Top 10 SQL Interview Questions for Data Engineers (2024 Update)

SQL is a must-have skill for data engineers. They use the querying language to perform essential tasks like modeling data, extracting performance metrics, and developing reusable data structures.

Data engineer SQL questions tend to mirror the work that engineers do.

Therefore, data engineers need to be proficient not just in querying data and pulling metrics, but also in data structures, manipulation and security within SQL. Broadly, a data engineer may face SQL questions in these categories:

• SQL queries - Using SQL data query language (DQL) statements to pull metrics and analyze data. Commands to know: SELECT
• Data modeling - Using DDL commands to create database schema and define data structures. Commands to know: CREATE, ALTER, DROP, RENAME, TRUNCATE, COMMENT
• Data manipulation - Using DML statements to retrieve and manipulate data. Commands to know: INSERT, UPDATE, DELETE, MERGE, CALL, EXPLAIN PLAN, LOCK TABLE
• Data security - Using DCL (data control language) commands to manage database security. Commands to know: GRANT, REVOKE

This data engineering SQL questions guide provides an overview of the types of the SQL data engineer interview questions you might face to help you prepare for your interview.

SQL interview questions for data engineers can vary from short-response explanations and definitions, to writing intermediate-to-advanced SQL queries. Here are the most frequently asked SQL topics:

• Database design - These questions assess your understanding of data architecture and design. Typically, they ask you to design databases from scratch, and include SQL data manipulation and data definition statements.
• ETL SQL questions - A frequently asked topic in data engineering interviews, these questions ask you to perform ETL tasks, e.g. extracting, transforming and loading data. These questions require proficiency in DDL and DML statements.
• Definition-based SQL questions - Short basic SQL questions like “What’s the difference between WHERE and HAVING?”
• Reporting and metrics - Writing queries to pull metrics. These questions will have a clearly defined metric to pull, and they can range from beginner to advanced.

Database design questions are common in SQL interviews for data engineering roles. You should be prepared to design the database schema for an application. One step further, you might be asked to write a query to produce a metric, based on the schema you proposed.

Some database design concepts to study for data engineering interviews include:

• Entity-relationship modeling
• Normalization and denormalization trade-offs
• Dimensional modeling

1. Design a database for a stand-alone fast food restaurant.

With this fast food restaurant database design question, expect to be asked to write queries for the data. In this question, you’re asked to write a SQL query for the top three items by revenue and the percentage of customers who order drinks with their meals.

2. Design a database for traffic on the Golden Gate Bridge.

This crossing bridges database design question asks you for some specifics, as well:

• What would the table schema look like if we wanted to track how long each car took coming into San Francisco to enter and exit the bridge?
• Write a query on the given tables to get the time of the fastest car on the current day.

3. How would you create a schema to represent client click data on the web?

The data schema question is more architecture-based and assesses experience within developing databases, setting up architectures, and in this case, representing client-side tracking in the form of clicks. A simple but effective design schema would be to first represent each action with a specific label. In this case, assigning each click event a name or label describing its specific action.

ETL questions are one of the most important topics covered in data engineering interviews, and a broad range of concepts get covered. You might get more definition/theory questions, as well as code writing tasks.

An important aspect to consider during SQL ETL interviews is the practical application of these concepts. For instance, a common scenario in the screener interview might involve using the WITH keyword to create common table expressions for better readability and maintaining a clear thought process.

Another key area is optimizing queries to reduce table scans, which is a critical factor in onsite interviews. Demonstrating proficiency in these practical applications is as important as theoretical knowledge.

Additionally some more concepts you want to focus on are:

• DDL, DML and DCL keywords
• JOINs
• SQL constraints
• Transactions
• Views
• Indexes
• Triggers
• Variables
• Query optimization

1. Write a query to get the current salary data for each employee.

You have a table representing company payroll schema. Due to an ETL error, the employees table isn’t properly updating salaries, but instead is doing an insert when performing compensation adjustments.

2. We want to create an aggregate table with the song count by date for each user. Write a SQL query that would make this ETL each day.

We have a table called song_plays that tracks each time a user plays a song. For this problem, we use the INSERT INTO keywords to add rows to the lifetime_plays table. If we set this query to run daily, it becomes a daily extract, transform, and load (ETL) process.

You’ll likely face SQL definition-based questions early in the interview process, because they’re used to quickly assess your skill level. For this type of question, it’s best to study definitions and learn how to define what’s going on in layman’s terms.

