Meta SQL Interview Questions 2025 | Sample Q&A + Prep Guide

SQL is one of the most commonly tested skills in Meta’s data science, analytics, and engineering interviews. In fact, candidates often report that SQL questions are a heavier focus in Meta interviews compared to other companies. That’s why preparing for the SQL section of Meta’s technical screen is non-negotiable—you can be sure it will come up.

Meta expects candidates to demonstrate a strong foundation in SQL, with the ability to write efficient, scalable queries and reason through data problems. The questions cover everything from basic joins to advanced window functions and real-world product metrics.

But with such broad expectations, it’s often tricky to know which SQL concepts to prioritize in your prep. That’s where this guide comes in.

We’ve compiled these frequently asked Meta SQL interview questions, collected from past Meta interviewees, along with detailed solutions and tips to help you crack the SQL round with confidence.

Preparing for Meta’s SQL interviews becomes much easier when you know what types of SQL questions to expect. Broadly, these questions fall into two main buckets:

1. Conceptual & definition-based questions

These are theory-based questions that assess your fundamental SQL knowledge. Expect quick-fire questions like:

• “What is a JOIN and when would you use it?”
• “What are window functions?”
• “Why do we use indexing?”

Clear, concise answers here build credibility and pave the way for more advanced problem-solving questions.

2. Query writing & problem-solving questions

The majority of Meta SQL interviews involve hands-on query writing. You’ll be given real-world business problems and datasets (often modeled around Facebook or Instagram products) to solve through SQL.

These query-based questions can be categorized into five core types:

SQL Category	Asked In Roles
Data Retrieval & Aggregation	Data Analyst, Data Scientist, Engineer
Window Functions & Advanced Joins	Data Scientist, Engineer
Product Metrics & Business Analysis	Data Scientist, Product Analyst
Experimentation & A/B Testing	Data Scientist, Product Analyst
Data Quality & Debugging Queries	Data Analyst, Engineer
Scalability & Query Optimization	Data Engineer, Software Engineer

a) Data retrieval and aggregation. You’ll be asked to extract and aggregate data across multiple tables—combining joins, group-bys, and filtering conditions to retrieve meaningful metrics like daily active users, retention rates, or session counts.

b) Window functions and advanced joins. Meta loves testing candidates on advanced SQL techniques like window functions (ROW_NUMBER, RANK, etc.) and multi-condition joins. These questions test your ability to navigate complex data relationships and sequence operations.

c) Product metrics & business problem solving. These problems assess your product sense and business intuition. You may need to calculate engagement metrics, detect anomalies, or analyze feature adoption using SQL queries. Understanding Meta’s business model is key here.

d) Experimentation & A/B testing analysis. For analyst and data science roles, SQL questions often focus on A/B test metrics, experiment design, and result interpretation. You’ll write queries to calculate lift, conversion rates, or segment-wise performance.

e) Data quality and debugging queries. Meta values candidates who can identify inconsistencies in data and debug faulty queries. You might be given a flawed dataset or buggy SQL code and asked to clean, validate, and correct it.

f) Scalability & query optimization (for engineering roles). For data engineering or infrastructure-focused roles, expect questions around optimizing queries for scale and performance. You’ll be tested on query refactoring, indexing strategies, and handling large-scale datasets efficiently.

Pro Tip

Meta interviewers often start with a simple SQL problem and gradually increase its complexity based on your answers. For example, they might first ask you to retrieve daily active users from a dataset. Once you solve that, they could follow up by asking you to:

• Segment by device type
• Calculate rolling 7-day averages using window functions
• Optimize your query for large datasets

The key is to think scalably and modularly—your queries should handle more than just the base case.

The depth and variety of SQL questions you’ll face at Meta depend largely on your target role. Some roles test SQL extensively through hands-on query challenges, while others may focus on conceptual understanding and optimization techniques.

Here’s what SQL looks like across different Meta roles:

Product Strategist

Expect SQL questions that assess your ability to extract actionable insights from product and user data. You’ll need to write queries that analyze KPIs, user behavior, and feature performance—often requiring nested queries and advanced aggregations.

Data Analyst

For analyst roles, SQL is foundational. Interviews will challenge you with realistic business problems that require joining multiple tables, filtering data, and building metrics that reflect user or business outcomes.

