SQL Interview Questions for Business Analysts (with Query Examples): 2025 Guide

This guide combines theoretical knowledge with practical application, featuring real-world scenarios you’ll encounter when analyzing customer behavior, calculating conversion rates, or building executive dashboards. By the end, you’ll have the confidence to tackle any SQL interview questions for business analyst roles, from entry-level positions to senior analytics leadership roles.

Table of Contents

• What Business Analyst SQL Screens Actually Test
• Core SQL Concepts Every Business Analyst Must Know
• 30 Most Common SQL Interview Questions for Business Analysts
• 10 Hands-On SQL Tasks with Business Context
• Practice Datasets and Preparation Resources
• Interview Success Tips and Best Practices

What Business Analyst SQL Screens Actually Test

Understanding what interviewers are really looking for in business analyst SQL interviews can dramatically improve your preparation strategy. Unlike software developer SQL assessments that focus heavily on algorithm optimization and complex database administration tasks, BA SQL screens evaluate your ability to solve business problems through data analysis.

The primary focus of these interviews is your capacity to translate business requirements into accurate SQL queries. Hiring managers want to see that you can take a real-world business question like “Which customer segments have the highest lifetime value?” and construct the appropriate joins, aggregations, and filters to deliver that insight. This practical approach to SQL for business analyst roles reflects the day-to-day responsibilities you’ll handle on the job.

Entry-Level Business Analyst SQL Expectations

For entry-level positions, SQL interview questions focus on demonstrating your foundational understanding of relational databases and basic query construction. Interviewers typically test your knowledge of essential operations like SELECT statements, WHERE clauses, and simple aggregations. The goal is to verify that you can reliably extract data to support reporting and analysis tasks without requiring extensive mentoring from senior team members.

Common topics include understanding different types of JOINs, using GROUP BY with aggregate functions like COUNT and SUM, and applying DISTINCT to eliminate duplicate records. You’ll also encounter questions about filtering data using IN operators and basic subqueries. The emphasis is on accuracy and clarity rather than performance optimization, though explaining your thought process is equally important.

Key Focus Areas for Entry-Level: Basic syntax accuracy, understanding JOIN logic, proper use of aggregate functions, and the ability to explain query construction step-by-step.

Mid-Level and Senior Business Analyst SQL Requirements

As you advance to mid-level and senior business analyst interview questions, the complexity increases significantly. Interviewers introduce scenarios involving window functions, Common Table Expressions (CTEs), and multi-layered queries that require sophisticated business logic. These assessments evaluate not just your technical SQL skills, but your ability to design efficient solutions for complex analytical challenges.

Advanced questions often involve calculating business metrics like customer retention rates, cohort analysis, and year-over-year growth comparisons. You might be asked to construct queries that handle time-series data, perform rolling calculations, or create dynamic segmentation logic. Performance considerations become crucial at this level, with expectations around query optimization and understanding of indexing strategies.

Senior roles frequently include case-study style questions where you’re given a business scenario and must design the entire analytical approach, including data model considerations and query architecture. These assessments test your ability to think strategically about data analysis rather than just execute predetermined tasks.

Business Context vs. Technical Proficiency

What sets business analyst SQL interviews apart from pure technical roles is the emphasis on business application. Interviewers don’t just want to see that you can write syntactically correct queries; they want evidence that you understand why specific approaches matter for business decision-making.

For example, when calculating customer lifetime value, a strong candidate doesn’t just provide the correct SUM and GROUP BY syntax. They explain considerations like handling refunds, accounting for different subscription models, and why certain time windows might be more meaningful for the business. This business-first thinking is what transforms data extraction into valuable insights.

Communication skills are equally important in these assessments. You’ll often be asked to walk through your query logic step-by-step, justify your approach, and explain how the results would be interpreted by non-technical stakeholders. This reflects the collaborative nature of business analyst work, where you’ll regularly present findings to executives, product managers, and other cross-functional partners.

Pro Tip: Always frame your SQL solutions in business terms. Instead of saying “This query uses a LEFT JOIN,” explain “This approach ensures we capture all customers, even those who haven’t made recent purchases, giving us a complete view for our retention analysis.”

Industry-Specific Variations

Different industries emphasize various aspects of SQL for business analytics based on their core business models and data challenges. E-commerce companies often focus heavily on customer behavior analysis, requiring expertise in funnel analysis and cohort calculations. Financial services emphasize risk analysis and regulatory reporting, often involving complex aggregations and time-series analysis.

SaaS companies typically test your ability to calculate subscription metrics like Monthly Recurring Revenue (MRR), churn rates, and user engagement scores. These scenarios require an understanding of window functions for period-over-period comparisons and sophisticated date handling for subscription lifecycle analysis.

Regardless of the industry, the underlying principle remains consistent: demonstrate that you can utilize SQL not just as a data extraction tool, but as a strategic instrument for driving business insights and supporting informed decision-making processes.

Core SQL Concepts Every Business Analyst Must Know

This section builds your foundation by covering the essential SQL concepts that appear in virtually every business analyst interview. Rather than diving straight into complex scenarios, we’ll establish the building blocks that make advanced analysis possible. Think of these concepts as your analytical toolkit – each one serves a specific purpose in transforming raw data into business intelligence.

The concepts covered here form the backbone of most SQL interview questions for business analyst positions. Mastering these fundamentals allows you to approach more complex scenarios with confidence, knowing you have the technical foundation to construct sophisticated queries that deliver accurate business insights.

Data Retrieval and Basic Filtering

Every business analysis begins with extracting the right data from the right tables. The SELECT statement forms the cornerstone of all SQL operations, but business analysts need to understand how to combine it effectively with WHERE clauses to filter data according to business criteria.

When working with customer data, you might need to filter transactions within specific date ranges, focus on particular product categories, or analyze behavior from specific geographic regions. Understanding how to construct precise WHERE clauses prevents common pitfalls like including test accounts, accounting for null values, or accidentally filtering out important edge cases.

The DISTINCT keyword becomes particularly important when analyzing unique entities. Business questions often revolve around counting unique customers, distinct products sold, or separate marketing campaigns. Knowing when and how to apply DISTINCT ensures your metrics accurately represent business reality rather than inflated counts from duplicate records.

Example:
— Finding unique customers who made purchases last quarter
SELECT DISTINCT customer_id, customer_name
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id
WHERE order_date >= ‘2024-10-01’
AND order_date < ‘2025-01-01’;

Aggregation Functions for Business Metrics

Aggregate functions transform detailed transactional data into meaningful business metrics. COUNT tells you how many records meet specific criteria, SUM calculates total revenue or quantities, and AVG provides baseline performance indicators. These functions become powerful when combined with GROUP BY statements to segment analysis by customer types, time periods, or product categories.

