Overview
Whether you’re interviewing for a data engineer role or just interested in learning more about it, this article will cover the ins and outs of the data engineer role, responsibilities, and how to ace the data engineer interview.
Data engineer interviews are becoming increasingly popular as the demand for data-driven decision-making grows by companies such as Amazon.
To help you learn the ins and outs of the data engineer interview process, we’ve researched and compiled the top 31 data engineer interview questions. These questions are from real-life interview experiences, and they cover the essential skills to pass data engineer interviews, including:
- Behavioral data engineer interview questions
- Database management interview questions
- Data engineer SQL interview questions
Want to practice real FAANG SQL interview questions? Get them here
But, before we get started with the behavioral questions, it’s important to first understand the responsibilities of a data engineer.
The data engineer’s responsibilities will vary from company to company, but in general, it consists of designing, implementing, and supporting the data infrastructure by providing access to large datasets and computing power. The data engineer will work to extract, transform, and load data from a wide variety of data sources using SQL. From a soft skills perspective, the candidate must possess strong verbal and written communication skills and be able to work with both non-technical and technical audiences.
Behavioral Data Engineer Interview Questions
There’s no shortcut to success. If you want to ace your interview, you’ve got to put in the time to vigorously prepare.
1. Why do you want to join this company?
Answer: My interest in data engineering began when I was working as a software engineer. I found that I was really interested in the process of designing, implementing, and supporting the data infrastructure. When I researched more about the role of a data engineer, I realized that it was the perfect combination of my technical skills and my interest in working with data. I want to join this company because I believe that it is at the forefront of data-driven decision-making. I am impressed by the company’s use of data to improve its products and services, and I believe that I can contribute to the company’s success in this area.
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2. Tell me about a time you improved a data-related process?
Answer: When I was working as a software engineer, I was tasked with designing and implementing a data ingestion pipeline that would take data from multiple sources and load it into a central data warehouse. The data sources were disparate and the data formats were inconsistent. I designed a process that used a combination of ETL and ELT techniques to cleanse and normalize the data. This process improved the data quality and made it easier for analysts to access the data.
3. Tell me about a time when you had to explain a technical concept to a non-technical audience?
Answer: When I was redesigning our ETL process, I had to get resource approval from our leadership team. I created a presentation that explained the technical details of the process in layman’s terms. I also prepared a demo that showed how the process worked. This allowed the non-technical members of the team to understand the process and give their approval.
4. What have you learned in your current role?
Answer: In my current role, I have learned how to design and implement data architectures that are scalable and efficient. I have also gained experience working with a variety of data sources, including structured and unstructured data. I have also learned how to use big data technologies, such as Hadoop and Spark, to process and analyze large datasets.
5. What was the maximum size of data that you have analyzed so far?
Answer: In my current role, I have processed and analyzed datasets that are up to {{10}} GB in size.
6. Can you tell me about your current projects?
Answer: I am currently working on a project to {{migrate our data warehouse from an on-premise solution to a cloud-based solution}}. I am also working on a project to improve our {{ETL pipeline}}. I am redesigning the process to make it more efficient and scalable.
7. What is the difference between star and snowflake schema?
Answer: Star schema is a type of data warehouse schema that is optimized for query performance. The snowflake schema is a type of data warehouse schema that is normalized for storage efficiency.
8. Can you explain the Simpson’s Paradox to me?
Answer: The Simpson’s Paradox is a statistical phenomenon that occurs when the overall trend of a dataset is reversed when the data is grouped by another variable.
9. What is the goal of data engineering?
Answer: The goal of data engineering is to design, build, and maintain efficient and reliable data infrastructure. Data engineers work with data architects to design the data architecture. They also work with software engineers to build the data infrastructure. Data engineers also work with data analysts and data scientists to ensure that the data infrastructure is able to support the needs of the business.
10. What do you want to get out of this data engineering role?
Answer: I want to gain experience working with a variety of data sources and technologies. I also want to learn how to design and implement efficient and scalable data architectures. Additionally, I want to contribute to the success of the company by helping to make data-driven decisions.