Data Engineer

Engineers at Meta face SQL questions focused on data processing efficiency, database management, and optimization. You’ll be expected to design queries that handle large-scale data and demonstrate an understanding of performance trade-offs.

Data Scientist (Analytics Track)

SQL is a must-have for data scientists working on product analytics, experimentation, or growth teams. You’ll solve end-to-end problems, from writing queries to extract data to interpreting results and delivering business insights.

In technical screens, candidates typically face 1-2 SQL challenges, with on-site rounds featuring more complex scenario-based SQL tasks, especially for analytics-heavy roles.

Meta’s SQL interviews typically begin with a few conceptual or definition-based questions to assess your foundational knowledge. These help interviewers quickly gauge how well you understand core database concepts. Once past this phase, the interview shifts towards practical SQL coding problems that simulate real business challenges faced by Meta teams. Below, we’ve compiled examples of both categories—starting with core definitions and moving into query-based problem-solving.

1. What is a foreign key in SQL? What role does it play?

A foreign key is a field or multiple fields in one table that can refer back to the primary key in another. The table with the primary key is referred to as the parent or referenced table, while the one with the foreign key is the child table. Foreign keys are used to provide the referential integrity between tables.

2. Are blank spaces or zero values treated the same as NULL?

No, NULL is not the same as a zero or blank space. Instead, NULL is used in the absence of any value. In other words, the value is unavailable, unknown, unassigned or not appropriate. Zeroes and blank spaces are treated differently, and they can be compared to other zeroes or blank spaces, respectively. NULL cannot be compared to another NULL.

3. What is meant by SQL injection? What would be the first step in preventing or mitigating an attack?

SQL injection, also known as SQLI, is a type of vulnerability that uses malicious SQL code to give attacks access to the backend database. Attackers can then manipulate the database and gain access to private information like user lists and customer details.

One of the first steps would be input validation, to identify the illegitimate user inputs.

4. What’s the difference between IN and BETWEEN operators?

Both allow you to find multiple values from the table. But a key difference is the type of data you are selecting with these operators. For example, BETWEEN selects a range of data between two values. Alternatively, IN allows you select multiple values.

5. What is a cursor?

In SQL, a cursor is a temporary memory or workstation. They store database tables, and in SQL there are two main types:

1. Implicit cursors- Implicit cursors are allocated by the SQL server when users perform DML operations.
2. Explicit cursors- Explicit cursors are based on user inputs.

6. Explain what a trigger is in SQL. When would you use a trigger?

A trigger is a stored procedure that automatically runs when a certain event occurs in the database server. For example, DML triggers run when a user tries to modify data. Triggers can be used to:

• Audit database activity
• Implement rules
• To enforce referential integrity

In the technical screen, you’ll be asked a range of query-based SQL questions. These will ask you to write queries based on provided datasets. The best prep strategy is to practice and write lots of sample queries aligned to real case studies. Here are questions that will help you develop that skill:

7. Write a SQL query to create a histogram of the number of comments per user in January 2020. Assume bin buckets class intervals of one.

users table

Columns	Type
id	INTEGER
name	STRING
created_at	DATETIME
neighborhood_id	INTEGER
mail	STRING

comments table

Columns	Type
user_id	INTEGER
body	VARCHAR
created_at	DATETIME

Hint: Since a histogram is just a display of frequencies of each user, all we really need to do is get the total count of user comments in the month of January 2020 for each user, and then group by that count.

8. In the table below, column action represents either (‘post_enter’, ‘post_submit’, ‘post_canceled’) for when a user starts a post (enter), ends up canceling it (cancel), or ends up posting it (submit). Write a query to get the post success rate for each day in the month of January 2020.

events table

Columns	Type
id	INTEGER
user_id	INTEGER
created_at	DATETIME
action	STRING
url	STRING
platform	STRING

Hint: Let’s see if we can clearly define the metrics we want to calculate before just jumping into the problem. We want post success rate for each day over the past week. How would we get that metric?

We can assume post success rate can be defined as: (total posts created) / (total posts entered)

Additionally, since the success rate must be broken down by day, we must make sure that a post that is entered must be completed on the same day.

Now that we have these requirements, it’s time to calculate our metrics. We know we have to GROUP BY the date to get each day’s posting success rate. We also have to break down how we can compute our two metrics of total posts entered and total posts actually created.

9. Write a SQL query to create a metric to recommend pages for each user based on recommendations from pages their friends have already liked.