Understanding the nuances of these functions is crucial for accurate business analysis. COUNT versus COUNT DISTINCT makes the difference between counting transactions and counting unique customers. MIN and MAX functions help identify ranges, outliers, and boundary conditions that often reveal important business insights.

The GROUP BY statement deserves special attention in business analyst SQL interview preparation. It enables the segmentation analysis that drives most business decisions. Whether you’re comparing performance across regions, analyzing trends over time, or segmenting customers by behavior patterns, GROUP BY makes these comparisons possible.

Example:
— Monthly revenue analysis by product category
SELECT
category_name,
DATE_TRUNC(‘month’, order_date) as month,
COUNT(DISTINCT order_id) as total_orders,
SUM(order_amount) as total_revenue,
AVG(order_amount) as avg_order_value
FROM orders o
JOIN products p ON o.product_id = p.product_id
WHERE order_date >= ‘2024-01-01’
GROUP BY category_name, DATE_TRUNC(‘month’, order_date)
ORDER BY month, total_revenue DESC;

JOIN Operations for Connected Data Analysis

Business data rarely exists in isolation. Customer information connects to transaction records, which link to product catalogs, which relate to supplier data. Understanding different types of JOINs enables you to bring together these related datasets to answer complex business questions.

INNER JOINs work well when you need data that exists in both tables, such as analyzing purchases by registered customers. LEFT JOINs become essential when you want to include all records from your primary table, even if matching data doesn’t exist in the secondary table. This is particularly important for customer analysis where you might want to see all customers, including those who haven’t made recent purchases.

RIGHT JOINs and FULL OUTER JOINs appear less frequently in business analysis but become valuable in specific scenarios like data quality assessments or comprehensive reporting, where you need to account for all possible combinations of data.

Self JOINs represent a more advanced concept that becomes valuable for hierarchical data analysis. You might use self JOINs to compare employee performance against their managers, analyze customer referral patterns, or identify trends by comparing current performance against historical baselines.

Filtering Groups with HAVING Clauses

The distinction between WHERE and HAVING clauses often appears in SQL for BA interview questions because it tests your understanding of SQL execution order. WHERE filters individual rows before grouping occurs, while HAVING filters groups after aggregation is complete.

This concept becomes particularly important when analyzing business metrics that require threshold-based filtering. For example, identifying high-value customer segments requires grouping customers by their total spending, then filtering groups where total spending exceeds a specific amount. This type of analysis drives customer segmentation strategies and helps prioritize sales efforts.

Example:
— Finding customer segments with high average order values
SELECT
customer_segment,
COUNT(DISTINCT customer_id) as customer_count,
AVG(order_amount) as avg_order_value,
SUM(order_amount) as total_revenue
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id
WHERE order_date >= CURRENT_DATE – INTERVAL ‘365 days’
GROUP BY customer_segment
HAVING AVG(order_amount) > 500
ORDER BY avg_order_value DESC;

Subqueries and Complex Data Relationships

Subqueries enable multi-step analysis within a single SQL statement, making them essential for sophisticated business analysis. They allow you to filter main queries based on calculated values, compare individual records against aggregated benchmarks, or break complex problems into manageable components.

Correlated subqueries, where the inner query references values from the outer query, become particularly valuable for customer analysis. You might use them to identify customers whose spending patterns deviate from their historical averages or to find products whose sales performance varies significantly across different time periods.

Understanding when to use subqueries versus JOINs affects both query performance and code readability. In business analyst roles, maintainability often matters as much as performance, since your queries may be modified by other team members or adapted for different business requirements.

Window Functions for Advanced Analytics

Window functions represent the bridge between basic SQL and advanced analytics. They enable calculations like running totals, moving averages, and ranking operations that are fundamental to business analysis. These functions become indispensable when calculating metrics like customer lifetime value, sales trends, or comparative performance analysis.

ROW_NUMBER, RANK, and DENSE_RANK functions help identify top performers, segment customers into percentiles, or find tied values in competitive analyses. LAG and LEAD functions enable period-over-period comparisons, making them essential for trend analysis and growth calculations.

Partitioning in window functions allows you to perform these calculations within specific groups, such as analyzing sales trends separately for each product category or calculating customer rankings within different geographic regions. This granular control makes window functions particularly powerful for business segmentation analysis.

Business Application: Window functions excel at answering questions like “What’s the month-over-month growth rate for each product line?” or “Which customers rank in the top 10% for their respective segments?” These are exactly the types of insights that drive strategic business decisions.

Common Table Expressions for Complex Analysis

Common Table Expressions (CTEs) serve as temporary result sets that make complex queries more readable and maintainable. In business analysis contexts, CTEs help break down multi-step calculations into logical components, making your analytical approach transparent to both technical and non-technical stakeholders.

CTEs become particularly valuable when performing cohort analysis, calculating complex business metrics, or creating intermediate datasets for further analysis. They allow you to build analytical narratives that follow natural business logic, starting with base data preparation and progressing through increasingly sophisticated calculations.

Recursive CTEs, while less common in typical business analysis, occasionally appear in interviews for roles involving organizational hierarchy analysis or multi-level marketing structures. Understanding their basic concepts demonstrates advanced SQL knowledge that can set you apart from other candidates.

Data Types and Business Context

Understanding data types goes beyond technical specifications to include business implications. Date and time handling becomes crucial when analyzing seasonal trends, calculating customer lifecycle metrics, or determining service level agreements. Numeric precision affects financial calculations and ensures accurate reporting to executives and regulatory bodies.

String manipulation functions help clean and standardize data for analysis, particularly important when working with customer-generated content or integrating data from multiple systems. Understanding how to handle NULL values appropriately ensures your analysis accounts for incomplete data without skewing results.

These foundational concepts create the technical vocabulary you need to tackle any SQL interview questions for business analysts. More importantly, they provide the building blocks for the sophisticated analytical thinking that separates successful business analysts from those who simply extract data without generating insights.

30 Most Common SQL Interview Questions for Business Analysts

This section presents the most frequently asked SQL interview questions for business analyst roles, organized by difficulty level to match your experience and target position. Each question includes both the technical solution and the business reasoning behind it, helping you understand not just how to write the query, but why specific approaches matter for analytical accuracy.

The questions progress from fundamental concepts that every business analyst should master to advanced scenarios that demonstrate senior-level analytical thinking. Pay attention to the business context provided with each question, as interviews often test your ability to translate business requirements into technical solutions rather than just coding syntax.

Beginner Level Questions (Foundation Building)

These questions test your grasp of essential SQL operations and basic data analysis concepts. They form the foundation that interviewers expect every business analyst candidate to demonstrate confidently.

Beginner-level SQL interview questions for business analyst positions typically focus on data retrieval accuracy and basic analytical thinking. Interviewers at this level want to see that you understand how databases store business information and can construct reliable queries to extract meaningful datasets. The emphasis is on demonstrating systematic thinking about data relationships rather than showcasing advanced technical capabilities.

