Data Engineers collect data from various sources and transform it into usable formats. They must also construct and maintain systems that generate the necessary raw data, as well as cleanse the procured information so that Data Scientists and Analysts can easily examine it in detail. As such, one of the key components of a data engineer's role is writing complex queries to make this data valuable.

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Put simply, data engineering is the operation of designing and building systems for collecting, storing, and analyzing data.

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During an interview, employers seek to find out how skilled a candidate is in these areas. Data engineer interview questions can range from culture fit, behavioral, and technical interview questions to situational ones. Candidates should be ready to answer both types of questions during their interview in order to demonstrate their expertise and professionalism.

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When it comes to the technical component of a data engineer's job, SQL (Structured Query Language) is one of the primary coding languages used. Employers will likely ask interviewees to demonstrate proficiency with it. This can include anything from writing queries to joining tables and understanding the output results.

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Python is also an integral language for data engineering and employers may ask questions that test a candidate's knowledge in this area as well. Python questions may include topics such as working with data frames, dealing with missing values and outliers, implementing standardization methods, or writing functions for data manipulation.

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If you're an interviewer writing questions for data engineer interview preparation, make sure to consider scenario-based questions. Scenario-based questions provide a more realistic view of how successful the candidate will be in their role. They also help employers assess how well an applicant can think on their feet and handle complex problems under pressure.

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For example, you might ask a candidate to describe how they would handle a situation where they are presented with a large, complex dataset that contains outliers, missing values, and inconsistent formats. This type of question will provide the employer with an insight into how well the candidate can troubleshoot errors and formulate solutions for cleaning and preparing data for analysis.

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By including both technical questions as well as scenario-based ones in your list of data engineer interview questions, you can get a better sense of how well the candidate is suited for the role they are applying for. Ultimately, this will help to make sure that your organization only hires the best talent available.

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Interview intelligence software with built-in data engineering interview questions that can be used as prompts will allow you to quickly prepare and assess your candidate's coding skills and technical knowledge.

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Data Engineer Coding Interview

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Data engineer coding interview questions are designed to assess applicants’ coding abilities and technical knowledge. These questions can range from basic SQL queries to more complex tasks such as writing functions in Python or processing data structures.

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If you're an interviewer looking for a comprehensive list of data engineer coding interview questions, here are some to consider:

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• Describe the process of cleansing raw data and explain how you would use Python for this.
• Explain the purpose of normalization, and give an example of where it can be used.
• Describe how you would use a decision tree algorithm to model a predictive problem.
• Write a function to parse a JSON string and create a data frame from the result.
• Explain how you would use MapReduce for large-scale processing of data stored in Hadoop.
• Describe the process of creating summary statistics from a dataset, and explain how you would use Python for this.
• Explain the purpose of data visualization, and give an example of how it can be used in data engineering.

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By asking these types of questions in a data engineer coding interview, you can get a better sense of an applicant's coding skills and technical knowledge related to working with data.

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SQL is a also vital language for data engineers and applicants must demonstrate proficiency with it in order to be considered for the role. If you're an interviewer preparing SQL interview questions for a data engineer, here are some to consider:

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• Write a SQL query to extract the first and last names from a table.
• Write a SQL query to join two tables using multiple columns as the joining key.
• What is the difference between a LEFT JOIN and an INNER JOIN?
• Write a query to rank records based on their values in a column.
• How would you use SQL to check for missing values in a table?

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These types of questions will help assess how well an applicant understands the basics of SQL, as well as their experience with more advanced concepts such as joins and aggregations.

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By including both technical questions and scenario-based ones in your list of data engineer interview questions, you can get a better sense of how well the candidate is suited for the role they are applying for. Ultimately, this will help to make sure that your organization only hires the best talent available.

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Pillar created a video interview platform with built-in data engineering interview questions that can be used as prompts that will allow you to quickly prepare and assess your candidate's coding skills and technical knowledge. This will ensure that the hiring process is efficient and effective so that you can make better hires.

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Data Engineer Scenario-based Interview Questions

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Since we focused on data engineer python interview questions in the section above, we'll focus on data engineer scenario-based interview questions here. These types of questions will help you to assess an applicant's ability to think critically and apply their knowledge in real-world scenarios.

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The following are examples of data engineer scenario-based interview questions:

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• You’re working with a large dataset that has missing values. How would you go about identifying where the missing values are and why?
• Describe how you would build a predictive model to anticipate customer churn based on past behaviors.
• Your company has just released a new product and you need to predict demand for it. What kind of data analysis techniques would you use to do this?
• You’re analyzing a dataset with a lot of outliers. Describe how you would identify and deal with them.
• Your company is looking to expand its customer base in a new market. Describe the steps you would take to analyze potential customer data in that market.

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Data engineer interview questions and answers like the ones above can help you to get a better understanding of an applicant's knowledge and experience related to data engineering. By asking these types of questions, you can gauge how well the candidate can think critically and apply their technical knowledge in real-world scenarios.

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Data Engineer Interview Questions Python (300)

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Before we get into specific questions, which type of data engineer will you be hiring? JavaSpark, PySpark, or Scala Spark? HackerRank data engineer questions are great for assessing the technical abilities of a candidate and understanding areas where they can grow. They list SQL interview questions for data engineers in each category from basic to expert but primarily focus on basic and intermediate questions.

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HackerRank is a great tool to perform data engineer coding challenges for the candidates you are interviewing. Along with platforms like CodeSignal and Codility, they provide data engineer technical interview questions that will allow you to evaluate a candidate’s skills across each coding language.

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Some basic questions interviewers may ask candidates in a data engineer interview are:

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• What is the difference between a hashmap and a dictionary in Python?
• Explain how garbage collection works in Python.
• Describe the map(), filter(), and reduce() functions in Python.
• How do you use lambda expressions in Python?
• Explain the purpose of generators and iterators in Python.

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These are just a few of the many data engineer interview questions data engineer candidates may be asked in an interview. Hopefully, they'll serve as a baseline for you to create your interview process. Asking these types of questions will help you to understand a candidate's programming knowledge and technical capabilities related to data engineering.

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By including both coding-focused and scenario-based data engineer interview questions, you can get a better understanding of a candidate's skills and how they would fit in the role. A combination of different types of questions will help you to make sure you are hiring the best data engineer for your company.

If you're currently assessing your data engineer interview process and would like to see how Pillar can help you hire the best employees in tech, schedule a demo to chat with someone on our team, today. We offer over 1000 data engineering, data science, software engineering, developer, and other technical interview questions and answers to help you assess your candidates' technical knowledge, problem-solving skills, and overall fit for the role.