Data Engineers' primary job is to ingest data from various sources into a data lake. This data should be organized in proper formats, processed desirably, and stored safely and in accordance with provided storage capacity and hardware architecture. Data engineers are in charge of developing and maintaining data infrastructure and applications and setting up data warehouses and pipelines. In the following Figure, you can see some of the most usual tasks in any data engineer's daily life.

As you can see, proficiency in work with multiple technologies is required. But don't worry. In the following paragraphs, we'll help you understand what you need to know to be a competitive data engineer candidate and what fundamental topics you should pay attention to for an interview.

REQUIRED SKILLS

Generally speaking, most of the required knowledge to be a data engineer can be broken up into the following categories:

Let’s examine together the skills mentioned above.

1 Programming knowledge

First of all, it is assumed that you successfully use at least one programming language. Statistics show that over 70% of jobs based on data engineering require knowledge of the Python programming language. It is a warm recommendation that if you do not have prior Python knowledge, start from today with mastering this popular and user-friendly language. Other highly recommended skills are proficiency in SQL, Java, Scala. Additionally, R, Ruby, and Perl are also considered popular programming environments in the world of data engineering. What do you need to pay special attention to when it comes to programming?

1. Be familiar with data structures. Be sure to know how to use lists, dictionaries and how to link them. Also, basic operations as searching, inserting, and appending are essential for data manipulation processes.
2. Understand algorithms and programming sequences that can search the data, merge or sort features and create new elements by combining the existing ones.
3. Solve practical problems by finding some existing data sets on the web, play with data, try to extract your own conclusions, and find hidden knowledge. Every newly processed data set is one level up in your experience, which will mean a lot for you at the beginning of your data engineering path.

2 SQL

Although many consider SQL to be a query language, do not underestimate its power and the great need to master it! Don’t be surprised if you spend a significant amount of time discussing SQL techniques and problem-solving approaches in a data engineering interview. In addition to using it for queries in databases, SQL is regularly utilized as a data processing pattern within various Big Data frameworks as KafkaSQL, SparkSQL, Python libraries, etc. If you are proficient in using SQL itself, that knowledge would be a valuable indicator that you can also be efficient in these Big Data Frameworks. A big plus for you!

3 Data Modeling

Data modeling is closely related to SQL, and it is considered to be an essential part of the overall system design process. Data modeling means designing a data model following provided data patterns and specific use cases. It is the first step towards the database design process and data analysis tasks.

4 Architectures and Design of Databases. Big Data Technologies

Be prepared to get a business use case by a company to test your capability to design a suitable data warehouse. In order to successfully respond to such a challenge, try to find real-life examples on the Internet, comments from the creators of online stores, and the experiences and guidelines they shared for designing such a system. Finally, try making a small test version of an online store yourself, such as a board game store, for example. If nothing else, try to graphically show the whole process of developing such a system and what stages it should go through (from data collection to the application interface). When it comes to working with databases, the following Figure shows the fundamental processes involved and some of the most popular frameworks used for their realization.

Don't be discouraged by the quantity of these tools! Of course, someone who is a beginner or even proficient in Data Engineering is not expected to know all the tools at once! But try to recognize all the involving elements of one data engineering path and build a proper knowledge foundation. It will definitely send a signal that you are familiar with all essential concepts from the data engineering domain of expertise. One another plus for you!

5 Soft Skills

You probably wonder why soft skills stand out here when they are probably negligible compared to technical abilities and expert knowledge. Suppose you went through this article until now with ease, recognizing each of the above concepts. In that case, it is totally unnecessary to talk about soft skills. Is that right? Well, not precisely… By no means neglect your personal skills and problem-solving abilities! They can be of crucial importance for a Data Engineer position.

Try to practice your presentation skills and try to explain the path to your solution in a clear and precise way. You can be the best programmer, but if you cannot present your solution optimally in oral communication during an interview, you will most likely get a rejection letter. In addition to good communication skills, critical thinking and the ability to work in a team can be crucial whether you get a job or not.