These questions often mirror real entry-level tasks like generating customer lists for marketing campaigns, calculating basic performance metrics for monthly reports, or extracting transaction data for financial reconciliation. Success at this level requires showing that you can work independently with common business datasets while maintaining data integrity and producing accurate results that stakeholders can trust.

Expect interviewers to probe your understanding of when to use different SQL operations and how various functions behave with edge cases like null values or empty result sets. They want confidence that you won’t inadvertently create misleading reports or miss important data due to incorrect query construction.

1. What is SQL, and why is it important for business analysts?

SQL stands for Structured Query Language and serves as the standard method for communicating with relational databases. For business analysts, SQL provides direct access to organizational data without requiring technical intermediaries, enabling faster hypothesis testing and more responsive analysis.

The importance extends beyond data extraction. SQL allows business analysts to validate data quality, perform exploratory analysis, and create repeatable analytical processes. This capability becomes essential when supporting cross-functional teams that need quick answers to emerging business questions.

2. How do you eliminate duplicate records in your analysis?

The DISTINCT keyword removes duplicate rows from query results, ensuring accurate counts and preventing inflated metrics. In business analysis, duplicates often arise from data integration issues, multiple system entries, or join operations that create unintended record multiplication.

— Finding unique customers who purchased in multiple categories
SELECT DISTINCT customer_id
FROM orders o
JOIN products p ON o.product_id = p.product_id
WHERE p.category IN (‘Electronics’, ‘Clothing’, ‘Books’);

Understanding when duplicates represent genuine business events versus data quality issues is crucial. Sometimes, apparent duplicates actually represent legitimate multiple transactions that should be preserved in your analysis.

3. Explain the difference between COUNT(*) and COUNT(column_name).

COUNT(*) counts all rows in a result set, including those with null values, while COUNT(column_name) only counts rows where the specified column contains non-null values. This distinction matters significantly when analyzing customer data or survey responses where incomplete information is common.

For business analysis, this difference affects metrics like response rates, data completeness assessments, and customer profile analysis. Using the wrong COUNT function can lead to misleading conclusions about data quality or customer engagement levels.

4. When should you use WHERE versus HAVING in a query?

WHERE filters individual rows before grouping occurs, while HAVING filters groups after aggregation is complete. This execution order difference becomes critical when calculating business metrics that require threshold-based analysis.

Use WHERE when filtering source data based on transaction dates, customer types, or product categories. Use HAVING when filtering aggregated results, such as identifying customer segments with average order values above a certain threshold or finding regions with total sales exceeding targets.

— WHERE filters before grouping, HAVING filters after
SELECT customer_segment, AVG(order_amount) as avg_value
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id
WHERE order_date >= ‘2024-01-01’ — Filter individual orders first
GROUP BY customer_segment
HAVING AVG(order_amount) > 200; — Filter groups after calculation

5. What are the main types of JOINs, and when do you use each?

INNER JOIN returns only records that exist in both tables, making it ideal for analyzing complete data relationships like customer purchases or product performance. LEFT JOIN includes all records from the left table plus matching records from the right table, essential for comprehensive customer analysis where you need to see all customers, including inactive ones.

RIGHT JOIN works similarly but keeps all records from the right table, though LEFT JOIN covers most business analysis scenarios. FULL OUTER JOIN returns all records from both tables, useful for data reconciliation or identifying gaps between systems.

The choice depends on your business question. Analyzing active customer behavior calls for an INNER JOIN, while customer retention analysis requires a LEFT JOIN to include customers who may not have recent activity.

6. How do you handle NULL values in business analysis?

NULL values represent missing or unknown data, common in customer profiles, survey responses, or incomplete transactions. Understanding how NULL values behave in calculations prevents analytical errors that could mislead business decisions.

Aggregate functions typically ignore NULL values, which can be beneficial or problematic depending on your analysis goals. Use COALESCE or CASE statements to handle NULLs explicitly, replacing them with default values or excluding them from calculations as appropriate for your business context.

Intermediate Level Questions (Analytical Depth)

These questions test your ability to construct more sophisticated queries that address real business scenarios. They require combining multiple SQL concepts to solve analytical challenges that reflect day-to-day responsibilities in business analyst roles.

Intermediate-level assessments move beyond basic data extraction to evaluate your analytical problem-solving abilities. Interviewers introduce scenarios that require multi-step thinking, such as calculating customer retention rates, identifying trend patterns, or performing comparative analysis across different time periods or customer segments. These questions mirror the type of analytical work that forms the core of most business analyst responsibilities.

At this level, you’re expected to demonstrate understanding of window functions for period-over-period comparisons, effective use of subqueries for complex filtering logic, and the ability to structure queries that handle real-world data complexities like missing values or irregular time series. The focus shifts from syntax accuracy to analytical methodology and business insight generation.

Success requires demonstrating that you can approach ambiguous business questions systematically, break them down into logical and analytical steps, and construct SQL solutions that deliver actionable insights. Interviewers often ask you to explain alternative approaches and justify why you chose specific techniques over others, testing both your technical knowledge and business judgment.

7. How do you calculate customer retention rates using SQL?

Customer retention analysis requires identifying customers active in a base period and determining how many remain active in subsequent periods. This typically involves date-based filtering, customer segmentation, and calculation of percentages.

— Monthly customer retention calculation
WITH base_customers AS (
SELECT DISTINCT customer_id
FROM orders
WHERE order_date >= ‘2024-01-01’ AND order_date < ‘2024-02-01’ ), returning_customers AS ( SELECT DISTINCT customer_id FROM orders WHERE order_date >= ‘2024-02-01’ AND order_date < ‘2024-03-01’ ) SELECT COUNT(bc.customer_id) as base_customers, COUNT(rc.customer_id) as returning_customers, ROUND(COUNT(rc.customer_id) * 100.0 / COUNT(bc.customer_id), 2) as retention_rate FROM base_customers bc LEFT JOIN returning_customers rc ON bc.customer_id = rc.customer_id;

8. Write a query to find the top 5 best-selling products by revenue.

This question tests your ability to combine joins, aggregation, and sorting to identify business performance leaders. The solution requires understanding how to calculate revenue when dealing with quantity and price relationships properly.

— Top 5 products by total revenue
SELECT
p.product_name,
SUM(o.quantity * o.unit_price) as total_revenue,
COUNT(DISTINCT o.order_id) as order_count,
SUM(o.quantity) as units_sold
FROM orders o
JOIN products p ON o.product_id = p.product_id
GROUP BY p.product_id, p.product_name
ORDER BY total_revenue DESC
LIMIT 5;

9. How do you identify customers who have made purchases in multiple product categories?

Cross-category purchasing analysis helps identify customer segments for cross-selling opportunities and customer value assessment. This requires understanding how to count distinct values within groups and apply appropriate filtering logic.

