Data Quality Analyst Interview Questions

If you are looking for a course that equips you with multidisciplinary skills in data analysis, then the Post Graduate Data Analytics Program is the right one for you. It trains you with all the necessary tools and learning for various data analysis roles like Data Analyst and Data Quality Analyst.

The Post Graduate Data Analytics Bootcamp teaches you foundational concepts of Statistics and how to analyze data using Python and R languages. The Data Analytics program teaches you data analytics, statistical analysis, data visualization, and how to implement regression modules, clustering, and supervised learning algorithms.

What is a Data Quality Analyst?

A Data Quality Analyst looks at the quality and integrity of an organization’s data. The Data Quality Analyst examines the complex data streams and translates them into insights for business decisions. Data Quality Analysts evaluate datasets, monitor performance and quality control plans, identify issues, improve database designs, collaborate with developers to advance data collection methods, and report data analysis findings and trends to the management.

Data Quality Analysts typically work for large institutions that work with Big Data. This role suits people who enjoy working with numbers and datasets, know computer systems, have exceptional analytical skills, and a knowledge of statistical methods and tests.

The average Data Quality Analyst salary in the United States is $78,068 as of June 28, 2021, but the salary range typically falls between $68,596 and $87,155. The salary depends on many important factors, like formal education, certifications, additional skills and tools in the know, and several years of experience.

Requirements for a Data Quality Analyst

The minimum requirement for a Data Quality Analyst is a bachelor’s degree in mathematics, economics, computer science, or statistics.

Some employers request a master’s degree or post-grad certification with extensive coursework in statistics, calculus, and algebra. Programming languages like SQL, Java, Python, and toolkits such as Tableau and R are preferred qualifications. Continuing education is important for this role, and you must keep up with the advances in technology, software tools, and analytical methods. A certification from a prestigious institute goes a long way toward imparting the necessary skills for a Data Quality Analyst. 

What does a Data Quality Analyst do?

A Data Quality Analyst examines the different types of data and tests the data to discover any anomalies. Upon discovery of anomalies, they apply Data Standardization and Data Enrichment techniques for Data Validation.

A Data Quality Analyst ensures the data fulfills the purpose and is of top quality. As the quality of data decides the outcome of the analysis, the Data Quality Analyst ensures that incoming data is of high standards and usable for analysis, and removes any anomalies as required.

Other responsibilities of a Data Quality Analyst include:

• Performing statistical tests on large datasets,
• Evaluating system performance and design
• Developing procedures to improve the accuracy and integrity of data
• Running data queries to identify coding issues and data exceptions,
• Cleaning data.
• Documenting processes
• Developing and implementing test plans and scripts for various data quality processes
• Maintaining test scripts and recommending modifications as per requirements.

Data Analyst vs. Data Quality Analyst

A Data Analyst analyses data to find hidden patterns and trends in the data.

The Data Quality Analyst validates the quality or correctness of the data, certifies its authenticity using various prescribed testing methods, and generally validates the data before it is used for any other purpose.

Data Quality Analyst Interview Questions

Data Quality Analyst interview questions test the candidate’s knowledge of statistical tests, scripts, testing techniques, and strategies.

1. What is data quality and why does it matter?
2. What are the prerequisites of data quality testing?
3. How do you check the quality of your data?
4. What is a data quality plan? What does it usually consist of?
5. What is functional testing? What are the many types?
6. What is a test strategy?
7. What is a data quality check?
8. Why do you need data quality testing?
9. Differentiate between load and stress testing.
10. Describe in brief data validation.
11. What are the dimensions of data quality?
12. What is data warehousing testing? What is its purpose?
13. What is data quality testing in ETL?
14. What are the different stages of ETL testing?
15. What is an ETL testing tool? What are the different ETL testing tools?
16. What are the types of ETL testing? Name any six.
17. What are the types of bugs found in ETL testing?
18. Differentiate between database testing and ETL testing.
19. What is performance testing in ETL?
20. What are the responsibilities of an ETL tester?
21. What are some best practices for ETL testing?
22. Differentiate between ETL tools and OLAP tools.
23. What are Cubes and OLAP Cubes?
24. Explain what is Grain of Fact.
25. What is the tracing level and what are the types?
26. How do you identify data quality issues?
27. What are the main criteria to measure data quality?
28. What are the characteristics of good data?
29. What is data accuracy?
30. What is data purging?
31. Can you describe your data cleanup measures?
32. Which data reporting tools have you found useful? Why?
33. What is debugging and how will you solve the problem?
34. What are the steps to ensure data quality?
35. What are some of the data quality testing frameworks? Name two and describe their benefits.
36. How do you ensure continuous integration of data?
37. What are the ways you will ensure ongoing data quality maintenance?
38. When do you use dynamic cache and static cache in connected and unconnected transformations?
39. What do you think are some of the key Data Quality Analyst skills every Data Quality Analyst must-have?
40. What are some of the typical Data Quality Analyst responsibilities in a day’s work?

Bottomline

Companies are keen to ensure data quality and integrity in their organizations and set benchmarks for the best industry standards. They want to recruit the best talent for the job. If you want to develop the skills of a Data Quality Analyst, there is no time like now. Register for the Data Analytics Bootcamp and watch your career grow, starting now.