Kpmg Data Engineer Interview Questions + Guide in 2024

KPMG is a global network of professional service firms providing Audit, Tax, and Advisory services, known for its inclusive culture and innovative approach. As a Data Engineer at KPMG, you will be responsible for building and optimizing complex data pipelines and systems. The role requires strong technical skills in SQL, Spark, ADF, and data modeling, along with experience in cloud technologies such as Azure or AWS.

In this guide, we’ll navigate through KPMG’s multi-stage interview process, covering sample KPMG data engineer interview questions, and provide insights 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 a KPMG data engineer interview:

Recruiter/Hiring Manager Call Screening

If your application is shortlisted, a KPMG recruiter will reach out to verify essential details, such as your experience and skill level. During this call, you may also face some behavioral questions.

In some instances, the hiring manager for the data engineering role might join the screening call to answer any questions you might have about the role and the company. They might also touch on some technical and behavioral aspects during this conversation.

This recruiter call generally lasts about 30 minutes.

HackerRank Assessment

You will likely be asked to complete a HackerRank assessment following the initial screening. This will include two coding questions, SQL coding tasks, and Spark multiple-choice questions (MCQs). Successfully passing this assessment will move you to the following interview stages.

Technical Virtual Interviews

After clearing the HackerRank round, you will be invited to participate in multiple technical virtual interview rounds, each lasting approximately 30 minutes. These interviews will assess various aspects of your technical skills:

1. First Round: Technical Introduction

   • You’ll be introduced to the team.
   • Questions on essential project work.

2. Second Round: Senior Technical Round

   • In-depth questions on Azure Data Factory (ADF), Spark, and databases.

3. Third Round: Coding Round

   • Both multiple-choice questions (MCQs) and coding tasks in Python or Spark.

4. Fourth Round: Managerial Round

   • Focuses on managerial and technical project-based questions.
   • Important topics include Windows functions, ADF, Spark, and databases.

Onsite Interview Rounds

Once the virtual technical interviews are completed, a second recruiter call will brief you about the onsite interview process. The onsite interview loop at KPMG will include multiple rounds designed to evaluate your technical skills and cultural fit meticulously:

1. Project Presentation

   • You’ll present a project to technical leads.
   • Questions will revolve around your presentation and technical competencies.

2. Director Round

   • Focuses on competency questions and technical knowledge.
   • Questions could span topics such as the architecture of Oracle vs. Teradata and differences between Avro, Parquet, ORC, and HDFS architecture.

Final HR and Offer Discussion

After the onsite interviews, the final step involves a brief discussion with the HR team. This may cover salary negotiations, company policies, and other offer-related logistics. Ensure that the expected and agreed-upon salary terms are respected; if previously discussed salary expectations change, communicate your stance.

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

1. What kind of analysis would you conduct to recommend changes to the UI?

You have access to tables summarizing user event data for a community forum app. Conduct a user journey analysis using this data to recommend changes to the user interface.

2. How would you assess the validity of the result in an AB test with a .04 p-value?

Your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM finds a .04 p-value. Assess the validity of this result.

3. How would you debug and improve the efficiency of a slow SQL query?

You are running a SQL query that is taking a long time. Determine how to know if it’s taking too long and how to debug the issue to improve efficiency.

4. How would you determine how much Spotify should pay for an ad in a third-party app?

You are a data scientist on the marketing team for Spotify. Determine how much Spotify should pay for an ad in a third-party app.

5. Write a SQL query to select the 2nd highest salary in the engineering department.

Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

6. How can we model the electricity supply for a town to avoid outages and waste?**

PG&E needs to forecast the exact amount of electricity to supply a town each year. The goal is to avoid outages from under-supply and waste from over-supply. What is one way to model this?

7. How do sentiment analysis models convert raw text data into numerical vectors and get trained?

To perform sentiment analysis on an Amazon customer feedback dataset, you need to convert raw text data into numerical vectors. Explain how these models work and how they are trained.

8. How would you assess the effectiveness of a clustering model for grouping similar basketball players without pre-labeled data?

As a data scientist for a basketball team like the LA Lakers, you need to identify and cluster similar players. After training a clustering model, how would you evaluate if it successfully grouped similar players without having pre-labeled data?

9. How can PG&E model the electricity supply needed for a town?

PG&E needs to forecast the exact amount of electricity to supply a town each year. Supplying too little causes outages, while supplying too much wastes money. What is one way to model the required electricity supply?

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 KPMG data engineer interview include:

• Solidify Your Technical Foundation: Brush up on key technical areas such as SQL queries, data warehousing concepts, data modeling, cloud ETL tools, and Spark. Make use of Interview Query to practice relevant questions.
• Know the Tools and Technologies: Familiarize yourself with tools like Azure Data Factory, Databricks, and database optimizations. Practical knowledge of cloud technologies (AWS, GCP) and big data platforms (Hadoop, Hive) is crucial.
• Prepare for Behavioral Questions: Thoroughly prepare for questions about team-building, project management, and scenarios that test your problem-solving and leadership skills.

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

What is KPMG’s work culture like for a Data Engineer?

KPMG is known for its collaborative and innovative work culture. You can expect a supportive environment that encourages creativity and continuous learning. The company is dedicated to diversity and inclusion, making it a great workplace for personal and professional growth. Moreover, KPMG offers competitive benefits and numerous opportunities for advancement within the organization.

How should I handle salary negotiations with KPMG?

Be clear and upfront about your expected CTC (Cost to Company). Make sure to: 1. Clearly communicate your salary expectations during the initial discussions with HR. 2. Provide all necessary documents for background checks promptly. 3. Hold firm on your minimum acceptable offer as discussed. Using Interview Query is an excellent way to understand industry standards and tailor your expectations accordingly.

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

According to Glassdoor, Data Engineers at KPMG earn between $93K to $141K per year, with an average of $114K per year.

What is KPMG’s work culture like for a Data Engineer?

KPMG is known for its collaborative and innovative work culture. You can expect a supportive environment that encourages creativity and continuous learning. The company is dedicated to diversity and inclusion, making it a great workplace for personal and professional growth. Moreover, KPMG offers competitive benefits and numerous opportunities for advancement within the organization.

How should I handle salary negotiations with KPMG?

Be clear and upfront about your expected CTC (Cost to Company). Make sure to: 1. Clearly communicate your salary expectations during the initial discussions with HR. 2. Provide all necessary documents for background checks promptly. 3. Hold firm on your minimum acceptable offer as discussed. Using Interview Query is an excellent way to understand industry standards and tailor your expectations accordingly.

Navigating the interview process for a Data Engineer position at KPMG offers a mixed bag of experiences, from engaging technical rounds to challenging managerial discussions. Despite occasional hiccups in the recruitment process, candidates often praise the technical prowess and professionalism of KPMG’s interviewers. For those aspiring to join this prestigious firm, understanding the multi-faceted nature of their interviews—from SQL and data engineering concepts to detailed project presentations—is essential.

To bolster your preparation and increase your chances of success, explore our KPMG Interview Guide on Interview Query. This guide covers a range of potential questions and role-specific strategies, providing you with the insights needed to confidently tackle the interview process. Dive into our comprehensive resources and transform your interview journey with Interview Query.

Good luck with your interview!