— Customers purchasing across multiple categories
SELECT
c.customer_id,
c.customer_name,
COUNT(DISTINCT p.category) as categories_purchased,
STRING_AGG(DISTINCT p.category, ‘, ‘) as category_list
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
JOIN products p ON o.product_id = p.product_id
WHERE o.order_date >= CURRENT_DATE – INTERVAL ’12 months’
GROUP BY c.customer_id, c.customer_name
HAVING COUNT(DISTINCT p.category) > 1
ORDER BY categories_purchased DESC;

10. Calculate year-over-year growth rates for monthly sales.

Growth analysis requires comparing current performance against historical baselines. This involves date manipulation, self-joins or window functions, and percentage calculations that account for periods where historical data might not exist.

— Year-over-year monthly sales growth
WITH monthly_sales AS (
SELECT
DATE_TRUNC(‘month’, order_date) as month,
SUM(order_amount) as total_sales
FROM orders
GROUP BY DATE_TRUNC(‘month’, order_date)
)
SELECT
month,
total_sales,
LAG(total_sales, 12) OVER (ORDER BY month) as same_month_last_year,
ROUND(
(total_sales – LAG(total_sales, 12) OVER (ORDER BY month)) * 100.0 /
LAG(total_sales, 12) OVER (ORDER BY month), 2
) as yoy_growth_percent
FROM monthly_sales
ORDER BY month;

11. Write a query to find customers who haven’t made a purchase in the last 90 days.

Identifying inactive customers requires understanding how to work with date ranges and handle cases where customers might not appear in recent transaction data. This type of analysis drives customer re-engagement campaigns and churn prevention strategies.

— Customers inactive for 90+ days
SELECT
c.customer_id,
c.customer_name,
c.email,
MAX(o.order_date) as last_purchase_date,
CURRENT_DATE – MAX(o.order_date) as days_since_last_purchase
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.customer_name, c.email
HAVING MAX(o.order_date) < CURRENT_DATE – INTERVAL ’90 days’ OR MAX(o.order_date) IS NULL ORDER BY last_purchase_date;

12. How do you calculate the average time between customer orders?

Order frequency analysis requires date arithmetic and often involves window functions to compare consecutive transactions for each customer. This metric helps understand customer behavior patterns and inform inventory planning.

— Average days between orders per customer
WITH order_gaps AS (
SELECT
customer_id,
order_date,
LAG(order_date) OVER (PARTITION BY customer_id ORDER BY order_date) as prev_order_date,
order_date – LAG(order_date) OVER (PARTITION BY customer_id ORDER BY order_date) as days_between
FROM orders
)
SELECT
customer_id,
COUNT(*) as total_orders,
AVG(days_between) as avg_days_between_orders,
MIN(days_between) as shortest_gap,
MAX(days_between) as longest_gap
FROM order_gaps
WHERE days_between IS NOT NULL
GROUP BY customer_id
HAVING COUNT(*) > 1 — Only customers with multiple orders
ORDER BY avg_days_between_orders;

Advanced Level Questions (Strategic Analysis)

Advanced questions test your ability to solve complex business problems using sophisticated SQL techniques. These scenarios often involve multiple analytical steps and require a deep understanding of both SQL capabilities and business metrics.

Senior-level business analyst SQL interview questions evaluate your capacity to handle enterprise-scale analytical challenges that directly impact strategic decision-making. These assessments often present open-ended business scenarios where you must design the entire analytical approach, from data model considerations to final metric calculations. Interviewers expect you to demonstrate advanced SQL techniques like recursive CTEs, complex window functions, and performance optimization strategies.

At this level, questions frequently involve cohort analysis for customer lifecycle understanding, advanced segmentation models for marketing optimization, or sophisticated financial calculations for business planning. You’re expected to handle multi-dimensional analysis that considers various business factors simultaneously, such as seasonal effects, customer behavior patterns, and competitive dynamics.

Success requires demonstrating strategic thinking about data architecture and analytical methodology. Interviewers often probe your understanding of when to use materialized views versus on-demand calculations, how to handle data quality issues at scale, and your ability to balance analytical precision with computational efficiency. These questions test whether you can serve as a trusted analytical partner to senior executives and strategic planning teams.

Advanced interviews also emphasize your ability to communicate complex analytical concepts to non-technical stakeholders. You might be asked to explain how your analytical approach addresses specific business risks or opportunities, demonstrating that you understand the broader business context beyond just technical execution.

13. Create a cohort analysis showing customer retention by acquisition month.

Cohort analysis tracks customer groups over time to understand retention patterns and lifecycle value. This requires creating customer acquisition cohorts, tracking their activity across subsequent periods, and calculating retention percentages for each cohort and time period combination.

— Customer cohort retention analysis
WITH customer_cohorts AS (
SELECT
customer_id,
DATE_TRUNC(‘month’, MIN(order_date)) as cohort_month
FROM orders
GROUP BY customer_id
),
customer_activity AS (
SELECT
c.customer_id,
c.cohort_month,
DATE_TRUNC(‘month’, o.order_date) as activity_month,
EXTRACT(MONTH FROM AGE(o.order_date, c.cohort_month)) as period_number
FROM customer_cohorts c
JOIN orders o ON c.customer_id = o.customer_id
)
SELECT
cohort_month,
period_number,
COUNT(DISTINCT customer_id) as active_customers,
FIRST_VALUE(COUNT(DISTINCT customer_id))
OVER (PARTITION BY cohort_month ORDER BY period_number) as cohort_size,
ROUND(COUNT(DISTINCT customer_id) * 100.0 /
FIRST_VALUE(COUNT(DISTINCT customer_id))
OVER (PARTITION BY cohort_month ORDER BY period_number), 2) as retention_rate
FROM customer_activity
GROUP BY cohort_month, period_number
ORDER BY cohort_month, period_number;

14. How do you identify seasonal trends in sales data using SQL?

Seasonal analysis requires extracting time components from dates and comparing performance across equivalent periods in different years. This involves date functions, window operations for year-over-year comparisons, and often statistical calculations to identify significant variations.

15. Write a query to calculate customer lifetime value (CLV).

CLV calculation involves aggregating all customer transactions, often with time-based weighting or future value projections. This metric drives customer acquisition spending decisions and helps prioritize customer success efforts.

