Data Architect Interview Questions You Should Be Prepared to Answer

A deep dive into conquering data architect interview questions: an in-depth exploration and strategic preparation guide for aspiring data architects.

Stepping into a data architect interview can be a nerve-wracking experience, especially when you're unsure of what questions might come your way. It's only natural to feel this way, especially when the role you're eyeing is as substantial and pivotal as that of a data architect.

Luckily, this guide is here to help you maneuver through potential interview questions you might encounter. This article is designed to cater both to newcomers dipping their toes in the field and seasoned professionals aiming to solidify their stance, and strives to be your trusty companion steering you through the maze of potential questions.

In this article, you will embark on a journey that meticulously unravels the most asked data architect interview questions, dissecting each one to provide you with the best strategies to construct your responses. Whether you are a newbie or a seasoned professional, we've got you covered. So sit back, relax, and let's dive in!

Preparing for the Data Architect Interview

Before you even set foot in the interview room, there's a lot you can do to set yourself up for success. In the following subsections, we will guide you through essential preparatory steps you can take:

• Research Company Background
• Understand the Job Description
• Review Relevant Technologies
• Interview Questions

In the end, the Data Architect Interview Questions, which are the meat of the matter, will be divided into three key categories:

• SQL
• Python
• Behavioral Questions

By the end of this section, you should have a solid understanding of what to expect and how to prepare for your data architect interview. So let's get started!

Research Company Background

Understanding the company's history, mission, and values can give you a leg up in the interview. Research the company's recent projects and familiarize yourself with their perspective.

A great start could be to check their official website and recent publications. Remember,  knowledge is power!

Understand the Job Description

The job description is like a roadmap to the data architect interview questions you might face. Pay special attention to the skills and experiences they seek in a potential candidate.

Tailor your responses to showcase how your background aligns with the job description. This could be your secret weapon to stand out in the interview.

Review Relevant Technologies

In the constantly changing tech environment, keeping track of the latest technologies is a must. Focus on the tools and technologies mentioned in the job description. It could range from understanding database management systems to mastering big data technologies.

And remember to get a grasp of the company-specific tools that might be mentioned during the interview.

Data Architect Interview Questions

Now, let’s see the data architect interview questions, starting with SQL and going to behavioral questions. By practicing these questions, your confidence level will increase to the top, which will give you to show the best version of yourself.

Data Architect SQL Interview Questions

Being proficient in SQL is a non-negotiable for a data architect. You'll be asked to manipulate and retrieve data, often in complex ways.

In the following parts, we will go into questions from the City of Los Angeles, Meta, and the City of San Francisco to test your ability to filter records, calculate averages, and find medians—core functionalities you'd need daily.

Finding all inspections

In our first SQL data architect interview question, the city of Los Angeles asks you to find all inspections that are part of an inactive program.

Link to this question: https://platform.stratascratch.com/coding/10277-find-all-inspections-which-are-part-of-an-inactive-program

In this query we will fetch records from a table where the program_status is ‘INACTIVE’. It uses a simple WHERE clause for this. Let’s see the code.

SELECT
    *
FROM
    los_angeles_restaurant_health_inspections
WHERE 
    program_status = 'INACTIVE'

Here is the expected output.

Average Session Time

In our second question, Meta asks you to calculate users by average session time.

Link to this question: https://platform.stratascratch.com/coding/10352-users-by-avg-session-time

In this more complex SQL query, we will see the use of a Common Table Expression (CTE) and window functions to calculate the average session duration for each user.

Our CTE calculates the session duration for each user and day. The final query then calculates the average session time. This will track how long users typically spend on a website. Let’s see the code.

with all_user_sessions as (
    SELECT t1.user_id, t1.timestamp::date as date,
           min(t2.timestamp::TIMESTAMP) - max(t1.timestamp::TIMESTAMP) as session_duration
    FROM facebook_web_log t1
    JOIN facebook_web_log t2 ON t1.user_id = t2.user_id
    WHERE t1.action = 'page_load' 
      AND t2.action = 'page_exit' 
      AND t2.timestamp > t1.timestamp
    GROUP BY 1, 2) 
SELECT user_id, avg(session_duration)
FROM all_user_sessions
GROUP BY user_id

Here is the expected output.

Median Job Salaries

In our final question, the city of San Francisco asks you to find the median job salaries for each job.

Link to this question: https://platform.stratascratch.com/coding/9983-median-job-salaries

Here, we will use the PERCENTILE_CONT() function to find median salaries for each job title. You're essentially asking the database to line up all salaries and find the middle one for each job title. Let’s see the code.

SELECT jobtitle,
       PERCENTILE_CONT(0.5) WITHIN GROUP(ORDER BY totalpay) as median_pay
FROM sf_public_salaries
GROUP BY 1
ORDER BY 2 DESC

Here is the expected output.

Data Architect Python Interview Questions

Python is another tool often used by data architects for data manipulation and analysis.

