutions to help your interview preparation. These questions focus on the core signals hiring teams evaluate: technical fluency (SQL, basic Python/pandas, data modeling), product and business sense (metrics, funnels, A/B interpretation), and communication skills that turn analysis into action. Expect a mix of timed SQL exercises, analytics case prompts, and behavioral stories that probe impact and stakeholder influence. Across companies currently hiring this role heavily — Capital One, The Home Depot, eBay, and ByteDance — three recurring technical themes show up most often: advanced SQL at scale (complex joins, window functions, performance tradeoffs), product/marketplace metric analysis and causal inference (funnels, cohorts, A/B interpretation), and practical data modeling/ETL plus stakeholder-facing reporting (dbt/warehouse basics, dashboarding, and clear storytelling). Typical interview loops commonly run 2–5 weeks: recruiter screen, an early SQL or take-home assessment, one or two technical rounds (deep SQL/analytics case and sometimes data-model design for senior roles), followed by behavioral rounds. Prepare by practicing timed SQL problems, framing business-first analyses, and rehearsing concise STAR stories.

29 Questions 14 Companies07.08.2026
Showing 9 results
Role
Natoora logo
Natoora
Medium
Data Analyst

How adapt to Google Sheets?

You are interviewing for a pricing analyst role. The interviewer says that you do not have direct pricing experience, and that much of the team's day-...

Behavioral & Leadership
6
0
43 people solved
Jan 18, 2026
Natoora logo
Natoora
Medium
Data Analyst Locked

Design a 25,000-CSV ETL pipeline

This question evaluates data engineering and ETL pipeline design skills, specifically competencies in automation, schema validation, type standardizat...

Coding & Algorithms
7
0
59 people solved
Jan 18, 2026
Newyorktimes logo
Newyorktimes
Easy
Data Analyst

Write SQL for content-view analytics

Context You work with page-view event logs and need to compute several engagement/usage summaries. Assume a single table: page_views | column | type |...

Data Manipulation (SQL/Python)
2
0
30 people solved
Jan 17, 2026
Newyorktimes logo
Newyorktimes
Medium
Data Analyst

Write SQL for content view metrics by device

Write SQL for content view metrics by device You are a reporting analyst for a news subscription product. You have a fact table of page/content view e...

Data Manipulation (SQL/Python)
4
0
70 people solved
Mar 1, 2022
eBay logo
eBay
Medium
Data AnalystIntern Locked

Explain Joins and Write Coupon SQL

This question evaluates proficiency in SQL fundamentals—logical execution order of query clauses, join semantics (INNER/LEFT/RIGHT), timezone-aware ti...

Data Manipulation (SQL/Python)
1
0
12 people solved
May 28, 2026
Bytedance logo
Bytedance
Hard
Data Analyst

Find high-value crypto users and top CTR

You are given three tables. Assume all timestamps are stored in UTC. - users(user_id BIGINT PRIMARY KEY, create_date TIMESTAMP): one row per user acco...

Data Manipulation (SQL/Python)
12
0
105 people solved
Feb 23, 2026
Capital One logo
Capital One
Medium
Data Analyst

Find Lowest Prices for Highly Rated Categories

You are given two tables containing product catalog information and purchase records. Tables products | Column | Type | Description | |---|---:|---| |...

Data Manipulation (SQL/Python)
1
0
17 people solved
Feb 15, 2026
Pinterest logo
Pinterest
Medium
Data Analyst

Compute percent of first-cancelled users who never rebook

You are interviewing for a health-tech product analytics role. Assume the following table contains one row per appointment with its final status. Tabl...

Data Manipulation (SQL/Python)
7
0
48 people solved
Nov 22, 2025
Pinterest logo
Pinterest
Easy
Data Analyst

Compute percent of first-cancel users who never return

You’re analyzing appointment behavior for a scheduling product. Table appointments - appointment_id (STRING, PK) - user_id (STRING) - scheduled_start_...

Data Manipulation (SQL/Python)
3
0
25 people solved
Oct 26, 2025

Frequently Asked Questions

How hard are Data Analyst interviews and what do interviewers evaluate?
Data Analyst interviews are generally moderate to challenging depending on seniority and the company. Interviewers evaluate three things: technical fluency with SQL and data tools, quantitative reasoning including basic statistics and experiment logic, and business sense—turning numbers into recommendations. Entry-level roles skew toward practical SQL and Excel problems, mid-level roles add A/B testing and modeling, and senior roles require data architecture and stakeholder influence. Expect to be scored on correctness, clarity of assumptions, and communication: being able to explain your steps and recommend actions often matters more than producing a perfect query.
What does a typical Data Analyst interview process look like, where are these roles being hired now, and what technical themes repeat across companies?
A typical loop starts with a recruiter or phone screen within 3–7 days to confirm fit, followed by a technical screening that is either a live SQL exercise or a take-home assessment scheduled within the next 3–10 days. Successful candidates move to two to three panel rounds over one to two weeks that combine SQL/problem solving, a case or metrics deep-dive, and behavioral interviews; senior roles add a data-modeling or analytics-design discussion. Companies actively hiring include Capital One, eBay, The Home Depot, and ByteDance/TikTok. Recurring technical themes across these employers are complex SQL and window functions, product-metrics case studies and experimentation, and data-modeling/ETL considerations for downstream reporting.
How should I structure my prep timeline for the 29-question Data Analyst interview set over the next several weeks?
Plan a focused 4–6 week program. Weeks one and two concentrate on SQL fundamentals and timed practice with joins, groupings, windows, and performance tuning while polishing Excel and basic Python/pandas workflows. Week three adds statistics and experimentation: hypothesis framing, significance, and power intuition plus walk-throughs of real A/B test examples. Week four simulates case studies and stakeholder storytelling: practice metric definition, root-cause analysis, and dashboard narratives. Reserve weeks five and six for full mock interviews, timed assessments, and targeted weak-point drills. Repeat post-mock reviews until you can explain solutions clearly and concisely.
Which technical subtopics should I master for Data Analyst interviews?
Master SQL concepts like joins, aggregations, window functions, CTEs, NULL handling, and query performance plus how to translate business questions into queries. Know data-wrangling with Python or Excel for cleaning and pivoting, and be fluent in exploratory data analysis and visualization best practices for dashboards. Understand experiment design, confidence intervals, and basic statistical testing to interpret A/B results. Also be familiar with data modeling and ETL tradeoffs so you can discuss how upstream choices affect reporting. Finally, practice metric design and backtesting to show you can define and validate reliable KPIs.
What are standout tips for interviews and common pitfalls to avoid?
Start every technical answer by clarifying the question, stating assumptions, and outlining your approach; interviewers reward structured thinking. On SQL challenges, narrate your logic as you write queries so reviewers follow your intent. For case studies, always tie analyses to stakeholder impact and quantify tradeoffs. Common pitfalls include ignoring NULLs and edge cases, reaching conclusions from underpowered tests, and over-indexing on algorithmic cleverness instead of actionable insight. Avoid vague phrasing; give concrete metrics and next steps. Finally, practice concise storytelling so your technical work maps directly to business outcomes.