How Did I Come Up With These Questions
First and foremost I’ve compiled this list of data governance questions from my own experience. And even though I’ve been through a few successful interviews, I’ve also made sure to check in with other data governance professionals and draw from their experience. Lastly, I was also able to draw from the community of:
Both on the hiring side and the interviewee side. Now let’s go dive into these 5 most asked data governance interview questions.
What Major Challenges Did You Face During Your Major Role What Did You Learn From It
Here, the interviewer is testing your credibility in the given area. Therefore, you should strive to talk about personal experience.
Tip #1: Talk about a credible challenge.
Tip #2: Make sure that the example is unintentional
During my last role, I had to move ahead and do most of the work without any group assistance because my group members were slow, and I had deadlines to beat. I learned that assessing the strength of every group member before grouping them is important.
Amazon Data Engineer Interview Questions
Youre given an IP address as an input in the form of a string. How would you find out if it is a valid IP address or not?
It is the most common question asked during coding interviews, and the answer is simple. You are going to split the string on . and create multiple checks to determine the validity of the IP address.
def is_valid: ip = ip.split for i in ip: if > 3 or int < 0 or int > 255): return False if len > 1 and int == 0: return False if > 1 and int != 0 and i == '0'): return False return TrueA = "255.255.11.135"B = "255.050.11.5345"
The A IP is valid and it returns True, whereas B returns False as it has 4 digits after the dot.
print)> > > Trueprint)> > > False
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How Would You Recommend New Technological Changes In This Firm
The interviewer wants to deduce your experience with IT systems and new integration recommendations.
Tip #1: Show the interviewer that you have strong analytical and research skills.
Tip #2: Keep in mind budgetary constraints and feasibility studies
I would conduct extensive research for the latest software, hardware, and data storage options for this firm by conducting IT seminars and reports. In these reports, I will outline the benefits of the investments and the preferred technological changes.
Explain How You Would Estimate
What theyâre really asking: Whatâs your thought process? Are you an analytical thinker?
With this type of question , the interviewer presents you with a problem to solve. How would you estimate the best month to offer a discount on shoes? How would you estimate the weekly profit of your favorite restaurant?
The purpose here is to evaluate your ability to problem solve and your overall comfort working with numbers. Since this is about how you think, think out loud as you work through your answer.
What types of data would you need?
Where might you find that data?
Once you have the data, how would you use it to calculate an estimate?
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What Kind Of Strategies And Mindset Is Required For This Role
The interviewer wants to know whether you are in the right mental frame for the job at hand.
Tip #1: Talk of a mindset that is related to the job
Tip #2: Exude confidence
Data management requires somebody who can teamwork. The best strategy for handling huge amounts of data is by designating roles. Therefore, it needs a team worker. One should have an accommodating mindset. It involves dealing with people from different departments.
Walk Me Through A Project You Worked On From Start To Finish
This answer should come naturally if you have previously worked on a data engineering project as a student or a professional. That being said, preparing ahead of time is always helpful.
Make sure you explain how it started and what business problem you were solving. Furthermore, explain each step from accessing the raw data and converting it into a cleaned structure data.
Sometimes you are working on multiple projects, and you freeze at this question. To avoid this ever happening, it is a good practice to review the last five projects you have worked on. You can read the project documentation and understand the problem statement.
Check out the example below:
Image from DataTalksClub/data-engineering-zoomcamp
In the above project, we are ingesting TLC Trip Record data, processing, transforming, and serving it using Kafka and Spark streams.
In the project:
- Spark and Kafka for data streaming
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Q3 What Do You Expect The Biggest Challenge To Be In Data Governance
This is where researching the organization really pays off. Because understanding their products, services, their customer base and the business landscape can give you an indication into what challenging they might be facing. And this can differ depending on the industry, organization size, if they are government or not and so on.
As one LinkedIn member mentioned, try not to make assumptions and flip this question around and ask your interviewer “what challenges is the company currently facing?”, while adding your own thoughts to it. Yes, sometimes the interviewer will play hardball and won’t give you much for the answer to see how you would go about answering the question with the little information that you have.