— Customer Lifetime Value calculation
SELECT
c.customer_id,
c.customer_name,
COUNT(DISTINCT o.order_id) as total_orders,
SUM(o.order_amount) as total_spent,
AVG(o.order_amount) as avg_order_value,
MIN(o.order_date) as first_purchase,
MAX(o.order_date) as last_purchase,
EXTRACT(DAYS FROM (MAX(o.order_date) – MIN(o.order_date))) as customer_lifespan_days,
ROUND(SUM(o.order_amount) / NULLIF(EXTRACT(DAYS FROM (MAX(o.order_date) – MIN(o.order_date))), 0), 2) as revenue_per_day
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.customer_name
HAVING COUNT(DISTINCT o.order_id) > 1
ORDER BY total_spent DESC;

16. How do you find the second highest salary in each department?

Ranking queries test your understanding of window functions and how to handle tied values appropriately. This scenario appears frequently because it mirrors common business needs like identifying top performers or backup candidates.

–– Second highest salary by department using DENSE_RANK
SELECT
department,
employee_name,
salary
FROM (
SELECT
department,
employee_name,
salary,
DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) as salary_rank
FROM employees
) ranked_salaries
WHERE salary_rank = 2;

17. Create a query to analyze sales performance by quarter.

Quarterly analysis requires date extraction functions and often involves comparing performance across multiple time periods. This type of analysis supports budget planning and performance review processes.

— Quarterly sales performance analysis
SELECT
EXTRACT(YEAR FROM order_date) as year,
EXTRACT(QUARTER FROM order_date) as quarter,
COUNT(DISTINCT order_id) as total_orders,
COUNT(DISTINCT customer_id) as unique_customers,
SUM(order_amount) as quarterly_revenue,
AVG(order_amount) as avg_order_value,
ROUND(SUM(order_amount) / COUNT(DISTINCT customer_id), 2) as revenue_per_customer
FROM orders
WHERE order_date >= ‘2023-01-01’
GROUP BY EXTRACT(YEAR FROM order_date), EXTRACT(QUARTER FROM order_date)
ORDER BY year, quarter;

18. How do you identify products with declining sales trends?

Trend analysis requires comparing current performance against historical patterns, often using window functions to calculate moving averages or period-over-period changes. This analysis helps identify products that may require marketing support or consideration for discontinuation.

19. Write a query to find the most popular product combinations.

Market basket analysis identifies products that are frequently purchased together, supporting cross-selling strategies and informed inventory planning. This requires self-joins and counting co-occurrence patterns within individual transactions.

— Most popular product combinations in same order
SELECT
p1.product_name as product_1,
p2.product_name as product_2,
COUNT(*) as combination_frequency
FROM order_items oi1
JOIN order_items oi2 ON oi1.order_id = oi2.order_id
JOIN products p1 ON oi1.product_id = p1.product_id
JOIN products p2 ON oi2.product_id = p2.product_id
WHERE oi1.product_id < oi2.product_id — Avoid duplicate pairs GROUP BY p1.product_name, p2.product_name HAVING COUNT(*) >= 10 — Minimum frequency threshold
ORDER BY combination_frequency DESC
LIMIT 20;

20. Calculate conversion rates for different marketing channels.

Conversion analysis requires joining marketing attribution data with transaction records and calculating percentages based on funnel progression. This analysis directly impacts marketing budget allocation and campaign optimization decisions.

21. How do you handle data quality issues in SQL queries?

Data quality assessment requires identifying null values, duplicates, outliers, and inconsistent formatting. Business analysts must understand how to detect these issues and account for them in analytical queries to ensure reliable insights.

22. Write a query to calculate the rolling 7-day average sales.

Moving average calculations smooth out daily fluctuations to reveal underlying trends. This analysis helps identify patterns that might be obscured by day-to-day volatility and supports demand forecasting efforts.

— 7-day rolling average of daily sales
WITH daily_sales AS (
SELECT
DATE(order_date) as sale_date,
SUM(order_amount) as daily_revenue
FROM orders
GROUP BY DATE(order_date)
)
SELECT
sale_date,
daily_revenue,
AVG(daily_revenue) OVER (
ORDER BY sale_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
) as rolling_7day_avg
FROM daily_sales
ORDER BY sale_date;

23. How do you identify outliers in sales data?

Outlier detection requires statistical calculations within SQL, often using standard deviation or percentile-based approaches. Identifying unusual patterns helps discover data quality issues or exceptional business events that warrant investigation.

24. Create a query for RFM analysis (Recency, Frequency, Monetary).

RFM analysis segments customers based on purchase recency, frequency, and monetary value. This sophisticated customer segmentation technique drives targeted marketing strategies and customer relationship management decisions.

— RFM Analysis for customer segmentation
WITH rfm_metrics AS (
SELECT
customer_id,
MAX(order_date) as last_purchase_date,
COUNT(DISTINCT order_id) as frequency,
SUM(order_amount) as monetary_value,
CURRENT_DATE – MAX(order_date) as recency_days
FROM orders
WHERE order_date >= CURRENT_DATE – INTERVAL ‘2 years’
GROUP BY customer_id
),
rfm_scores AS (
SELECT *,
NTILE(5) OVER (ORDER BY recency_days) as recency_score,
NTILE(5) OVER (ORDER BY frequency DESC) as frequency_score,
NTILE(5) OVER (ORDER BY monetary_value DESC) as monetary_score
FROM rfm_metrics
)
SELECT
customer_id,
recency_days,
frequency,
monetary_value,
recency_score,
frequency_score,
monetary_score,
CONCAT(recency_score, frequency_score, monetary_score) as rfm_segment
FROM rfm_scores
ORDER BY monetary_value DESC;

25. How do you calculate market share for different product categories?

Market share analysis requires calculating each category’s performance relative to total market performance. This involves aggregation at multiple levels and percentage calculations that inform competitive positioning strategies.

26. Write a query to identify customers at risk of churning.

Churn prediction through SQL involves analyzing customer behavior patterns, purchase frequency changes, and engagement metrics. This analysis enables proactive customer retention efforts and helps prioritize customer success resources.

— Customers at risk of churning based on declining activity
WITH customer_trends AS (
SELECT
customer_id,
DATE_TRUNC(‘month’, order_date) as order_month,
COUNT(DISTINCT order_id) as monthly_orders,
SUM(order_amount) as monthly_spend
FROM orders
WHERE order_date >= CURRENT_DATE – INTERVAL ‘6 months’
GROUP BY customer_id, DATE_TRUNC(‘month’, order_date)
),
trend_analysis AS (
SELECT
customer_id,
order_month,
monthly_orders,
monthly_spend,
LAG(monthly_orders, 1) OVER (PARTITION BY customer_id ORDER BY order_month) as prev_month_orders,
LAG(monthly_spend, 1) OVER (PARTITION BY customer_id ORDER BY order_month) as prev_month_spend
FROM customer_trends
)
SELECT DISTINCT
customer_id,
COUNT(*) OVER (PARTITION BY customer_id) as active_months,
AVG(monthly_orders) OVER (PARTITION BY customer_id) as avg_monthly_orders,
SUM(CASE WHEN monthly_orders < prev_month_orders THEN 1 ELSE 0 END) OVER (PARTITION BY customer_id) as declining_months FROM trend_analysis WHERE prev_month_orders IS NOT NULL HAVING COUNT(*) OVER (PARTITION BY customer_id) >= 3
AND SUM(CASE WHEN monthly_orders < prev_month_orders THEN 1 ELSE 0 END) OVER (PARTITION BY customer_id) >= 2;

27. How do you analyze customer acquisition costs by channel?

Channel effectiveness analysis requires joining marketing spend data with customer acquisition metrics and calculating cost per acquisition ratios. This analysis directly impacts marketing budget allocation and campaign optimization strategies.

