Experian Data Engineer Interview Questions + Guide in 2024

Experian is a global leader in information services, transforming data into meaningful solutions to support individuals, businesses, and society. With a history spanning over 125 years, Experian operates in more than 40 countries and employs over 20,000 people dedicated to using data for positive impact.

As a Data Engineer at Experian, you will collaborate with a team to manage data pipelines, focusing on the extraction, loading, and transformation of data from various sources utilizing SQL and AWS big data technologies. To succeed in this role, a proven track record of developing complex data extraction and ETL projects, including ingesting data into large data lakes using programming, SQL, and ETL tools, alongside a solid understanding of software development principles and operations, is a must.

This guide will walk you through the interview process, commonly asked Experian data engineer interview questions, and valuable tips to help you succeed. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on an Experian data engineer interview:

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Experian Talent Acquisition Team will make contact and verify critical details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Experian Data Engineering hiring manager stays present during the screening round to answer your queries about the role and the company. They may also indulge in surface-level technical and behavioral discussions. The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Experian Data Engineer role is usually conducted virtually, including video conference and screen sharing. Questions in this interview stage may revolve around Experian’s data systems, machine learning algorithms, ETL processes, and programming languages like Python and SQL.

Case studies and similar real-world problems may also be assigned depending on the position’s seniority. Based on reported experiences, candidates can expect a mix of competency-based questions and technical assessments focusing on areas like KNN, K-means, the differences between L1 and L2, random forests, and general statistics.

Assessment Center

You may be invited to an assessment center if you successfully clear the technical virtual interview. This stage often involves a series of exercises designed to evaluate your technical and interpersonal skills in various scenarios. You could be asked to participate in group activities or solve challenging puzzles, reflecting practical problem-solving abilities. This is also a chance to demonstrate your teamwork and collaborative skills.

In the interview experiences shared, candidates had to solve a group challenge that involved listing the importance of various items for a spaceship crash survival scenario. Such exercises assess your logical thinking, ability to work in a team, and problem-solving capabilities.

Onsite Interview Rounds

After the assessment center, successful candidates are usually invited to attend the onsite interview loop. Multiple interview rounds will be conducted during your day at the Experian office, varying with the role. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the other finalists throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Engineer role at Experian.

Typically, interviews at Experian vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

1. Create a function combinational_dice_rolls to list all possible combinations of dice rolls.

Given n dice each with m faces, write a function combinational_dice_rolls to dump all possible combinations of dice rolls. Bonus: Can you do it recursively?

2. Develop a function is_subsequence to check if one string is a subsequence of another.

Given two strings, string1 and string2, write a function is_subsequence to find out if string1 is a subsequence of string2.

3. Write a function to return all prime numbers up to a given integer N.

Given an integer N, write a function that returns a list of all of the prime numbers up to N. Note: Return an empty list if there are no prime numbers less than or equal to N.

4. Create a function to add the frequency of each character in a string after each character.

Given a string sentence, return the same string with an addendum after each character of the number of occurrences a character appeared in the sentence. Do not treat spaces as characters and exclude characters in the discard_list.

5. Develop a function sorting to sort a list of strings in ascending alphabetical order from scratch.

Given a list of strings, write a function sorting to sort the list in ascending alphabetical order without using the built-in sorted function. Return the new sorted list rather than modifying the list in-place. Bonus: Aim for a solution with (O(n \log n)) complexity.

6. How would you explain what a p-value is to someone who is not technical?

Explain the concept of a p-value in simple terms to someone without a technical background.

7. What is the probability that a red marble was pulled from Bucket #1?

You have two buckets with different distributions of red and black marbles. If a red marble is pulled, calculate the probability it came from Bucket #1.

8. What is the probability that Amy wins the game by rolling a “6” first?

Amy and Brad take turns rolling a fair six-sided die, with Amy starting first. Calculate the probability that Amy wins by rolling a “6” before Brad.

9. How would you evaluate if a decision tree algorithm is the correct model for predicting loan repayment?

As a data scientist at a bank, you need to build a decision tree model to predict if a borrower will repay a personal loan. How would you determine if a decision tree is the appropriate algorithm for this problem?

10. What factors could have biased Jetco’s fastest average boarding times result?

Jetco had the fastest average boarding times in a study. Identify potential biases in the study and what factors you would investigate to validate the result.

11. How would you ensure data quality across different ETL platforms for PayPal’s Southern African division?

PayPal uses multiple ETL pipelines to connect data marts with a survey platform’s data warehouses, including translation modules for text data. Describe how you would ensure data quality across these ETL platforms.

12. How would you build a model to predict which merchants DoorDash should acquire in a new market?

As a data scientist at DoorDash, you need to build a model to predict which merchants to target for acquisition when entering a new market. Explain your approach.

13. How would you debug the marriage attribute marked ‘TRUE’ for all auto insurance clients?

You find that the marriage attribute is marked ‘TRUE’ for all auto insurance clients. Describe how you would debug this issue, what data you would look into, and how you would determine who is actually married.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Experian data engineer interview include:

• Know Your Machine Learning Algorithms: Be prepared to answer questions related to machine learning algorithms such as KNN, K-means, L1 vs. L2, and random forests. Understanding these algorithms in depth is crucial.
• Brush Up on SQL and Python: Experian technical interviews often include SQL and Python assessments. Ensure you have strong development skills in these languages, including practical experience with complex SQL queries and Python data manipulation.
• Prepare for Group Activities: During the assessment center, you might need to participate in group activities and problem-solving scenarios. Focus on showcasing your teamwork, logical thinking, and problem-solving skills.

What is the average salary for a Data Engineer at Experian?

According to Glassdoor, Data engineers at Experia earn between $101K to $140K per year, with an average of $119K per year.

What qualifications are required for the Data Engineer position at Experian?

Candidates should have extensive experience in modern data manipulation and preparation via SQL, preferably within Redshift and/or PostgreSQL. Proficiency in Python or other object-oriented languages, CI/CD pipeline construction, and general knowledge of AWS services like Glue and Lambda are also essential. Additionally, experience with Git, automation skills, and the ability to design and create solutions are crucial.

What specific responsibilities will a Senior Data Engineer at Experian have?

Responsibilities include constructing complex datasets using custom SQL, developing full lifecycle data solutions from data ingestion to real-time and historical reports, building reusable data tools, enabling DevOps model usage, and ensuring security components are implemented via automation. The role also involves training team members and supporting US Operations.

What’s the company culture like at Experian?

Experian values innovation, diversity, and a people-first approach. They emphasize DEI, work/life balance, development, authenticity, and collaboration. Experian celebrates individuality and is committed to creating a supportive and inclusive environment where everyone can bring their whole selves to work.

Landing a job as a Data Engineer at Experian is not just about technical prowess—it’s about fitting into a team that’s shaping the future of data-driven insights and solutions. From an initial recruiter outreach, through various interview stages including technical questions and assessments, to understanding business logic, the process is exhaustive yet rewarding. Experian’s commitment to innovation and a supportive, inclusive culture ensures that your career here can thrive.

Want more insights about the company? Check out our main Experian Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as Software Engineer and Data Analyst, where you can learn more about Experian’s interview process for different positions.

Good luck with your interview!