Something helpful to note is that in general if they don’t have a data governance program in place, you can expect:
- A lack of ownership and accountability when it comes to data management
- A very silo approach when it comes to dealing with data quality issues
- Redundant systems and processes
- And so much more…
You can then extrapolate what the biggest challenges would be. It’s a safe bet to mention that the whole change management aspect of data governance won’t be easy. So basically getting people on board to embrace this data ownership and responsibility, come to the table to reach a common understanding, and ultimately change the way they work.
Data Engineer Manager Interview Questions
For engineering manager positions, the questions are related to decision making, business understanding, curating and maintaining datasets, compliance, and security policies.
What is the difference between a data warehouse and an operational database?
A Data warehouse serves historical data for data analytics tasks and decision making. It supports high-volume analytical processing such as OLAP. Data warehouses are designed to load high and complex queries that access multiple rows. The system supports a few concurrent users, and it is designed to retrieve fast and high volumes of data.
Operational Database Management Systems are used to manage dynamic datasets in real-time. They support high-volume transaction processing for thousands of concurrent clients. Usually, the data consists of day-to-day information about business operations.
Why do you think every firm using data systems requires a disaster recovery plan?
Disaster management is the most crucial part of a data engineer manager’s job. The data engineer manager plans and prepares for disaster recovery for various data storage systems.
This task involves real-time backing up of files and media. The backup storage will be used to restore the files in case of a cyber-attack or equipment failure. Security protocols are placed to monitor, trace, and restrict both incoming and outgoing traffic.
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How Long Have You Been Working In Data Governance
If we define it loosely, I started doing Data Governance about 30 years ago, long before that name emerged. It was called data administration then and was taken very seriously at the company I worked for. Things were different at that time, of course. With the mainframe being king of the jungle, there was already some serious governance going on, except that IT was in charge instead of the business. Over the years the business gained more control of IT, and rightly so, but governance has lagged behind. The king is practically gone and what we have left is a rapidly expanding jungle of disparate data that is cultivated by innovation and savvy users. Hence the need for data governance is now greater than ever.
Questions About Data Governance That You Were Too Shy To Ask
A companys data is one of its most valuable and important resources. Managing and protecting that data are big responsibilities, and a data governance processes must be put into place to avoid misuse and to meet regulations. In this article, William Brewer answers questions you may have about data governance but were too shy to ask.
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What Is The Biggest Challenge You Foresee In This Job
This is a typical interview question that is asked to assess your level of preparation and prediction.
Tip #1: You can mention a challenge that affects you personally.
Tip #2: Think wide
One of the biggest challenges I foresee with this job is commuting from my workplace every day. However, that should not be a problem. If I am given a chance, I will relocate to a nearby residence for convenience.
Hiring Here Are Some Data Governance Questions You Might Want To Ask
Thinking about interviewing someone for a data governance or data intelligence role such as a Data Governance Manager or a Data Governance Coordinator? Maybe this is a new role in the organization. Maybe data governance is new to the organization. Maybe this is a person that is already on staff and seeking a new challenge. When hiring someone, you want to make sure that they are a culture fit for the organization. You want to ask them questions that will allow them to give answers where you can make an informed decision about their fit for the position. Make sure that the job description is clearly defined and that the candidate has reviewed the description before the interview.
Questions About Previous Organization:10. What has been your biggest accomplishment in data governance?11. What has been your biggest challenge in data governance?12. How many data systems were included in your data governance efforts at your previous organization?13. What functional areas at your previous organization were included in the data governance efforts?14. Tell me about your experience with data stewards.15. What data governance tools/software/solutions have you used in the past?16. What were these tools used for and how effective were they?17. What was your involvement with these tools?
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Question : Why Do I Need It
Various stakeholder groups have different needs in data lineage. Lets take as an example three groups of professionals: business, data management, and information technology .
Business users from financial, risk, and compliance departments need data lineage for several reasons. First, data lineage assists in complying with some regulations. Personal data protection regulations affect all businesses worldwide. Data lineage, in this case, makes data processing transparent: it allows tracing the origin, destination, and transformations applied to personal data. Data lineage is also crucial for financial professionals as it helps to answer audit questions about data origin and applied transformations. Data lineage also assists in optimizing business processes and application architecture that leads to reducing operational risks and costs.
IT professionals need data lineage for realizing migration or DevOps projects. Optimization of data and application landscapes is also impossible without knowing data lineage. So, data lineage as a must condition is also valid for these initiatives.