Although a range of topics come up, be sure you can quickly define:

• Common table expressions
• Window functions
• CASE and PIVOT
• WITH, UNION and HAVING
• Aggregate case statements
• Transitive closure
• Query plans
• Dialect differences

1. What are aggregate functions in SQL?

Aggregate functions in SQL are functions where values from multiple rows are merged to form a single value. Some of the most common ones are COUNT, MIN, MAX, SUM and AV

2. What is an index in SQL? When would you use an index?

Indexes are lookup tables used by the database to perform data retrieval more efficiently. An index can be used to speed up SELECT or WHERE clauses, but they do slow down UPDATE and INSERT statements.

3. What’s the difference between IN and BETWEEN?

In SQL, BETWEEN is used to test if a particular expression lies between a range of value. It returns all the values that lie within the range. The IN operator, on the other hand, tests whether the values expressed match those in a specific list.

Practice intermediate to advanced SQL reporting problems to prepare. These questions are usually clearly defined, e.g. write a query to return X metric, and they assess your ability to quickly write clean code.

This type of SQL question is so common because data engineers are tasked with building tables, ETLs, and aggregation views for visualizations and dashboards. You might also get an SQL case study question.

To practice, you should focus on:

• Complex joins
• Sub-queries
• Self-joins
• Window functions
• Aggregations

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

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the 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.

2. Write a query to return data to support or disprove the hypothesis that CTR is dependent on search rating.

In this search click-through-rate SQL problem, you’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5 where 5 is high relevance and 1 is low relevance.

The Data Engineering SQL Interview Process

In addition to understanding the technical aspects of SQL for data engineering, it’s crucial to grasp the structure and demands of the interviews themselves.

Big tech companies typically conduct two main types of SQL interviews: the screener and the onsite interview.

The Screener Interview: This initial round, lasting 45-60 minutes, tests basic SQL proficiency. Candidates might face questions involving WHERE conditions with GROUP BY, JOIN operations with aggregation, and window functions like RANK or ROW_NUMBER.

Using common table expressions with the WITH keyword can enhance readability and organization in your solutions. Lastly, self-joins or optimization-based questions may appear.

The Onsite SQL Interview: This more in-depth, 60-minute interview requires detailed and efficient solutions. You’ll be evaluated on your ability to use window functions, aggregate case statements, and perform rolling or cumulative operations. Proficiency in LEFT JOIN operations and understanding table scans and query optimization are also critical.

Common Mistakes in Data Engineering SQL Interviews

Premature Coding: One of the most critical mistakes is to jump directly into coding without fully understanding the problem. This approach often leads to unnecessary debugging and wasted time. Spend a few minutes clarifying the question and planning your approach.

Overreliance on Specific Features: Relying too heavily on UDFs (User-Defined Functions) or engine-specific code can be detrimental. Most interviews focus on basic SQL functionalities, like those found in MySQL or Postgres. Similarly, an overdependence on window functions without understanding their replication through self-joins can be a red flag.

Communication Breakdown: Not engaging with the interviewer is a common error. They are there to guide and assist you. Ignoring their hints or not communicating your thought process effectively can lead to misunderstandings and missed opportunities to showcase your problem-solving skills.

Effective Interview Strategies

Understanding Key SQL Functions: A profound understanding of SQL functions, especially those related to window functions, JOIN operations, and aggregation, is crucial. Know how and when to use different functions and be ready to explain your choices.

Optimizing for Performance: Big tech companies often emphasize the efficiency of SQL queries. Understanding how to reduce table scans and optimize query performance is vital. Demonstrating your knowledge of query plans, index usage, and table partitioning can give you an edge.

Interacting with the Interviewer: Remember, an interview is a two-way street. Being personable and treating the interviewer as a colleague rather than a mere evaluator can work wonders. A friendly demeanor, coupled with technical proficiency, can significantly impact the interviewer’s perception.

If you’d like to land a job in Data Engineering, Interview Query offers the following resources:

• The Data Engineering Learning Path to sharpen your skills and learn to tackle data engineering interview questions.
• The job board showing recent openings and company interview guides to help you prepare.
• The list of database design interview questions, as well as the list of SQL Interview Questions, to help you practice for your next interview.
• Individual coaching sessions with our experts.

Then, you can continue planning for your career in data engineering with our articles:

• How to Prepare for the Data Engineer Interview
• Top 100 data engineer interview questions.