11. What do you think sets your experience apart from other candidates?
Answer: I have a strong technical background in data engineering. I also have experience working with a variety of data sources and technologies. Additionally, I have a proven track record of designing and implementing efficient and scalable data architectures.
12. What are your long-term career aspirations in data engineering?
Answer: I aspire to become a Data Architect. In this role, I would be responsible for designing the data architecture for the company. I would also work with the software engineers to build the data infrastructure. Additionally, I would work with the data analysts and data scientists to ensure that the data infrastructure is able to support the needs of the business.
Database Management Interview Questions
13. What is a relational database?
Answer: A relational database is a type of database that stores data in tables. Tables are composed of rows and columns. Data in relational databases is organized into relations, which are similar to tables.
14. What is data skewness?
Answer: Data skewness is a statistical phenomenon that occurs when the distribution of a dataset is not symmetrical. Data skew can impact the performance of data processing and analysis.
15. What are the different file storage formats and how do you know when to use them?
Answer: The different file storage formats are text, CSV, JSON, XML, and binary. Each file storage format has its own advantages and disadvantages. The format that you choose should be based on the needs of your project.
16. Describe a time you had difficulty merging data. How did you solve this issue?
Answer: I once had difficulty merging data because the data sets were in different formats. I solved this issue by using a data transformation tool to convert the data into the same format.
17. How would you design a data warehouse given limited resources?
Answer: I would design a data warehouse by considering the needs of the business. I would also consider the size of the data sets and the resources that are available. I would then choose the appropriate storage format and file structure.
18. What is a columnar database? How is it different from a relational database?
Answer: A columnar database is a type of database that stores data in columns. Columnar databases are designed for data warehousing and data analysis. They are different from relational databases because they are optimized for query performance.
19. What are the different types of SQL statements?
Answer: The different types of SQL statements are DDL, DML, and DCL.
- DDL statements are used to create and modify tables.
- DML statements are used to query and update data.
- DCL statements are used to control access to the database.
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20. How would you normalize a database?
Answer: To normalize a database, I would first identify the functional dependencies. I would then create a table for each functional dependency. I would then create relationships between the tables.
21. What is your experience with Data Modeling?
Answer: I have experience working with Data Modeling. I have used Data Modeling to create conceptual, logical, and physical data models. I have also used Data Modeling to reverse engineer data models.
22. What is your experience with Data Mining?
Answer: I have experience working with Data Mining. I have used Data Mining to discover hidden patterns and trends in data.
Data Engineer SQL Interview Questions
23. Write a query to retrieve all the customer_name(s) (ordered) whose transactions have a -10 second gap from each other.
Table: customer_transactions +---+-------------+-------------------+------------------+ |id |customer_name|transaction_time |transaction_amount| +---+-------------+-------------------+------------------+ |1 |A |2022-02-21 15:21:10|533 | |2 |B |2022-02-21 15:21:20|234 | |3 |D |2022-02-21 15:21:25|789 | |4 |D |2022-02-21 15:21:45|34 | |5 |F |2022-02-21 15:21:40|12 | |6 |A |2022-02-21 15:22:05|445 | |7 |B |2022-02-21 15:22:15|236 | |8 |C |2022-02-21 15:22:30|643 | |9 |F |2022-02-21 15:22:40|563 | |10 |C |2022-02-21 15:22:60|876 | +---+-------------+-------------------+------------------+
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Output
+--------------+ |customer_name | +--------------+ |A | |B | |C | |F | +--------------+
Answer:
Step 1: First, we’ll measure the amount of seconds between each transaction:
select
customer_name
,date_part('seconds'
,transaction_time
-
lag(transaction_time) over (
partition by customer_name
order by transaction_time asc
)
) seconds_between
from customer_transactions
- We are using the DATE_PART() function to determine the amount of seconds between two dates/times.
- We use the LAG() window function to find the previous transaction time to each transaction time record.
- PARTITION BY the customer_name so that the window is specific to each customer.