The essence of the answer is: business users should clearly identify their purpose of data lineage usage and clarify their requirements at the early stages of a data lineage initiative.
What Is Data Governance With Example
A data governance method enables one group of people to act upon the data contained in a given circumstance, using the corresponding method. Using business drivers that emphasize improving privacy as part of your data governance strategy will help you manage patient data securely throughout your business.
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The Key Questions To Answer Before Creating A Data Governance Framework
To succeed with a data governance project, its not enough to find a couple of data stewards and tell them to create a couple of information policies. Rather, an effective data governance program requires careful planning, the right people and proper technologies for specific tasks like records management.
Here are 5 important questions that will help you develop a framework that works for your organization. Remember that data governance is a cycle, not a linear project, so you will need to regularly revisit questions youve already worked on.
Role Of A Data Manager In An Enterprise
From financial services to healthcare, almost every sector is looking to use exploding data volumes to gain insights into customer/market behavior and drive intelligence that could streamline day-to-day operations across functions and improve margins, among other things. A data manager oversees how the most useful of these data assets are accessed, validated, stored and analyzed to meet various consumption requirements. For this, they implement necessary practices, governance rules, architectural techniques and tools that achieve consistent access to and delivery of data across a spectrum of subject areas.
Simply put, data managers are data stewards with responsibility for data availability, control and security.
For data availability, a data manager makes data easily accessible to end-users in a secure and compliant manner, notes Nicolas Sekkaki, global practice leader for applications, data and AI at Kyndryl.
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State The Difference Between Data Management And Data Governance
Ans.Data management is the technical process of organizing and managing data within an organization. Data governance is the business practice governing how data is used and shared across an organization. Data governance includes setting policies, procedures, and standards for data management, as well as defining who has access to which data sets.
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Describe A Time When You Failed In This Role And The Lesson Learned
This question aims to deduce whether your problems have built you overtime. Therefore, answer it well.
Tip #1: It should be a personal experience.
Tip#2: Touch on the lesson
I once delegated some roles to the junior staff but failed to occasionally supervise them, thinking that they had all it takes, and I had deadlines to meet too. I had to redo all the work when they submitted their final project. I always learned to supervise the junior staff, regardless.
Why Is A Disaster Recovery Plan Vital For All Companies Using Data Systems
A data manager devises disaster recovery plans for data storage systems. The protocol involves backing up files as soon as changes are made and removing the data storage media from the property daily. The backup media is used to restore the files if an attack happens that causes your company to lose all of its data or if the information becomes corrupted. He or she uses the same recovery plan if your business property is destroyed.
What to look for in an answer:
- Experience implementing a disaster recovery plan
- Ability to follow protocols when disaster recovery is needed
- An understanding of the importance of protecting all data
The disaster recovery plan mitigates the risk of data loss and helps companies protect their data from attacks that lead to data corruption and potential identity theft liabilities.
Q5 What Data Governance Tools Solutions Software Have You Used In The Past
I’m keeping this video software agnostic, but you know what to list here. Just mention the tools that you’ve used. And if don’t have experience with any data governance specific tools, that’s fine. Maybe the organization didn’t have budget for them. That’s understandable so you can mention how you’ve repurposed some tools, even Excel, for different data governance tasks.
Does Data Governance Restrict Agile Application Development
Not necessarily. If both are done right, the converse is true. Data governance needs to be given the opportunity to be involved at all relevant points in development using a similar model to operations teams with DevOps. As long as it meets the companys objectives, Data Governance can be Agile too.
The approach to Data Governance need to fit the organizations culture, the maturity of its data management and the data governance objectives it needs to meet
Many of the problems in the past have come through developers facing the requirements of data compliance only at the point of deployment. Data Governance in some organizations have lacked the agility to revise the model in the light of business changes or a clearer understanding of existing data, and have failed to promote any insights into the value of, and need for, data governance.
It is a bad idea to impose data governance on an unwilling delivery teams. It can lead to all sorts of complications such as Rogue data repositories, mapping systems, or an over-reliance on inflexible monolithic transactional systems. Not only can these defeat any chance of maintaining the rapid delivery of changes and enhancements to the business, but risks making subsequent necessary changes such as the deletion of personal data on request frighteningly complex.
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