SQL Interview Questions That Make You Think: Real Problems, Real Logic

Posted by Rakesh Men on

SQL Interview Questions That Actually Teach You Something

Table of Contents

A Short Journey Before the Questions

Years ago, when I started collecting SQL interview questions, I didn’t expect they would become my late-night companions. Ghar ki light chali jaaye to bhi laptop ki blue tint mein ye questions chamak rahe hote. Each question felt like a puzzle waiting to be decoded, and each solution quietly sharpened my thinking. Over time, students, colleagues, and curious beginners kept asking for “the good questions”, the ones that actually teach you something instead of confusing you.

That’s how this collection grew: slow, steady, and thoughtful, like a craftsman polishing a toolset. Whenever I worked with a new team or trained someone transitioning into data roles, these questions helped them unlock clarity. They’re not simply questions—many of them reflect real problems that appear in analytics teams, consulting assignments, and hiring rounds.

So here it is: a curated set of SQL questions worth your scrolling time. Story-friendly, logic-friendly, and interview-friendly. If you're preparing for analytics roles, or you’re already solving business problems daily, this collection will work like an all-season toolkit.

Core SQL Interview Questions

1. Difference Between RANK(), DENSE_RANK(), and ROW_NUMBER()

Every analyst hits this crossroad sooner or later. Knowing when to use each function feels like learning traffic rules in a busy city. RANK() allows ties but skips numbers, DENSE_RANK() allows ties without skipping, and ROW_NUMBER() eliminates ties completely. These tiny differences often decide whether an insight is trustworthy.

2. The Classic: Nth Highest Salary

Given an employee table, you must extract the nth highest salary. This appears everywhere—interviews, competitions, real dashboards, and sometimes in debugging someone else’s query. The logic tests your comfort with subqueries, window functions, or both.

3. Find Employees Under a Manager (Complete Hierarchy)

This isn’t just SQL. This is tree traversal hidden behind tables. Managers have managers, employees have subordinates, and recursion becomes the hero. If you can handle hierarchy questions, you're already in the top tier of problem-solvers.

4. Cumulative Salary of New Joiners

This tests your skill with window functions and date filtering. Real analytics teams use this pattern for rolling revenue, user onboarding, and month-over-month tracking.

5. Top 2 Customers by Order Amount per Category

A classic Top-N problem with ties. If you understand this, you understand ranking logic that fuels leaderboards, dashboards, performance summaries, and sales funnels.

Scenario-Based SQL Problems

When hiring managers say, “We want someone who thinks,” they mean someone who handles scenarios like these. Problems linked to subscriptions, growth, product activity, churn, and cohort insights almost always rely on such logic.

• Window Function Challenges

Rolling totals, moving averages, running revenue, cohort progression—these make an analyst valuable. Window functions are the Swiss Army Knife of SQL, compact yet powerful.

• YOY and MOM Growth

Growth calculations are the bread and butter of analytics. Finding products beating last year’s numbers, measuring city-wise improvements, or analyzing monthly spikes—all stem from these patterns.

• Pivot and Unpivot

Converting rows to columns is the SQL equivalent of turning a story into a spreadsheet. Useful, necessary, and occasionally tricky.

• Join Analysis

Inner, outer, cross, self—this is where interviews secretly test your ability to think across tables. A good join isn’t just a condition; it’s a relationship model.

Advanced SQL Thinking

Here the questions become less about syntax and more about mindset—pattern recognition, optimisation, and storytelling through data. These questions quietly separate those who merely write queries from those who architect solutions.

1. Employees Earning More Than Their Managers

It sounds simple, but requires clean join logic and clarity on relationships. Real companies use this thinking to analyse pay parity and organisational design.

2. Finding Gaps in Sequences

Useful for missing orders, skipped entries, corrupted logs, or tracking system failures. Data tells the full story only when you understand where it goes silent.

3. Top 10% Earners

Percentile logic appears in growth experiments, user scoring, fraud detection, and performance ranking. It’s a favourite in analytics interviews because it tests conceptual maturity.

4. Median, Mode, and Distribution-Based Queries

Average rarely tells the full story. Teams that rely only on averages often miss the “elephant in the room”—a few extreme values skewing everything.

5. Recursive CTEs and Trees

Hierarchy queries show up in finance, HR, retail, product analytics, and marketplace platforms. From referrals to multi-level approvals, recursion sits quietly behind many systems.

Real Datasets & Practice Ideas

Netflix Movies Dataset

Investigate directors, growth patterns, yearly content distribution, and genre spikes. The data behaves like a mini-entertainment universe—messy, rich, and fun.

Employee Information Dataset

Ideal for beginners learning filtering, grouping, benching analysis, and leave prediction logic. Practising on this teaches both SQL skills and business intuition.

Sales and Orders Datasets

Revenue analysis, order trends, customer segmentation, and behavioural patterns—perfect simulation of what analytics teams handle daily.

Closing Thoughts

If you’ve scrolled this far, you already understand the charm of SQL: it’s less about commands and more about clarity. Clarity in thought, clarity in logic, and clarity in how data speaks to you.

Kaam wahi safal hota hai jo dimaag aur dil dono se nikle. SQL follows the same rule—half logic, half intuition. This collection will keep growing as I continue working on real-world problems, teaching motivated learners, and solving challenges for teams that want to make decisions backed by insight instead of guesswork.

If these questions sharpen your approach, feel free to explore deeper topics or reach out where conversations lead to growth. The world of data rewards curiosity, and curiosity grows stronger when shared.

Location: Paris, France

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