28. Create a query to calculate inventory turnover rates.

Inventory analysis involves comparing sales velocity against stock levels to identify fast-moving and slow-moving products. This operational analysis supports purchasing decisions and helps optimize working capital management.

29. How do you build a customer segmentation model using SQL?

Customer segmentation requires combining multiple behavioral and demographic criteria to create meaningful customer groups. This involves complex CASE statements, percentile calculations, and often scoring algorithms that classify customers into actionable segments.

— Customer segmentation based on value and engagement
WITH customer_metrics AS (
SELECT
c.customer_id,
c.customer_name,
c.registration_date,
COUNT(DISTINCT o.order_id) as total_orders,
SUM(o.order_amount) as total_spent,
AVG(o.order_amount) as avg_order_value,
MAX(o.order_date) as last_purchase,
CURRENT_DATE – MAX(o.order_date) as days_since_last_purchase
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
WHERE c.registration_date >= ‘2023-01-01’
GROUP BY c.customer_id, c.customer_name, c.registration_date
)
SELECT
customer_id,
customer_name,
total_orders,
total_spent,
CASE
WHEN total_spent >= 1000 AND days_since_last_purchase <= 30 THEN ‘VIP Active’ WHEN total_spent >= 1000 AND days_since_last_purchase > 30 THEN ‘VIP At Risk’
WHEN total_spent >= 500 AND days_since_last_purchase <= 60 THEN ‘High Value’ WHEN total_orders >= 5 AND days_since_last_purchase <= 90 THEN ‘Frequent Buyer’ WHEN total_orders >= 1 AND days_since_last_purchase <= 180 THEN ‘Regular Customer’ WHEN total_orders >= 1 AND days_since_last_purchase > 180 THEN ‘Dormant’
ELSE ‘New/Inactive’
END as customer_segment
FROM customer_metrics
ORDER BY total_spent DESC;

30. Write a query to calculate employee productivity metrics across departments.

Productivity analysis requires combining operational data with performance metrics to identify efficiency patterns and improvement opportunities. This type of analysis supports resource allocation decisions and performance management processes.

— Department productivity analysis
WITH department_metrics AS (
SELECT
d.department_name,
COUNT(DISTINCT e.employee_id) as employee_count,
COUNT(DISTINCT p.project_id) as projects_completed,
SUM(p.project_value) as total_project_value,
AVG(p.completion_days) as avg_completion_time
FROM departments d
JOIN employees e ON d.department_id = e.department_id
JOIN project_assignments pa ON e.employee_id = pa.employee_id
JOIN projects p ON pa.project_id = p.project_id
WHERE p.status = ‘Completed’
AND p.completion_date >= CURRENT_DATE – INTERVAL ’12 months’
GROUP BY d.department_id, d.department_name
)
SELECT
department_name,
employee_count,
projects_completed,
total_project_value,
ROUND(projects_completed::DECIMAL / employee_count, 2) as projects_per_employee,
ROUND(total_project_value / employee_count, 2) as value_per_employee,
ROUND(avg_completion_time, 1) as avg_days_to_complete
FROM department_metrics
ORDER BY value_per_employee DESC;

Advanced Interview Tip: When presenting complex queries like these, always explain your approach step by step. Start with the business problem, break down your solution into logical components, and explain how each part contributes to the final result. This demonstrates both technical competence and business thinking.

These 30 questions represent the core competencies expected in SQL for business analyst interviews across different industries and experience levels. Each question tests not only your technical SQL skills but also your ability to think analytically about business problems and construct solutions that deliver actionable insights.

The progression from basic data retrieval to complex analytical scenarios mirrors the typical career development path for business analysts. Mastering these concepts provides the foundation for tackling any business analyst SQL interview challenge while demonstrating the strategic thinking that separates great analysts from those who simply execute queries.

10 Hands-On SQL Tasks with Business Context

This section presents practical SQL tasks for business analyst interviews that simulate real-world analytical challenges you’ll encounter on the job. Unlike standalone technical questions, these tasks require you to approach complex business scenarios systematically, demonstrating both your SQL proficiency and analytical problem-solving capabilities.

Each task includes a business scenario, sample data structure, and detailed solution approach. These exercises mirror the type of analytical work that drives business decisions, from customer behavior analysis to operational efficiency optimization. Practice with these scenarios builds confidence for both technical assessments and case-study style interviews, where you must design analytical solutions from scratch.

The tasks progress from foundational analytical challenges that entry-level analysts encounter to sophisticated strategic analysis that senior business analysts perform for executive teams. Pay attention to how each solution balances analytical rigor with practical business considerations, as this balance often determines interview success.

Task 1: Customer Purchase Behavior Analysis

Business Scenario: Your e-commerce company wants to understand customer purchase patterns to optimize inventory management. Management needs to identify which customers consistently purchase high-value items and how their behavior differs from that of occasional buyers.

Data Available: Customer table (customer_id, name, registration_date), Orders table (order_id, customer_id, order_date, total_amount), Order_items table (order_id, product_id, quantity, unit_price).

Required Analysis: Segment customers into three groups based on purchase frequency and average order value, then calculate key metrics for each segment, including total customers, average order frequency, and revenue contribution.

— Customer segmentation and behavior analysis
WITH customer_summary AS (
SELECT
c.customer_id,
c.customer_name,
COUNT(DISTINCT o.order_id) as order_count,
SUM(o.total_amount) as lifetime_value,
AVG(o.total_amount) as avg_order_value,
MIN(o.order_date) as first_purchase,
MAX(o.order_date) as last_purchase,
EXTRACT(DAYS FROM (MAX(o.order_date) – MIN(o.order_date))) as customer_lifespan
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
WHERE c.registration_date >= ‘2023-01-01’
GROUP BY c.customer_id, c.customer_name
),
customer_segments AS (
SELECT *,
CASE
WHEN order_count >= 10 AND avg_order_value >= 200 THEN ‘Premium Frequent’
WHEN order_count >= 5 OR avg_order_value >= 150 THEN ‘Valuable Regular’
WHEN order_count >= 1 THEN ‘Occasional Buyer’
ELSE ‘Registered Only’
END as customer_segment
FROM customer_summary
)
SELECT
customer_segment,
COUNT(*) as segment_size,
ROUND(AVG(order_count), 2) as avg_orders_per_customer,
ROUND(AVG(lifetime_value), 2) as avg_lifetime_value,
ROUND(SUM(lifetime_value), 2) as segment_total_revenue,
ROUND(SUM(lifetime_value) * 100.0 / SUM(SUM(lifetime_value)) OVER (), 2) as revenue_share_percent
FROM customer_segments
GROUP BY customer_segment
ORDER BY avg_lifetime_value DESC;

Business Impact: This analysis enables targeted marketing strategies, helps optimize customer acquisition costs, and identifies opportunities for customer development programs. The segmentation provides actionable insights for both marketing and customer success teams.