More Context: You want to build a naïve recommender, and you are given two tables. One is called friends (user_id and friend_id) and another called page_likes (with user_id and page_id). Note: it shouldn’t recommend pages that the user already likes.

Let’s solve this Meta SQL interview question by visualizing what kind of output we want from the query. Given that we have to create a metric by which to evaluate recommending a user to a page, we know we want something with a user_id and a page_id along with some sort of recommendation score.

Let’s try to think of an easy way to represent the scores of each user_id and page_id combo. One naïve method would be to create a score by summing up the total likes by friends on each page that the user has not currently liked. Then the max value on our metric will be the most recommendable page.

The first thing we have to do is then to write a query to associate users to their friends’ liked pages. We can do that easily with an initial join between the two tables.

WITH t1 AS (
    SELECT 
        f.user_id 
        , f.friend_id
        , pl.page_id 
    FROM friends AS f
    INNER JOIN page_likes AS pl
        ON f.friend_id = pl.user_id
)

What comes next to reaching the desired output?

10. Given three tables representing customer transactions and customer attributes, write a query to get the average order value by gender.

transactions table

Columns	Type
id	integer
user_id	INTEGER
created_at	DATETIME
product_id	INTEGER
quantity	INTEGER

users table

Columns	Type
id	INTEGER
name	VARCHAR
sex	VARCHAR

products table

Columns	Type
id	INTEGER
name	STRING
price	FLOAT

11. Say our example output from the query in Question 4 is:

Men

• AOV: $46.3
• Total purchases: 2500
• Unique purchasers: 1500

Women

• AOV: $50.2
• Total purchases: 3500
• Unique purchasers: 500

Would the difference in AOV (average order value) be significant?

Note: Remember to round your answer to two decimal places.

12. You’re given a table that represents search results from searches on Meta. The query column is the search term, position column represents each position the search result came in, and the rating column represents the human rating of the search result from 1 to 5 where 5 is high relevance and 1 is low relevance.

Part 1:

Write a query to compute a metric to measure the quality of the search results for each query.

This is an unusual SQL problem, given it asks to define a metric and then write a query to compute it. Generally, this should be pretty simple. Can we rank by the metric and figure out which query has the best overall results?

For example, if the search query for ‘tiger’ has 5s for each result, then that would be a perfect result.

The way to compute that metric would be to simply take the average of the rating for all of the results. In which the query can very easily be:

SELECT query, ROUND(AVG(rating), 2) AS avg_rating 
FROM search_results 
GROUP BY 1

Part 2: You want to be able to compute a metric that measures the precision of the above ranking system based on position. For example, if the results for dog and cat are:

query	result_id	position	rating	notes
dog	1000	1	2	picture of hotdog
dog	998	2	4	dog walking
dog	342	3	1	zebra
cat	123	1	4	picture of cat
cat	435	2	2	cat memes
cat	545	3	1	pizza shops

We would rank ‘cat’ as having a better search result ranking precision than ‘dog’ based on the correct sorting by rating.

Write a query to create a metric that can validate and rank the queries by their search result precision. Round the metric (avg_rating column) to 2 decimal places.

The precision metric is a little more difficult now that we have to account for a second factor, which is position. We now have to find a way to weight the position in accordance to the rating to normalize the metric score.

This type of problem set can get very complicated if we wanted to dive deeper into it. However, the question is clearly more marked towards being practical in figuring out the metric and developing an easy SQL query, rather than developing a search ranking precision scale that optimizes for something like CTR.

To solve the problem, it’s helpful to look at the example to construct an approach towards a metric. For example, if the first result is rated at 5 and the last result is rated at a 1, that’s good. Even better, however, is if the first result is rated 5 and the last result is also rated 5. Bad is if the first result is 1 and the last result is 5.

However, if we use the approach from Part 1, we’ll get the same metric score no matter which way the values are ranked by position. So how do we factor position into the ranking?

13. Given the following tables, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

employees table

Columns	Types
id	int
first_name	VARCHAR
last_name	VARCHAR
salary	INTEGER
department_id	INTEGER

departments table

Columns	Types
id	INTEGER
name	VARCHAR

Let’s approach this problem by looking at the output of what the response would look like.

We know that we need to calculate the total number employees that are making over $100K by each department. This means that we’re going to have to run a GROUP BY on the department name since we want a new row for each department.

