“Entry-Level SQL Interview Questions for SaaS Data Analysts”
Are you preparing for a SQL interview for a data analyst role at a SaaS company? These fundamental SQL interview questions cover beginner-to-intermediate-level while supporting you in developing the analytical thinking and business logic to solve business questions.
SQL is a critical technical skill for data analysts because it supports extracting, transforming, validating, and analyzing large amounts of data to answer business questions. In SaaS analytics, SQL is also used to calculate business metrics, including KPIs such as MRR, ARR, churn, retention, and segmentation.
During a SQL technical interview, hiring managers evaluate candidates who know beyond the SQL syntax, understand table relationships, choose appropriate joins, handle NULL values and duplicates, validate results, communicate assumptions, and connect analysis to business outcomes.
Introduce you to practical SQL interview questions relevant to SaaS analytics roles.
Top 10 Fundamental SQL Interview Questions
Q1: How would you filter active SaaS accounts with MRR greater than $1000 in November 2025?
This question is to test understanding of basic SELECT and SQL filtering logic. For example, suppose the account_monthly_snapshot table contains monthly account-level metrics in SaaS data. Identify accounts that were active in November 2025 and had ending MRR above $1000.
Example answer with SQL query:
“First, I chose the required columns for the SELECT clause, then filtered for active accounts and applied the MRR threshold with the reporting_month in the WHERE clause.”
SELECT account_id, mrr_ending
FROM account_monthly_snapshot
WHERE account_status = 'active'
AND mrr_ending > 1000
AND snapshot_month = '2025–11–01';Q2: How would you calculate total MRR by customer segment?
This question is to evaluate aggregation, GROUP BY, and understanding of how to summarize SaaS metrics at the correct business level. For example, in SaaS data, the accounts table contains company_size_segment, while the account_monthly_snapshot table contains mrr_ending. The business wants to compare recurring revenue across customer segments.
Example Answer with SQL query:
“I used an INNER JOIN to connect the account_monthly_snapshot table with accounts using account_id. Then I filtered the data to November 2025, grouped accounts by company size segment, and summed mrr_ending to calculate total ending MRR for each customer segment. This compares revenue contribution across segments.”
SELECT a.company_size_segment,
SUM(s.mrr_ending) AS total_mrr
FROM account_monthly_snapshot s
INNER JOIN accounts a
ON s.account_id = a.account_id
WHERE s.snapshot_month = '2025–11–01'
GROUP BY a.company_size_segment;Q3. How would you find customer segments with more than $500,000 in MRR?
The question is to test aggregation and the difference between WHERE and HAVING. WHERE filters individual rows, while HAVING filters aggregated groups. The candidate should clearly understand SQL execution logic and aggregation. For example, a SaaS company wants to identify the company_size_segment generating more than $500,000 in ending MRR during November 2025.
Example Answer with SQL query:
“I used an INNER JOIN to connect account-level information with monthly MRR using account_id. I filtered the reporting month, grouped MRR by company_size-segment, and calculated total ending MRR. Finally, I used HAVING to return only customer segments with more than $500,000 in ending MRR.”
SELECT a.company_size_segment,
SUM(s.mrr_ending) AS total_mrr
FROM account_monthly_snapshot s
INNER JOIN accounts a
ON s.account_id = a.account_id
WHERE s.snapshot_month = '2025–11–01'
GROUP BY a.company_size_segment
HAVING SUM(s.mrr_ending) > 500000;Q4: How would you join accounts with their subscriptions?
This question is to assess understanding of JOIN logic, table relationships, and awareness of one-to-many relationships. For example, in SaaS data, the accounts table contains customer information, while the subscriptions table contains subscription details. Both tables share the `account_id`, so use a LEFT JOIN based on the shared key. An INNER JOIN returns accounts with matching subscriptions, while a LEFT JOIN preserves accounts without subscriptions. A strong candidate considers the relationship’s cardinality and checks whether multiple subscriptions per account could affect downstream calculations. Candidates should know when to use INNER JOIN versus LEFT JOIN and recognize how one-to-many relationships can create duplicate rows or inflate metrics.
Example Answer with SQL query:
“I identify the shared business key, `account_id`, and join the tables using that relationship.”
SELECT a.account_id, a.account_name, s.subscription_id, s.current_mrr
FROM accounts
LEFT JOIN subscriptions s
ON a.account_id = s.account_id;Q5: How would you handle NULL values in SaaS data?
This question is to assess whether candidates understand data quality and analytical accuracy. For example, a SaaS dataset may contain missing values. Candidates should explain that NULLs must be handled based on business meaning rather than automatically replaced. Functions such as COALESCE() can provide controlled defaults when appropriate, helping prevent NULLs from producing misleading calculations.
Example Answer with SQL query:
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“I would first determine why the value is missing, then decide whether to exclude it, replace it, or treat it separately. If a missing MRR value means zero, I can safely use COALESCE; otherwise, I would preserve the NULL and investigate the data.”
SELECT a.account_id,
COALESCE(annual_company_revenue, 0) AS annual_revenue
FROM accounts;Q6: How would you classify customers into the SaaS segment using CASE?