Task 2: Sales Performance Dashboard Data

Business Scenario: The sales team needs a monthly dashboard showing performance trends, goal achievement, and territory comparisons. Your task is to create the data foundation that will feed into their visualization tools.

Data Available: Sales_reps table (rep_id, name, territory, hire_date), Sales table (sale_id, rep_id, customer_id, sale_date, amount), Targets table (rep_id, month, target_amount).

Required Analysis: Calculate monthly performance metrics, including actual vs target achievement, territory rankings, and quarter-over-quarter growth rates for each sales representative.

— Sales performance dashboard data preparation
WITH monthly_sales AS (
SELECT
sr.rep_id,
sr.name as rep_name,
sr.territory,
DATE_TRUNC(‘month’, s.sale_date) as sale_month,
COUNT(DISTINCT s.sale_id) as deals_closed,
SUM(s.amount) as monthly_revenue,
AVG(s.amount) as avg_deal_size
FROM sales_reps sr
LEFT JOIN sales s ON sr.rep_id = s.rep_id
WHERE s.sale_date >= CURRENT_DATE – INTERVAL ’24 months’
GROUP BY sr.rep_id, sr.name, sr.territory, DATE_TRUNC(‘month’, s.sale_date)
),
performance_with_targets AS (
SELECT
ms.*,
t.target_amount,
ROUND(ms.monthly_revenue * 100.0 / NULLIF(t.target_amount, 0), 2) as target_achievement_percent,
LAG(ms.monthly_revenue, 3) OVER (PARTITION BY ms.rep_id ORDER BY ms.sale_month) as same_quarter_last_year
FROM monthly_sales ms
LEFT JOIN targets t ON ms.rep_id = t.rep_id
AND DATE_TRUNC(‘month’, ms.sale_month) = DATE_TRUNC(‘month’, t.month)
)
SELECT
rep_name,
territory,
sale_month,
deals_closed,
monthly_revenue,
target_amount,
target_achievement_percent,
CASE
WHEN target_achievement_percent >= 100 THEN ‘Target Met’
WHEN target_achievement_percent >= 80 THEN ‘Close to Target’
ELSE ‘Below Expectations’
END as performance_status,
ROUND((monthly_revenue – same_quarter_last_year) * 100.0 /
NULLIF(same_quarter_last_year, 0), 2) as yoy_growth_percent
FROM performance_with_targets
WHERE sale_month >= CURRENT_DATE – INTERVAL ’12 months’
ORDER BY territory, rep_name, sale_month;

Task 3: Product Profitability Analysis

Business Scenario: The product management team needs to evaluate which products contribute most to company profitability and identify underperforming items that may need pricing adjustments or discontinuation.

Data Available: Products table (product_id, name, category, cost_price), Orders table (order_id, product_id, quantity, selling_price, order_date), Returns table (return_id, order_id, quantity_returned, return_date).

Required Analysis: Calculate net profitability by product, including return rates, identify margin trends over time, and rank products by contribution to overall company profitability.

— Product profitability analysis with returns consideration
WITH product_sales AS (
SELECT
p.product_id,
p.product_name,
p.category,
p.cost_price,
SUM(o.quantity) as units_sold,
SUM(o.quantity * o.selling_price) as gross_revenue,
SUM(o.quantity * p.cost_price) as total_cost
FROM products p
JOIN orders o ON p.product_id = o.product_id
WHERE o.order_date >= CURRENT_DATE – INTERVAL ’12 months’
GROUP BY p.product_id, p.product_name, p.category, p.cost_price
),
product_returns AS (
SELECT
o.product_id,
SUM(r.quantity_returned) as units_returned,
SUM(r.quantity_returned * o.selling_price) as return_revenue_lost
FROM returns r
JOIN orders o ON r.order_id = o.order_id
WHERE r.return_date >= CURRENT_DATE – INTERVAL ’12 months’
GROUP BY o.product_id
)
SELECT
ps.product_name,
ps.category,
ps.units_sold,
COALESCE(pr.units_returned, 0) as units_returned,
ROUND(COALESCE(pr.units_returned, 0) * 100.0 / ps.units_sold, 2) as return_rate_percent,
ps.gross_revenue,
COALESCE(pr.return_revenue_lost, 0) as return_revenue_lost,
ps.gross_revenue – COALESCE(pr.return_revenue_lost, 0) as net_revenue,
ps.total_cost,
(ps.gross_revenue – COALESCE(pr.return_revenue_lost, 0) – ps.total_cost) as net_profit,
ROUND((ps.gross_revenue – COALESCE(pr.return_revenue_lost, 0) – ps.total_cost) * 100.0 /
ps.gross_revenue, 2) as profit_margin_percent
FROM product_sales ps
LEFT JOIN product_returns pr ON ps.product_id = pr.product_id
WHERE ps.units_sold > 0
ORDER BY net_profit DESC;

Task 4: Marketing Campaign Effectiveness

Business Scenario: The marketing team ran multiple campaigns last quarter and needs to evaluate which channels delivered the best return on investment. They aim to understand both the immediate impact on conversion and the longer-term value generated for customers.

Required Analysis: Calculate campaign-specific conversion rates, customer acquisition costs, and 90-day customer lifetime value to determine overall campaign effectiveness and guide future marketing budget allocation.

Task 5: Inventory Optimization Analysis

Business Scenario: Your retail company struggles with inventory management, experiencing both stockouts and overstock situations. Management needs data-driven insights to optimize inventory levels and improve cash flow management.

Required Analysis: Calculate inventory turnover rates, identify slow-moving products, and analyze seasonal demand patterns to support purchasing decisions and warehouse space allocation.

Task 6: Customer Service Performance Metrics

Business Scenario: The customer service department wants to improve response times and resolution rates. They need a detailed analysis of ticket patterns, agent performance, and customer satisfaction trends to guide operational improvements.

Required Analysis: Create comprehensive service metrics including average response times, resolution rates by issue type, customer satisfaction scores, and agent productivity comparisons.

Task 7: Financial Performance Analysis

Business Scenario: The finance team needs monthly business reviews that highlight key performance indicators, identify concerning trends, and provide variance analysis against budget projections.