Question 5

New technology should be implemented this month in your company. How will you explain it to coworkers who are unfamiliar with it?

Answer: To answer this question, try to present your communication skills that will illustrate how well you interact with your coworkers. You can also describe a situation where you introduced a new technical topic to the audience and how you overcame an initial misunderstanding.

ADDITIONAL INTERVIEW QUESTIONS

When data engineering roles are in question, the dominant part of the coding assessment relies on the data side, not on the algorithm side. Be ready to solve practical problems! Below are a few more examples of questions you may encounter very often in a data engineering interview.

QUESTION 6

The task is to construct a SQL query that will show the unique number of occurrences of one class within a single column.

Small help: You can get required results by using the next SQL functions: SELECT, COUNT, FROM, GROUP BY

QUESTION 7

Name three Python libraries that can be used for data processing tasks.

Answer: Numpy, Pandas, TensorFlow

QUESTION 8

Your assignment is to visually present outliers from a data set. Name one library and its function, which is an adequate solution for this kind of visualization.

Possible answers: Box plot, Scatter plot

QUESTION 9

The volume of data is rapidly increasing. What is your plan to add more capacity to the existing architecture?

Possible answers: You can request more database instances in the cloud on Google Cloud Platform, for example. Or to suggest removing old data sets and better data compression. Try to research on your own more solutions for this problem.

QUESTION 10

Name the main components of Hadoop. And, of course, what is Hadoop?

Answer: Shortly explained, Hadoop is the tool for processing Big data. Two main components of Hadoop are HDFS and MapReduce.

Suppose you know the answers to the previous questions on your own. In that case, you are well on your way to successfully passing one interview for Data Engineer. If you have problems solving these questions, try to investigate occurred issues and find solutions yourself. In any case, let this tutorial be the basis and starting point for mastering new data engineering knowledge. Be curious and don't just dwell on case studies and problems in the existing literature. Find yourself a real problem based on existing databases, approach it from a data engineer's point of view, and go through the entire development path from data collection to analysis. It will undoubtedly be fun, and more importantly, you will gain valuable practical experience in working with data!

One Pager Cheat Sheet

• Data Engineers build and maintain data infrastructure and applications, managing ingestion, organization, processing, storage, and warehousing of data from various sources according to hardware architecture and storage capacity.
• Being a Data Engineer requires knowledge in software engineering, data warehousing, data modeling, data integration and big data technologies.
• Acquiring proficiency in programming languages such as Python, SQL, Java, Scala, R, Ruby, and Perl and understanding data structures, algorithms, and practical problem-solving are key to succeeding in data engineering.
• The map() function can be used to transform a vector of strings into integers, by mapping each string to a specific corresponding integer value.
• Mastering SQL is an essential part of being a successful Data Engineer, and proficiency in it indicates proficiency in Big Data Frameworks such as KafkaSQL, SparkSQL and Python libraries.
• The COUNT() function in combination with the GROUP BY clause can be used to identify and get an exact count of duplicate records in a column.
• Datamodelingis an integral part of the system design process that involves creating a data model following particular data patterns and use cases.
• Data models are the foundations of a data system and can be classified into three main types: Conceptual, Logical, and Physical.
• Get familiar with the fundamental processes and tools used in data engineering by exploring real-life examples, trying to develop a small test version of an online store, and learning the basics of database architecture and design.
• The correct approach to validate a data migration process from one database to another would involve running tests to check for data type, record count and other discrepancies, whereas Digital Preservation requires a different set of processes to protect digital information over time.
• Emphasizing the importance of soft skills, it is essential for a Data Engineer to have excellent communication, problem-solving and team working skills in order to stand out in the job market.
• Explaining new technology to unfamiliar coworkers requires excellent communication skills and the ability to illustrate concepts in a way that is easy to understand.
• Preparing for a data engineering interview may require solving practical coding problems, such as constructing a SQL query to reveal the unique number of occurrences of one class within a single column, as well as knowing certain terms and libraries such as Numpy, Pandas, TensorFlow, and Hadoop with its two main components HDFS and MapReduce.