This question is to test conditional logic and your ability to translate business rules into SQL. For example, in a SaaS scenario, the company wants to classify accounts based on employee count in the accounts table. Use CASE to create business-friendly categories. The boundaries should be confirmed with the business rule rather than assumed.
Example Answer with SQL query:
“I used a CASE expression to segment customers based on employee count. Accounts with fewer than 50 employees are classified as Small, those with 50–249 as Mid-Market, and those with 250 or more as Enterprise. This translates the agreed segmentation rules into mutually understandable conditions.”
SELECT account_id,
CASE
WHEN employee_count < 50 THEN 'Small'
WHEN employee_count < 250 THEN 'Mid-Market'
ELSE 'Enterprise'
END AS customer_segment
FROM accounts;Q7: How would you calculate Monthly Recurring Revenue (MRR) for a specific reporting period?
This question is to test date filtering in the reporting period logic and metric accuracy. For example, in SaaS data, the account_monthly_snapshot table stores MRR information. Filter to the required reporting month and aggregate ending MRR with SUM(). Clarify whether the business wants beginning or ending MRR before writing the query.
Example Answer with SQL query:
“I first filtered the account_monthly_snapshot table to November 2025 and summed mrr_ending to calculate total ending MRR for that reporting month. I grouped the results by snapshot_month to return the aggregated MRR value for the specified period.”
SELECT snapshot_month,
SUM(mrr_ending) AS ending_mrr
FROM account_monthly_snapshot
WHERE snapshot_month = '2025–11–01'
GROUP BY snapshot_month;Q8: How would you use a CTE to identify accounts with declining product usage?
This question is to test structured analytical thinking and basic CTE usage. For example, in SaaS data, the feature_usage_summary table contains monthly `event_count` by account. Compare usage across two months. A CTE can separate the monthly aggregation from the comparison logic, making the query easier to validate and explain.
Example Answer with SQL query:
“I used two CTEs to identify accounts with declining product usage. The first CTE aggregates feature events by account and month. The second CTE compares October and November usage using conditional aggregation with CASE. Finally, I filtered accounts where November usage is lower than October, identifying customers with a month-over-month decline in product engagement.”
WITH usage AS (
SELECT account_id,
summary_month,
SUM(event_count) AS events
FROM feature_usage_summary
GROUP BY account_id, summary_month
),
usage_comparison AS (
SELECT account_id,
MAX(CASE WHEN summary_month = '2025–10–01' THEN events END) AS oct_events,
MAX(CASE WHEN summary_month = '2025–11–01' THEN events END) AS nov_events
FROM usage
GROUP BY account_id
)
SELECT account_id,
oct_events,
nov_events
FROM usage_comparison
WHERE nov_events < oct_events;Q9: How would you identify the latest subscription record for each account?
The question is to test basic window functions, deduplication, and record selection. For example, in SaaS data, an accounts table can have multiple subscription records. You need the most recent record based on `subscription_start_date`. The ROW_NUMBER() ranks records within each account, allowing you to retain the latest one.
Example Answer with SQL query:
“I used a CTE with ROW_NUMBER() to rank each account’s subscriptions by subscription_start_date in descending order. This places the most recent subscription first for each account. Finally, I filtered for rn = 1 to return only the latest subscription record per account, helping avoid duplicate or outdated subscription records.”
WITH ranked AS (
SELECT *,
ROW_NUMBER() OVER (
PARTITION BY account_id
ORDER BY subscription_start_date DESC
) AS rn
FROM subscriptions
)
SELECT *
FROM ranked
WHERE rn = 1;Q10: How would you calculate churned MRR by customer segment?
This question is to test business-oriented SQL, joins, aggregation, and metric interpretation. For example, in SaaS data, the churn table contains the `account_id` and `mrr_lost`, while the accounts table contains the `company_size_segment` field. The business wants to understand where revenue loss is concentrated. Join the tables, aggregate lost MRR by segment, and validate that each churn record is counted correctly.
Example Answer with SQL query:
“I connect churn records to customer segments, calculate lost MRR by segment, then compare each segment’s churned MRR with total churned MRR to determine its contribution.”
SELECT a.company_size_segment,
SUM(c.mrr_lost) AS churned_mrr
FROM churn c
LEFT JOIN accounts a
ON c.account_id = a.account_id
GROUP BY a.company_size_segment
ORDER BY churned_mrr DESC;SQL tips:
The key is to connect churned MRR to business impact, not just report the total amount. The analysis helps stakeholders identify which segments require the most attention and prioritize retention strategies based on revenue impact.
Final Thoughts
These SQL interview questions are designed for entry-level data analyst candidates. The right approach to prepare for SQL interview questions is to understand the data, clarify assumptions, choose the correct tables, validate results, and explain what the numbers mean for the business.
Strong SQL skills, combined with analytical thinking and business knowledge, demonstrate your ability to solve real-world business problems using data.
👩💻 Which SQL interview question do you find most challenging? Share it and compare your approach with other data analysts preparing for their next interview.🧠