Required Analysis: Develop queries that calculate month-over-month and year-over-year growth rates, budget variance analysis, and key financial ratios that executive teams use for strategic planning.

Task 8: Supply Chain Efficiency Assessment

Business Scenario: Your manufacturing company wants to optimize its supply chain by identifying bottlenecks, evaluating supplier performance, and analyzing delivery time patterns that affect customer satisfaction.

Required Analysis: Create metrics for supplier reliability, delivery time analysis, cost efficiency comparisons, and impact assessment of supply chain delays on customer orders.

Task 9: User Engagement and Retention Analysis

Business Scenario: Your SaaS platform needs to understand user engagement patterns to reduce churn and identify expansion opportunities. The product team wants data-driven insights about feature usage and customer lifecycle stages.

Required Analysis: Build comprehensive user engagement metrics, including daily/monthly active users, feature adoption rates, usage trend analysis, and early warning indicators for customer churn risk.

— User engagement and retention metrics
WITH user_activity AS (
SELECT
user_id,
DATE_TRUNC(‘month’, activity_date) as activity_month,
COUNT(DISTINCT DATE(activity_date)) as active_days,
COUNT(DISTINCT feature_used) as features_used,
SUM(session_duration_minutes) as total_session_time
FROM user_sessions
WHERE activity_date >= CURRENT_DATE – INTERVAL ‘6 months’
GROUP BY user_id, DATE_TRUNC(‘month’, activity_date)
),
engagement_trends AS (
SELECT
user_id,
activity_month,
active_days,
features_used,
total_session_time,
LAG(active_days) OVER (PARTITION BY user_id ORDER BY activity_month) as prev_month_days,
LAG(total_session_time) OVER (PARTITION BY user_id ORDER BY activity_month) as prev_month_time
FROM user_activity
)
SELECT
user_id,
activity_month,
active_days,
features_used,
total_session_time,
CASE
WHEN prev_month_days IS NULL THEN ‘New User’
WHEN active_days > prev_month_days THEN ‘Increasing Engagement’
WHEN active_days = prev_month_days THEN ‘Stable Engagement’
WHEN active_days < prev_month_days AND active_days > 0 THEN ‘Declining Engagement’
ELSE ‘At Risk’
END as engagement_trend,
CASE
WHEN active_days >= 20 AND features_used >= 5 THEN ‘Power User’
WHEN active_days >= 10 AND features_used >= 3 THEN ‘Regular User’
WHEN active_days >= 3 THEN ‘Casual User’
ELSE ‘Low Engagement’
END as user_segment
FROM engagement_trends
ORDER BY user_id, activity_month;

Task 10: Competitive Analysis and Market Position

Business Scenario: Your company operates in a competitive market and needs to understand its position relative to industry benchmarks. Management wants insights about market share trends, competitive pricing impacts, and opportunities for market expansion.

Required Analysis: Analyze market share evolution, price competitiveness assessment, customer acquisition patterns compared to industry trends, and identification of market segments where the company has competitive advantages or vulnerabilities.

— Market share and competitive position analysis
WITH market_data AS (
SELECT
DATE_TRUNC(‘quarter’, order_date) as quarter,
‘Our Company’ as company,
SUM(order_amount) as revenue,
COUNT(DISTINCT customer_id) as customers
FROM orders
WHERE order_date >= CURRENT_DATE – INTERVAL ‘2 years’
GROUP BY DATE_TRUNC(‘quarter’, order_date)UNION ALL

SELECT
quarter,
competitor_name as company,
estimated_revenue as revenue,
estimated_customers as customers
FROM competitor_data
WHERE quarter >= CURRENT_DATE – INTERVAL ‘2 years’
),
market_totals AS (
SELECT
quarter,
SUM(revenue) as total_market_revenue,
SUM(customers) as total_market_customers
FROM market_data
GROUP BY quarter
)
SELECT
md.quarter,
md.company,
md.revenue,
mt.total_market_revenue,
ROUND(md.revenue * 100.0 / mt.total_market_revenue, 2) as market_share_percent,
LAG(md.revenue * 100.0 / mt.total_market_revenue)
OVER (PARTITION BY md.company ORDER BY md.quarter) as prev_quarter_share,
ROUND(
md.revenue * 100.0 / mt.total_market_revenue –
LAG(md.revenue * 100.0 / mt.total_market_revenue)
OVER (PARTITION BY md.company ORDER BY md.quarter), 2
) as share_change_points
FROM market_data md
JOIN market_totals mt ON md.quarter = mt.quarter
ORDER BY md.quarter, md.revenue DESC;

Business Impact: This analysis provides executive teams with concrete data about competitive positioning, helps identify market opportunities, and supports strategic planning for market expansion or defensive positioning strategies.

Interview Success Tip: When working through these tasks, always start by clarifying the business objective and explaining your analytical approach before diving into SQL code. Interviewers want to see your problem-solving methodology as much as your technical execution.

These hands-on tasks represent the type of analytical work that distinguishes strong business analyst candidates from those with only theoretical SQL knowledge. Each scenario requires combining technical SQL skills with business judgment to deliver insights that drive organizational decision-making.

Practice with similar scenarios builds the confidence needed to tackle complex SQL for BA interview challenges while demonstrating the strategic thinking that makes business analysts valuable contributors to cross-functional teams and executive planning processes.

Practice Datasets and Preparation Resources

Effective preparation for SQL interview questions for business analyst roles requires hands-on practice with realistic datasets that mirror the type of data you’ll encounter in actual business environments. This section provides curated resources and sample datasets specifically chosen to develop the analytical skills that interviewers evaluate during technical assessments.

The key to successful preparation lies in working with data that reflects real business complexity, including missing values, data quality issues, and the multi-table relationships common in enterprise environments. Generic tutorial datasets often lack the messiness and business context that make SQL analysis challenging and rewarding in professional settings.

Essential Practice Datasets for Business Analysis

Start your preparation with datasets that cover core business functions. E-commerce datasets provide excellent practice for customer analysis, sales forecasting, and inventory management scenarios. Look for datasets that include customer demographics, transaction histories, product catalogs, and seasonal sales patterns.

Financial datasets help you practice with budget analysis, performance tracking, and variance reporting scenarios common in business analyst roles. These datasets typically include time-series data that requires period-over-period comparisons and trend analysis techniques.

Customer service datasets provide practice in calculating operational metrics, performance benchmarking, and quality analysis. These scenarios test your ability to work with timestamp data, categorical variables, and satisfaction scoring systems.

• Kaggle Datasets: E-commerce sales data, customer behavior datasets, and financial performance data
• Sample Business Databases: Northwind, Sakila, and AdventureWorks provide realistic business scenarios
• Public Company Data: SEC filings and retail sales data offer real-world complexity
• Survey Datasets: Customer satisfaction and market research data for analysis practice