In this article, we will go into the questions from Yelp, Box, and Amazon to test your ability to use Python for filtering, aggregation, and ranking tasks, all essential for data architects.

Yelp Pizza

In our first Python data architect interview question, yelp asks you to find the number of Yelp businesses that sell pizza.

Link to this question: https://platform.stratascratch.com/coding/10153-find-the-number-of-yelp-businesses-that-sell-pizza

In this following code, we will filter out businesses that sell pizza based on the 'categories' column. The length of this filtered DataFrame will be the output. Let’s see the code.

import pandas as pd
import numpy as np

pizza = yelp_business[yelp_business['categories'].str.contains('Pizza', case = False)]
result = len(pizza)

Here is the expected output.

Class Performance

In the next question, box asks you to evaluate class performance.

Link to this question: https://platform.stratascratch.com/coding/10310-class-performance

In the following question, we will add up scores from three different assignments box score into one and create a new column, total_score.

Then we will find the range by subtracting the minimum total score from the maximum. Essentially, the output includes the performance gap between the best and worst students.

Let’s see the code.

import pandas as pd
import numpy as np

box_scores['total_score'] = box_scores['assignment1']+box_scores['assignment2']+box_scores['assignment3']
box_scores['total_score'].max() - box_scores['total_score'].min()

Here is the expected output.

Best Selling Item

Here’s the final Python data architect interview question where Amazon asks you to find the best selling item for each month, where the biggest total invoice was paid.

Link to this question: https://platform.stratascratch.com/coding/10172-best-selling-item

Here, we will calculate the total amount paid for each item in each month and rank them. It's like looking at monthly sales data and identifying the top seller for each month.

To do that, we will create new columns month, paid, and total_paid first. Then we will group our newly shaped dataframe and rank them. Here is the code.

import pandas as pd
import numpy as np

online_retail['month'] = (online_retail['invoicedate'].apply(pd.to_datetime)).dt.month
online_retail['paid'] = online_retail['unitprice'] * online_retail['quantity']
online_retail['total_paid'] = online_retail.groupby(['month','description'])['paid'].transform('sum')

result =  online_retail[['month', 'total_paid', 'description']].drop_duplicates()
result['rnk'] = result.groupby('month')['total_paid'].rank(method='max', ascending=False)
result = result[result['rnk']==1][['month', 'description','total_paid']].sort_values(['month'])

Here is the expected output.

Data Architect Behavioral Interview Questions

These gauge whether you'd fit into the company culture and how you approach problems, teamwork, and challenges.

Solving Complex Data Problem

“Tell me about a time when you had to solve a complex data problem. How did you go about it?”

This data architect interview question is similar to a plot twist in a movie. The interviewer wants to know how you adapt and find a solution when faced with an unexpected challenge.

Your answer should demonstrate your problem-solving skills and ability to innovate and the best answer includes the real-life problem that you faced and solved.

Managing Time

“Tell me about a time you faced a strict deadline. How did you organize your time and resources to meet it?”

By asking this question, the interviewer is interested in your time-management skills and how you handle pressure. To answer that question, explain to the interviewer the technique that you used to make plans to manage time.

Collaboration

“Can you share an experience where you had to collaborate with other departments or teams for a data-related project? How did you ensure effective communication?”

This data architect interview question aims to test your communication skills and your ability to collaborate across different departments or teams.

If you want more questions, read this article, 40+ Data Science Interview Questions From Top Companies, which offers you 40+ more questions.

Final Thoughts

Stepping into a new career, such as data architect, can feel complex at first glance. However, by adopting a "divide and conquer" approach, you can turn this complex journey into a shorter and easier path.

This article has aimed to be your compass, steering you through SQL and Python-based questions, company research, and behavioral inquiries. Whether you're a newcomer or a senior, these insights provided here should give you the confidence to construct articulate and strategic responses to any question thrown your way.

But remember, the best preparation doesn't stop here. You need to practice what you learned to build a habit from your knowledge. StrataScratch offers a wide range of interview questions from companies worldwide, giving you an unparalleled edge in your job search.

The more you practice, the more you refine your skills, which eventually will increase your chance of landing that dream job.

FAQs

How do I prepare for a data architect interview?

To ace a data architect interview, first do your homework on the company's background, mission, and recent projects. Then, practice SQL, Python, and behavioral data architect interview questions that align with the job description and relevant technologies.

How do I prepare for data architect?

To prepare for the role of a data architect, focus on mastering SQL and Python, as they're essential tools in the field. Also, gain a solid understanding of database management systems and big data technologies that are mentioned in the job description.

What does a data architect do?

A data architect designs and creates the data architecture of a company, like laying down the blueprint for a building. They handle tasks like data storage, retrieval, and management, often using SQL and Python to do so.

What data architect should know?

A data architect should be proficient in SQL for data manipulation and retrieval. They also need to know Python for data analysis and should be skilled in database management systems. Soft skills like effective communication and teamwork are also key skills.