Great! You've got an interview at your dream job... you're a great data professional, you're a great team player... but what might you have to chat about in the interview? Here are a few examples from some of our clients! 

Interview Questions Data Engineering

Data engineering interview questions on data engineering-related tools, techniques, and basics are asked. Data engineering interview questions are categorized according to the following strategy:

• Data engineering interview questions on technical concepts

• Data engineering interview questions on basic concepts 

• Data engineering interview questions on business concepts

Technical Data Engineering Interview Questions:

1. What is a data warehouse? 

2. What are the different types of data warehouses? 

3. Differentiate between univariate, bivariate, and multivariate analysis.

4. What is data virtualization, and how does it work? 

5. Can you explain data warehousing concepts like star schema, snowflake schema, and fact constellation? 

6. What's the difference between data integration and data quality? 

7. What data quality dimensions do you know, and what data quality problems can these data engineering interview questions data quality dimensions cause?

8. What is the ETL process, and what are the possible ETL tools that you're familiar with? 

9. What's the difference between data normalization and data de-normalization? What's 

10. Difference between data aggregation and data summarization?

Basic Data Engineering Interview Questions:

1. What data engineering tools are you familiar with?

2. Explain the data cleansing process.

3. What data mining algorithms do you know, and what data mining tasks can they be used for?

4. What practical data mining applications of data cleaning, data integration, data warehousing, and data preprocessing did you work on? 

5. What is the difference between data visualization and data reports?

6. What data quality dimensions do you know, and what data quality problems can these data engineering interview questions data quality dimensions cause? 

7. What real-life data mining applications of association rule learning have you've worked on? 

8. What were data preparation steps needed for those data mining projects before applying the association rule learning algorithm?

9. We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?

10. Why is R used in Data Visualization?

Business Data Engineering Interview Questions:

1. What data engineering tools are you familiar with? 

2. How can data engineers benefit from data visualization in the data exploration phase?

3. Can data mining be applied to social media data, and what is the data format of this data? 

4. What data preparation steps would you need before applying data mining to social media data?

5. In your work experience, data engineering interview questions data mining application, have you've worked on data with categorical data attributes? 

6. How can data engineers work with categorical data attributes?

7. What statistical tools, data visualization techniques are you familiar with, and what data engineering tasks these interview questions data visualization techniques can be applied to? 

8. How to use text analytics for sentiment?

9. There is a pool of people who took Uber rides from two cities that were close in proximity, for example, Menlo Park and Palo Alto, and any data you could think of could be collected. What data would you collect so that the city the passenger took a ride from could be determined?