SQL Interview Questions for Data Analysts in 2026: The 9 Concepts Interviewers Test

SQL interview questions for data analyst roles usually test JOINs, GROUP BY/HAVING, aggregates, window functions, CTEs, subqueries, date functions, CASE, and NULL handling. Expect live coding, timed tests, or take-homes scored on clarifying assumptions, choosing the right pattern, validating output, and explaining why the query answers the business question.

The non-obvious shift: the question bank is dead; the evaluation rubric is not. SQL is still a hiring filter - 365 Data Science found SQL in 52.9% of 855 relevant US data analyst postings - but AI has changed what "good at SQL" means (365 Data Science, retrieved 2026-07-25). Interviewers increasingly test whether you can frame an ambiguous metric, verify output, and defend the query after an AI tool could have drafted it.

Data analyst SQL interviews use three formats and score four habits

Expect one of three formats: a live browser-coding round with an interviewer watching, a timed online SQL assessment, or a take-home query with a written explanation. SQLPad's interview guide describes those formats and notes that SQL rounds often pair query-writing with interpretation of the result (SQLPad, retrieved 2026-07-25). The scoring rubric is broader than "does the query run." You are judged on problem framing, stated assumptions, readable structure, and debugging when the first result looks wrong. Before typing, clarify the metric, grain, date window, and excluded rows. While typing, use aliases and CTEs that make your logic auditable. After typing, sanity-check row counts and edge cases.

KnowledgeCheck: An interviewer says: "Find the top 3 customers by spend last month." Before writing SQL, what two business definitions do you clarify?

Answer: clarify whether "spend" means gross order value, net revenue after refunds, or payments received, and whether "last month" means the previous calendar month, the company's fiscal month, or a rolling 30-day window.

JOIN questions test whether you understand the grain of the data

JOIN questions ask you to combine tables without changing the meaning of the result. A typical prompt is: "Find each customer's total revenue and include customers with no orders." The interviewer is checking whether you choose LEFT JOIN instead of accidentally dropping non-buyers, whether you join on the right key, and whether you notice one-to-many duplication. SQLTutorial's cheat sheet lists inner, left, right, full outer, cross, and self joins as core query patterns (SQLTutorial, retrieved 2026-07-25). In an interview, say the grain out loud: "one row per customer" or "one row per order." That sentence prevents many wrong answers.

GROUP BY and HAVING questions test metric definitions

GROUP BY questions ask you to turn rows into business summaries. A typical prompt is: "Show products with more than 100 orders last month." The interviewer is checking whether you group at the correct level and whether you use HAVING for aggregate filters instead of WHERE. SQLTutorial shows GROUP BY for applying aggregate functions to groups and HAVING for filtering those groups after aggregation (SQLTutorial, retrieved 2026-07-25). Clarify whether "orders" means all orders, paid orders, completed orders, or distinct customers. In analyst interviews, the metric definition often matters more than the keystrokes.

Aggregate questions test whether you can summarize without hiding defects

Aggregate questions use COUNT, SUM, AVG, MIN, and MAX to answer questions such as "What was average order value by channel?" StrataScratch calls out aggregate functions as a common interview area, and its SQL examples combine aggregates with grouping, ordering, and real-world business prompts (StrataScratch, retrieved 2026-07-25). The hidden test is whether you count the right thing. COUNT(*) counts rows; COUNT(user_id) skips NULL user IDs; COUNT(DISTINCT user_id) answers a different question again. Say which one you chose and why.

Window function questions test ranking, comparisons, and "top per group"

Window function questions ask you to keep row detail while calculating across related rows. A typical prompt is: "Find the top two products by revenue in each category." PostgreSQL documents window functions as calculations across rows related to the current row, while keeping separate row identities, and shows row_number() with PARTITION BY and ORDER BY (PostgreSQL, retrieved 2026-07-25). StrataScratch also lists window functions with CTEs and subqueries for analyst SQL prep (StrataScratch, retrieved 2026-07-25). Practice ROW_NUMBER, RANK, DENSE_RANK, LAG, and running totals.

CTE questions test whether you can structure an answer under pressure

CTE questions test readability and decomposition. A prompt may ask for "users whose second purchase happened within seven days of their first." You can solve it in one dense query, but a CTE lets you name steps: first orders, second orders, final comparison. StrataScratch describes a CTE as a temporary result set that can be named and reused by another query (StrataScratch, retrieved 2026-07-25). In interviews, CTEs are useful because the interviewer can follow your thinking. They also make debugging easier when a count looks wrong.

Subquery questions test filtering logic

Subquery questions ask you to use one query's result inside another. A common prompt is: "Find employees who earn more than the company average" or "List customers who bought a product from a target category." StrataScratch defines subqueries as queries inside another query, appearing in SELECT, FROM, or WHERE clauses (StrataScratch, retrieved 2026-07-25). The interviewer is checking whether you understand when the inner query returns one value, many values, or a derived table. If the logic becomes hard to narrate, rewrite it as a CTE.

Date function questions test business calendar thinking

Date questions ask whether you can filter, bucket, and compare time periods. Expect prompts such as "daily active users in the last 30 days," "month-over-month revenue," or "users retained seven days after signup." The syntax varies by database, so the important move is clarifying the time zone, inclusive dates, and whether the company uses calendar months, fiscal months, or rolling windows. SQLTutorial lists date functions as a core SQL function category, but interviewers usually care more about the boundary logic than the exact function name (SQLTutorial, retrieved 2026-07-25).

