Technical Interview Mastery

# SQL Window Functions for Data Analysts: The Interview Cheat Sheet (2026)

By Vinay

Founder of Vtricks Technologies

Domain: Technical Interview Prep & SQL Architecture • June 2026

If you are preparing for a Data Analyst role in Bangalore, you can forget about basic `SELECT` and `WHERE` clauses. In the 2026 hiring climate, every single technical interview—from fast-moving fintech startups in HSR Layout to established MNCs in Whitefield—will throw at least one complex Window Function problem at you.

The reason is simple: Window functions allow you to perform calculations across a set of table rows that are related to the current row, without collapsing them into a single aggregate row (like `GROUP BY` does). This makes them the ultimate tool for sophisticated business intelligence reporting. Mastering these is the fastest way to get your resume marked as "Hired."

## The "Big 5" Window Functions Cheat Sheet

#### 1. RANK() & DENSE_RANK()

Interview question: "Find the top 3 selling products in every category." Use RANK to assign ranks; DENSE_RANK handles ties without skipping values.

`RANK() OVER (PARTITION BY Category ORDER BY Sales DESC)`

#### 2. LEAD() & LAG()

The absolute essential for time-series analysis. Use these to compare current month revenue vs. previous month revenue without doing a self-join.

`LAG(Revenue) OVER (ORDER BY Month)`

#### 3. ROW_NUMBER()

Use this to assign a unique, sequential integer to each row. Perfect for de-duplicating records or picking the "first" entry of a user.

`ROW_NUMBER() OVER (PARTITION BY UserID ORDER BY LoginTime DESC)`

#### 4. SUM() / AVG() OVER()

Calculates running totals or moving averages while keeping your detail-level data accessible.

`SUM(Revenue) OVER (ORDER BY Date)`

#### 5. NTILE()

Divides your result set into equal buckets (e.g., Quartiles or Percentiles). Often used for customer segmentation.

`NTILE(4) OVER (ORDER BY SpendAmount DESC)`

## Why Interviewers Obsess Over These

When you use a standard `GROUP BY`, you destroy the raw row-level data. You can no longer see the individual transaction that led to the total. Window functions solve this by allowing you to inject aggregated metrics—like a running total or a department-wide rank—directly into the row without sacrificing the granular detail level.

In Bangalore's highly competitive tech sector, understanding this distinction marks the difference between a Junior Analyst (who can only summarize data) and a Senior Data Architect (who can query complex business logic independently). If you can solve an interview problem using a window function rather than a clunky, slow self-join, you demonstrate technical maturity that immediately validates a higher salary bracket.