The demand for data engineering skills is increasing exponentially, and top companies set difficult data engineer interview questions to test your core competencies. To ace a technical interview for a position in data engineering, you must plan ahead of time.
Your answers to data engineer interview questions should demonstrate your extensive knowledge of data modeling, machine learning, building and maintaining databases, and locating warehousing solutions. During the data engineering interview, you should also be prepared to answer some behavioral interview questions that probe your soft skills. Read on to discover the most anticipated data engineer interview questions at top FAANG+ companies to uplevel your tech interview prep.
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To help you kickstart your data engineer technical interview prep, here is a compiled list of data engineer interview questions.
Here's what we'll cover in this article:
Before we get into the most common data engineer interview questions, let's go over the top skills that a data engineer should have. Before you begin your data engineering technical interview prep for FAANG+ companies, you must have the following core skills:
Read more about the role of a Data Engineer vs. Data Scientist, their career outlooks, salaries, and skill requirements.
There are mainly two types of design schemas in data modeling:
Big Data is the collection of data from several sources. It is a result of exponential growth in data availability, processing power, and storage technology. It is often characterized by four Vs:
Hadoop is a framework technology that helps in handling huge volumes of data in the Big Data ecosystem. The components of the Hadoop application:
NameNodes in Hadoop store metadata of all the files on the Hadoop cluster. The metadata has data nodes, information of the location of blocks, size of files, hierarchy, and more. NameNode is the master node. It maintains and manages blocks present on DataNodes in the Apache Hadoop HDFS Architecture.
NameNode crash results in the non-availability of data, but all blocks of data remain intact. In a high availability setup, there is a passive NameNode that backs up the primary one. So, this takes over in case of a NameNode crash.
Hadoop Distributed File System or HDFS automatically splits huge data files into smaller fragments. Blocks form the smallest unit of a data file.
Block Scanner identifies corrupt DataNode blocks. It verifies the available list of blocks presented on a DataNode.
The following steps occur when Block Scanner identifies a corrupted data block:
Name nodes and data nodes communicate via Heartbeat in Hadoop. Heartbeat is the signal sent by the DataNode to the NameNode at regular intervals to indicate its presence. Therefore, as the name suggests, heartbeat indicates that DataNode is alive.
Learn more about the roles and responsibilities of a data engineer.
Distributed cache is a useful utility feature in Hadoop that improves the performance of jobs by caching the application files. It can cache read-only text files, jar files, archives, and more. An application specifies a file for the cache by employing JobConf configuration.
COSHH stands for Classification and Optimization-based Scheduling for Heterogeneous Hadoop systems. This multi-objective Hadoop job scheduler provides scheduling at the cluster and the application levels to impact the completion time for jobs positively.
Metastore is a central repository in Hive that stores partitions in a relational database, schema as well as the Hive table location. It is stored in RDBMS supported by JPOX. Hive provides clients access to this information by using Metastore service API.
SerDe is the short form of Serializer or Deserializer. SerDe allows you to read data from table to and write it back out to HDFS in any format you want in Hive.
The 'create' statement in MySQL creates the following objects:
Facebook deals with a significant amount of user data, making it the perfect place for a data engineer. If you are a data engineer aspiring to join the Facebook team, you must go through the following Facebook Data Engineer interview questions.
Amazon relies heavily on data collection and utilization, thereby making data engineering a lucrative position at the company. You can ace your Amazon data engineer interview with radical preparation. Here is a list of the most anticipated Amazon data engineer interview questions that you must practice before your final interview.
You must be intrigued about how much an Amazon data engineer earns per year. Check out Amazon Data Engineer Salaries here.
If you are preparing for Google data engineering positions, you should be well-versed in cleaning, organizing, and manipulating data using pipelines and other latest technologies. The following Google data engineer interview questions will help you ace your upcoming interview.
Recommended Reading: Google Data Engineer roles and responsibilities.
You can practice some more data engineering interview questions to get through your interview successfully.
Q1. What should I study for data engineer interview questions?
You must brush up on fundamental and advanced topics of SQL and Python. As a data engineer, you should be well-versed in data modeling, data pipelines, distributed system fundamentals, event streaming, and some system design as well.
Q2. Is machine learning required for data engineering interview questions?
If you are preparing for data engineer interviews, you only need a basic knowledge of machine learning so that it enables you to understand a data scientist's needs better and build more accurate data pipelines.
Q3. Do I need to ace math for data engineering?
You only require basic math knowledge for data engineering. You need to focus primarily on statistics and probability in math, as your knowledge of statistics will help you have an idea of what the data scientists on your team will be doing.
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