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How To Prepare For A Data Engineering Interview

How To Prepare For A Data Engineering Interview

  • The popular open-source libraries a data engineer should know include Spark, Pandas, Hadoop, and Kafka.

Lately, the demand for data engineers has surpassed the demand for data scientists. It is challenging to prepare for data engineering interviews due to the lack of readily available resources. The fact that the data engineering field is still evolving and still not clearly defined makes the preparation routine tricky for candidates. While the interview process is quite similar to data science interviews, the focus areas are different. 

Below, we look at the skills a data engineer must possess and the things to bear in mind when preparing for data engineer interviews.

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Things To Focus On

Programming & coding skills: The data engineering interviews count programming and coding skills, and the ability to implement complex algorithms as critical criteria. The coding bit of the interview is usually focused on the data side and is more practical-driven. Data engineer candidates are expected to use optimal data structures and algorithms to handle potential data issues.

Tools to know: The popular open-source libraries a data engineer should know include Spark, Pandas, Hadoop, and Kafka. Knowledge in languages such as Python, Java/Scala, HTML, CSS and JavaScript will be highly beneficial. Data engineers are also expected to build data visualisations that require an understanding of tools such as React and D3. Some of the other tools to learn for data engineering interviews are scala, spark, hive, pig etc.

Upping the SQL game: SQL is one of the essential skills data engineers must be good at. SQL helps in the data processing of big data frameworks such as SparkSQL, pandas, and KafkaSQL. It also helps in translating business queries that end-users can run against your table. It is essential to prepare for SQL-related questions to ace data engineering interviews. 

Relational databases and data warehouses: A data engineer has to deal with NoSQL databases, graph databases, and more. Data engineers are also expected to design a proper data warehouse based on the use case. Preparing for questions around data warehouses, databases etc., could come in handy in interviews.  

Data architecture: Data engineers are expected to have a good grip on data architecture and big data systems. While experience gets preference, you can always make a case for yourself by upgrading your skills and understanding the concept thoroughly by making use of good resources available online. 

System design: System design is the most important and most challenging part of data engineering technical interviews. It involves designing an end-to-end data solution that involves data storage, data processing and data modelling.

Explaining the use cases: Explaining the business problem and the solution your team have come up with to tackle it will give the recruiters a chance to size up your expertise and thought process. The candidate can expect to field questions about how you worked with your team to design the solution, the frameworks and tools used, the impact of the resolution, challenges faced, etc.

Soft skills: Good communication and problem-solving skills increase candidates’ prospect to land a data engineering job. The passion for your profession and the willingness to take up challenges will leave a good impression on the interviewers. 

Below are some of the common interview questions for data engineer jobs. 

  • What’s the biggest professional challenge you’ve faced, and how did you overcome it?
  • Which frameworks and applications are critical for data engineering?
  • What are the data engineering platforms you are most familiar with, and how did you use them in your previous jobs?
  • Which computer languages are you fluent in?
  • Questions on pipelines, databases, distributed systems and more
  • Do you have any experience with data modelling?
  • What is your approach towards developing a new analytical product as a data engineer?
  • What were the algorithms and languages you have used on a recent project?
  • How does a data warehouse differ from an operational database?
  • What is a common data engineering maxim you disagree with?
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