- ORDER BY transaction_time ASC so that time is in the normal, temporal order.
- We perform a calculation between the current record’s transaction time and the previous transaction time (per the LAG() that we built) and measure in seconds using DATE_PART().
Step 2: Putting Step 1 in a CTE (called “measured”), we can select only the records where our new, calculated seconds_between is within the requested criteria:
select customer_name from measured where seconds_between<=10 order by customer_name asc;
Logic
- The WHERE clause specifies the criteria from the requirement.
Final Query
with measured as (
select
customer_name
,date_part('seconds'
,transaction_time
-
lag(transaction_time) over (
partition by customer_name
order by transaction_time asc
)
) seconds_between
from customer_transactions
)
select customer_name
from measured
where seconds_between<=10
order by customer_name asc;
24. Write SQL to insert a new date’s data into a datestamp-partitioned table using incremental data from the other date partitions.
Answer:
INSERT INTO table_name (date, data)
SELECT date, data
FROM other_table
WHERE date > ‘YYYY-MM-DD’
AND date < ‘YYYY-MM-DD’;
25. How would you create a view in SQL?
Answer:
CREATE VIEW view_name AS
SELECT column_name
FROM table_name;
26. What is a primary key? How would you create one in SQL?
Answer: A primary key is a column or set of columns that uniquely identify a row in a table. A primary key can be created in SQL using the
CREATE TABLE statement. For example, the following SQL statement creates a table with a primary key on the “id” column:
CREATE TABLE table_name (
id INTEGER PRIMARY KEY,
column_name1 data_type,
column_name2 data_type,
…
);
27. What is a foreign key? How would you create one in SQL?
Answer: A foreign key is a column or set of columns that contains a reference to a primary key in another table. A foreign key can be created in SQL using the CREATE TABLE statement. For example,
CREATE TABLE table_name
(
column1 datatype,
column2 datatype,
CONSTRAINT fk_column
FOREIGN KEY (column1) REFERENCES other_table (column2)
);
28. What is a join? How would you create one in SQL?
Answer: A join is a SQL statement that allows you to combine data from two or more tables. Joins are created using the JOIN keyword. For example, the following SQL statement would join the “customers” and “orders” tables:
SELECT * FROM customers JOIN orders ON customers.id = orders.customer_id;
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29. How do joins work and what is an example of an inner join?
Answer: Joins work by matching the values in the columns of two or more tables. An inner join is a type of join that returns only rows that have matching values in the columns of both tables. For example,
SELECT * FROM customers JOIN orders ON customers.id = orders.customer_id;
This SQL statement would return all rows from the “customers” and “orders” tables where the id column in the “customers” table matches the customer_id column in the “orders” table.
30. What is a self-join? How would you create one in SQL?
Answer: A self-join is a join between two copies of the same table. Self-joins are created using the JOIN keyword. For example, the following SQL statement would join the “customers” table to itself:
SELECT * FROM customers c1 JOIN customers c2 ON c1.id = c2.id;
This SQL statement would return all rows from the “customers” table where the id column in one row matches the id column in another row.
[Bonus] Data engineer Python interview question
31. Write a function that can break a large SMS message string given a length limit per substring. Words must stay together. If a word is longer than the limit, use the word in a new substring and split it as relevant.
Example input: “Joe is the funniest guy”, 6
Example output: [“Joe is”, “the”, “funnie”, “st guy”]
Answer:
def sms_messages(sms_text_str, limit_int):
messages = []
current_message = “”
for word in sms_text_str.split():
if len(word) + len(current_message) <= limit:
current_message += word + ” “
else:
messages.append(current_message)
current_message = word + ” “
messages.append(current_message)
return messages
In conclusion,
We covered the behavioral, database management, and technical questions asked during data engineer interviews at top tech companies. We also went over how to answer each type of question and what interviewers are looking for. Finally, we provided a bonus Data engineer Python interview question. If you want to practice actual data engineering, data science, or data analyst SQL interview questions, visit app.bigtechinterviews.com.
