Introduction

Data quality is one of the most important aspects of any data-driven organization. No matter how much effort you put into collecting data, it’s meaningless if you can’t trust what your system outputs. As a Data Analyst, you’ll be responsible for ensuring that this information is accurate and reliable so that stakeholders can rely on it when making business decisions. When hiring new talent, organizations want to know if candidates have the right experience to handle these responsibilities—and they want to see how well they understand what makes an effective Data Analyst at their organization.

Begin with a very general question.

Begin with a very general question. You want to get your interviewee talking, so you can learn more about them and their experience. The best way to do this is by asking open-ended questions that allow the candidate to talk about themselves freely. For example:

• What’s your background?
• What are some of your interests and hobbies?
• How long have you been working in this field?

This will help establish rapport with the candidate before moving into more specific questions about data quality analysis skills.

Ask the candidate to define data quality in their own words.

A great way to start the interview is by asking the candidate to define data quality in their own words. This is a good way to gauge how well they know the topic, and it can also help you understand what they consider important when it comes to data quality.

The next step is to ask them about different types of data quality: accuracy, validity and reliability are some common ones that we often hear during interviews. You might also want to ask them about completeness (do all fields have values?), timeliness (is there any lag time between when something happens and when you enter it into your system?) or uniqueness (are there duplicates?).

How do you apply metrics to measure the effectiveness of your work?

• Define the problem before starting on a solution.
• Set goals before you start.
• Don’t worry about what other people’s goals are.
• Be ambitious, but stay realistic. For example, if someone asked me “How many push-ups can I do?” my answer would be 100! But if they wanted to know how much weight I could lift or how fast my mile time was, I’d say 80 pounds and 7 minutes respectively–and then give advice on how they could achieve that goal within 3 months (or 6 months).

What level of accuracy is acceptable for your organization?

• What level of accuracy is acceptable for your organization?
• How do you measure accuracy?
• How do you improve data quality?
• What are some ways to measure improvement in data quality?

Do you think it is possible to improve data quality without lowering customer satisfaction?

It is possible to improve data quality without lowering customer satisfaction. You can improve the process that created the data and make sure it’s done right, or you can improve your understanding of what’s happening with your customers and how they interact with their information. For example, if someone is using a website to book tickets for a flight but they keep getting charged extra fees because there was an error in the booking system, then that’s something we need to fix so this doesn’t happen again!

It could also be helpful for us as analysts if we got more training on how best practices work so we’re able to create better reports ourselves (and earn more money).

What is a typical day like in your role?

Every day is different, so it’s difficult to say what a “typical” day looks like. I do spend a lot of time in Excel and SQL, but I also get to work with other tools like Tableau, Power BI and R.

I have had the opportunity to work on some really interesting projects that have helped our company improve its business processes and decision making capabilities.

Why did you leave your last job after only six months?

It’s a good idea to prepare for this question in advance. If the answer is “I was laid off,” don’t be afraid to say so. If you were fired or left under bad terms, be sure that the interviewer knows that it was not due to performance issues or misconduct on your part (and if it was, do not admit it).

Have you ever been involved in any data clean-up projects? If so, what was this progress like over time? What were some of the challenges you faced during this project, and how did you overcome them? How did your team communicate during this process? Any fun stories from those days that could illustrate how crucial communication was during this project (if applicable)?

• Have you ever been involved in any data clean-up projects? If so, what was this progress like over time? What were some of the challenges you faced during this project, and how did you overcome them?
• How did your team communicate during this process? Any fun stories from those days that could illustrate how crucial communication was during this project (if applicable)?

Conclusion

And there you have it–35 Data Quality Analyst Interview Questions (With Answers). If you have any other questions that we didn’t cover in this article, feel free to post them below! We’ll do our best to answer them for everyone else who comes by here looking for help with their next interview.