We also need a formula to represent how we can differentiate employees that make over $100K and those that make less. We can calculate that by formulating:

(Number of people making over $100K) / (Total number of people in that department)

Now in terms of implementation, if we first do a JOIN between the employees table and the departments table, then we can get all of the data points we need together. Then all that is left is:

• a function to get all of the employees
• a function to get all employees making over 100K
• dividing those values by each other

14. We’re given two tables, a users table with demographic information and the neighborhood they live in and a neighborhoods table.

users table

Columns	Types
id	INTEGER
name	VARCHAR
neighborhood_id	INTEGER
created_at	DATETIME

neighborhoods table

Columns	Type
id	INTEGER
name	VARCHAR
city_id	INTEGER

Write a query that returns all of the neighborhoods that have 0 users.

Whenever the question asks about finding values with 0 something (users, employees, posts, etc..) immediately think of the concept of LEFT JOIN! An inner join finds any values that are in both tables, a left join keeps only the values in the left table.

Our predicament is to find all the neighborhoods without users. To do this, we must do a left join from the neighborhoods table to the users table, which will give us an output like this:

neighborhoods.name	users.id
castro	123
castro	124
cole valley	null
castro heights	534
castro heights	564

If we then add in a where condition of WHERE users.id IS NULL, then we will get every single neighborhood without a singular user.

15. Given a table of user sessions, write a query to get the number of days between each user’s first session and last session for the year 2020.

Let’s use SQL’s DATEDIFF function to calculate the difference between a user’s first and last session dates. We’ll use the MIN and MAX functions to identify these dates and restrict our query to sessions in 2020 with a WHERE clause using the YEAR function. Finally, we’ll GROUP BY user_id to get a summary row for each user.

16. We want to calculate the total spend for IT, HR, and Marketing, and also have a total for Other departments, grouped by fiscal quarters. Write a query to display this result.

Note: Display only quarters where at least one transaction occurred. Quarter names should be Q1,Q2,Q3 and Q4. Q1 is from January to March.

Categorize transaction dates into fiscal quarters, with Q1 covering January to March, Q2 from April to June, Q3 from July to September, and Q4 from October to December. Calculate the total spending for each department—IT, HR, and Marketing—within these quarters. Sum any transactions from other departments into a separate “Other” category. Group the results by quarter to provide a summary of departmental spending across the fiscal year.

You’ll notice a recurring theme in Meta’s SQL interviews: combining business metrics with SQL techniques like GROUP BY, CASE WHEN statements, and advanced joins. The key to acing these questions is not just writing functional queries but explaining your thought process, assumptions, and handling edge cases. Interviewers are interested in how you approach ambiguous problems, optimize your queries, and derive business insights—not just syntax correctness.

Most candidates expect Meta SQL interviews to be about syntax and writing queries. In reality, Meta evaluates your problem-solving approach more than your SQL fluency. The difficulty doesn’t come from writing a complex JOIN—it comes from how well you handle vague problems, identify missing information, and connect data to business impact.

Here’s how Meta quietly tests you beyond SQL:

1. Assumptions under pressure

Meta interviewers often withhold details intentionally. They want to see if you can:

• Ask the right clarifying questions.
• Make realistic assumptions.
• State your assumptions out loud as you code.

2. Business-driven querying

It’s not enough to write “correct” queries. You need to explain:

• Why you’re querying certain metrics.
• How the results tie back to user behavior or business outcomes.
• What next steps would you recommend if this were a live project.

3. Handling data edge cases

Expect follow-ups like:

• “What if this dataset has NULLs?”
• “What happens if 20% of users have no activity?”
• “How would you modify your query if the table scales to a billion rows?”

4. Mental debugging

Interviewers will nudge you with “Are you sure this query works?” even if it’s syntactically correct. They’re testing your debugging mindset, not just correctness.

At Meta, SQL is the vehicle; structured problem-solving is the driver. Knowing syntax is table stakes. The real challenge is navigating open-ended data problems, making smart assumptions, and translating SQL outputs into business insights on the fly.

Meta’s SQL interviews are not just about writing queries—they’re testing how you think through data problems in real time. Here’s exactly how to approach them:

1. Clarify before you code

• Always start by asking clarifying questions about metrics, data availability, and assumptions.
• Don’t assume the interviewer will provide missing information—you have to ask.
• If data gaps remain, state your assumptions explicitly.