CASE questions test segmentation and business rules

CASE questions ask you to encode business categories directly in SQL. A prompt may say: "Classify customers as new, returning, or dormant" or "Bucket orders into low, medium, and high value." StrataScratch describes CASE as SQL's way to apply IF-THEN style logic (StrataScratch, retrieved 2026-07-25). The interviewer is checking whether your rules are mutually exclusive and ordered correctly. Ask what should happen on boundary values: if high value starts at $500, does exactly $500 count as high?

NULL questions test whether your result can be trusted

NULL handling questions test whether you know missing values are not the same as zero. A typical prompt is: "Find customers with no purchases" or "Calculate conversion rate when some users have no events." SQLTutorial includes IS NULL, IS NOT NULL, COALESCE, and NULLIF in its reference areas (SQLTutorial, retrieved 2026-07-25). The interviewer is checking whether joins, counts, averages, and division behave as intended. In analyst work, NULLs are often where inflated dashboards and false conclusions start.

Recurring patterns matter more than memorized questions

Most SQL interview questions are variations on five patterns. Top-N questions use ranking or ordering. Deduplication questions use ROW_NUMBER() and a partition key. Running totals use windowed SUM. Retention and funnel questions join events across time. Period-over-period questions compare date buckets. If you can name the pattern before writing code, the query becomes less intimidating.

Runnable example: paste this into SQLite or another SQL runner that supports common table expressions and window functions. It finds each customer's first two orders and flags repeat purchases within seven days.

``sql WITH orders(customer_id, order_id, order_date) AS ( VALUES (1, 101, '2026-07-01'), (1, 102, '2026-07-05'), (2, 201, '2026-07-01'), (2, 202, '2026-07-20'), (3, 301, '2026-07-03') ), ranked AS ( SELECT customer_id, order_id, order_date, ROW_NUMBER() OVER ( PARTITION BY customer_id ORDER BY order_date ) AS order_rank FROM orders ), first_two AS ( SELECT customer_id, MAX(CASE WHEN order_rank = 1 THEN order_date END) AS first_order_date, MAX(CASE WHEN order_rank = 2 THEN order_date END) AS second_order_date FROM ranked GROUP BY customer_id ) SELECT customer_id, first_order_date, second_order_date, CASE WHEN second_order_date IS NOT NULL AND julianday(second_order_date) - julianday(first_order_date) <= 7 THEN 'repeat_within_7_days' ELSE 'not_repeat_within_7_days' END AS repeat_status FROM first_two ORDER BY customer_id; ``

KnowledgeCheck: Before running this query in a live interview, what ambiguity should you clarify?

Answer: clarify whether "within seven days" includes the seventh day, whether order dates use UTC or local time, and whether cancelled or refunded orders should count. Those assumptions change the business answer even when the SQL syntax is valid.

2026 interviews score judgment because AI can draft the query

In 2026, interviewers assume candidates can use AI. dbt Labs' 2026 report says AI-assisted coding is embedded in development workflows and frames analytics engineering around trust, quality, ownership, and governance (dbt Labs 2026, retrieved 2026-07-25). dbt Labs' 2025 press release reported that 80% of surveyed data practitioners used AI in their daily workflow (dbt Labs via PRNewswire, retrieved 2026-07-25). Stack Overflow's 2025 survey adds the caution signal: 46% of developers actively distrusted AI output accuracy, while SQL remained one of the most-used languages at 58.6% (Stack Overflow, retrieved 2026-07-25).

So do not say, "I never use AI." Say: "I use AI for a first-pass query or alternative approach, then I verify the join grain, sample rows, NULL behavior, date boundaries, and final metric." That is the modern answer. The interviewer is not only testing whether SQL runs; they are testing whether your result should be trusted.

FAQ

How much SQL is enough for a data analyst interview?

Enough SQL means you can solve medium analyst prompts across joins, grouping, aggregates, windows, CTEs, subqueries, dates, CASE, and NULL handling while explaining assumptions. SQL led programming-language requirements in 365 Data Science's 2026 analysis of 855 relevant US data analyst postings, appearing in 52.9% of postings (365 Data Science, retrieved 2026-07-25). Practice patterns, not a 100-question script.

Are window functions required for data analyst SQL interviews?

Window functions are not guaranteed in every round, but they are too useful to skip. They power top-per-group, deduplication, running totals, rank comparisons, and lag-based period changes. PostgreSQL documents window functions as row-preserving calculations across related rows, and StrataScratch includes them in analyst and data-science SQL prep (PostgreSQL, StrataScratch, retrieved 2026-07-25).

Is SQL alone enough to get hired as a data analyst?

No. SQL is often the first technical filter, but analyst hiring also looks for spreadsheets, BI tools, statistics, communication, and business judgment. The 2026 posting analysis that put SQL at 52.9% also found Excel, Power BI, Tableau, and broader analysis skills in the mix (365 Data Science, retrieved 2026-07-25). Pair SQL with one dashboard or memo.

Which SQL dialect should I practice?

Practice standard SQL patterns first, then adapt syntax to the employer or interview platform. PostgreSQL is a useful default because its documentation explains window functions clearly, but MySQL, SQL Server, and SQLite-style interviews all test similar ideas: joins, grouping, ranking, dates, and NULL handling (PostgreSQL, retrieved 2026-07-25). The transferable skill is choosing the right pattern and validating it.

Career funnel: turn SQL prep into a data analyst path

If SQL is your strongest signal, use it as a starting point, not the whole career plan. Read the broader data analyst interview prep guide, then compare your skills against the data analyst skill map. Starting from zero? Use the no-experience roadmap before drilling interview rounds.

For a personalized route, upload your CV to Career Compass. The wizard compares your current background with career tracks, shows the skill gaps, and points you toward the next course sequence instead of another generic question list.

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