2. Think in terms of business impact

• Before jumping into SELECT, ask yourself: “What decision will this query inform?”
• Relate metrics to user behavior, product KPIs, or operational efficiency.
• Your answer isn’t done until you’ve explained how the insight helps the business.

3. Prioritize query structure, then syntax

• First, explain the logical flow of your query (joins, filters, groupings) out loud.
• Then, code it.
• Meta interviewers care more about your structured approach than perfect syntax.

4. Preempt edge cases

• After coding, mention checks like: “I’ll add a WHERE condition to handle NULLs.”
• Bring up considerations like indexing columns for scalability.
• This shows foresight and practical awareness.

5. Stay calm with follow-ups

• Expect pushback like: “What if the dataset is 10x larger?” or “How would you optimize this?”
• Focus on high-level strategies (indexing, limiting data early, partitioning) instead of worrying about exact code changes.

How hard is the Meta interview?

Meta interviews are known for their difficulty, especially for technical roles like data analysts, data scientists, and engineers. The SQL questions are designed to assess not only your query-writing skills but also your ability to analyze business problems, structure ambiguous data scenarios, and communicate insights effectively. It’s less about rote memorization and more about problem-solving under pressure.

What is the Facebook SQL language?

Facebook primarily uses a variant of SQL called Presto (now known as Trino) for querying large-scale datasets. Presto is an open-source distributed SQL query engine designed for interactive analytics on big data. For interview preparation, standard SQL syntax is sufficient, but understanding how queries scale and work in distributed environments is an added plus.

How do I prepare for a SQL interview?

Start by mastering foundational SQL concepts like joins, aggregations, filtering, and window functions. Then, move on to practicing real-world business problems that require translating ambiguous requirements into structured queries. Mock interviews, take-home assignments, and SQL problem sets from platforms like Interview Query can help simulate the actual interview environment. Focus equally on writing clean queries and explaining your thought process.

How does Meta use SQL?

At Meta, SQL is extensively used for data analysis, experiment tracking, business metrics reporting, and building data pipelines. Analysts and data scientists use SQL to extract insights from large datasets, while engineers use it for database operations and system performance optimization. SQL is often paired with other languages like Python for deeper statistical analysis or automation.

Is it easy to get hired by Meta?

Meta’s hiring process is competitive and thorough. For analytics roles, a strong grasp of SQL, product sense, and business acumen is essential. The interview process involves multiple rounds, including technical screens, case studies, and behavioral interviews. With targeted preparation—especially through practice problems and mock interviews—you can significantly improve your odds.

What backend does Meta use?

Meta’s backend infrastructure is built on a combination of in-house technologies and open-source systems. For data processing, they heavily rely on Presto (Trino), Hadoop-based systems, and other distributed computing platforms. Meta has also developed proprietary tools and databases optimized for scalability and performance to handle their massive data workloads.

Do you run code in a Meta interview?

For SQL interviews at Meta, you typically won’t be expected to run code in a live environment. Instead, you’ll be asked to write queries on a shared document or whiteboard, focusing on logic, structure, and problem-solving approach. In some cases, especially for take-home assignments or technical screens, you may write runnable SQL code and provide outputs or expected results.

How do you answer “why do you want to work at Meta?”

The most effective answer is personalized and connects your career goals to Meta’s mission. Highlight specific products or initiatives at Meta that resonate with you, and explain how your skills in data analytics or problem-solving can contribute to their impact. Show enthusiasm for working on large-scale problems and mention cultural aspects like collaboration and innovation that attract you to Meta.

How should you answer “what is your biggest weakness” in a data interview?

Choose a weakness that shows self-awareness and growth. For instance, you could say: “Earlier in my career, I tended to focus too much on perfecting SQL queries without considering scalability. Over time, I’ve learned to prioritize both efficiency and maintainability in my solutions.” The key is to demonstrate how you’ve taken steps to address the weakness.

In nearly all Meta roles, including Meta data science internships, SQL interview questions are an essential part of the interview. Prep for these questions with a solid study of the basics. You need a strong foundation of SQL expertise to ace this part of the technical screen. But you’ll also want plenty of experience writing advanced queries as well, especially case-based SQL questions.

Looking for more topics like this? Visit our blog! We talk about everything from PostgreSQL interview questions to machine learning case studies and articles on careers in data science.

Discover more with the resources at Interview Query!