Accenture Data Engineer Interview Questions + Guide in 2024

With a total FY23 revenue of $64.1B, Accenture is a global professional service company that provides a wide range of consulting and outsourcing services, including application development, cloud solutions, and improving business processes to clients across various industries.

Data engineers at Accenture typically play critical roles in designing, building, and maintaining data pipelines and infrastructure to support data-driven projects. Your responsibility, as a data engineer at Accenture, may also extend to managing and optimizing databases, data warehouses, and maintaining data pipelines to ETL data from various sources.

As a candidate for the data engineer interview at Accenture, you’ve come to the right place to learn more about the process and prepare for the upcoming Accenture data engineer interview questions.

The Accenture Data Engineer interview process typically consists of multiple rounds designed to evaluate your technical proficiency, problem-solving skills, and cultural fit within the company. Expect the following stages:

Recruiter Phone Interview Round

The Accenture data engineer interview process usually begins with a conversation with a recruiter. This is a preliminary phone interview where they assess your resume, experience, and motivation for applying to Accenture. They might also ask about your background, why you’re interested in the role, and your familiarity with data engineering concepts.

Furthermore, expect some behavioral questions gauging your soft skills, teamwork, and how you handle challenges in a work environment. Check out the interview questions section for the behavioral questions.

Online Coding Tests and Technical Assessments

If the recruiters are satisfied with our answers, you might be asked to complete an online coding test, typically on Accenture’s proctored portal. The test could include problems related to data structures, algorithms, SQL queries, and basic programming in languages like Python or Java, whichever you prefer.

You might also be presented with data engineering-specific assignments, such as ETL processes, data modeling, and working with big data tools like Hadoop, Spark, or cloud platforms.

Technical Interview Rounds

If you pass the initial screening and online test, you’ll likely have one or more technical interviews. These interviews are usually conducted by senior data engineers or technical leads at Accenture. Expect to solve coding problems in real time, either on a whiteboard or in a collaborative coding environment. The focus will be on data structures, algorithms, and SQL questions.

Moreover, you may be asked to design a data pipeline or system architecture. This could involve designing a scalable ETL process, optimizing a data storage solution, or discussing how to handle large-scale data processing.

Behavioral Interview Round

The behavioral interview stage focuses on assessing your cultural fit and alignment with Accenture’s values. Expect in-depth questions about your previous work experiences, challenges you’ve faced, and how you’ve collaborated with teams.

Situational questions or data sense questions, might also be presented with hypothetical scenarios to see how you’d approach real-world problems. For example, you could be asked how you would handle a failing data pipeline or optimize a slow query in a production environment.

On-Site Technical Presentation and Interview

Technical presentations are typical in most data-related roles at Accenture. You’ll be expected to present your findings and approach to a panel. This is an opportunity to showcase your technical expertise, communication skills, and ability to explain complex concepts clearly.

In some cases, you may also be given a case study related to data engineering. This could involve analyzing a dataset or designing a data solution.

Here are some Accenture data engineer questions that may occur in our upcoming interview:

1. What is your approach to resolving conflict with co-workers or external stakeholders, partially when you don’t really like them? Give an example of when you resolved a conflict with someone on the job.

2. Tell me a time when your colleagues did not agree with your approach. What did you do to bring them into the conversation and address their concerns?

3. Describe an analytics experiment that you designed. How were you able to measure success?

4. How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?

5. What are you looking for in your next job?

6. Given a table called employees, get the largest salary of any employee by department

7. Write a function named grades_colors to select only the rows where the student’s favorite color is green or red and their grade is above 90.

8. Return a list of values where they occur only once. Note: You can return the values in any order.

9. Write a function ugly_powers(s: set) -> bool which takes a set ss and returns a boolean value determining whether or not all the elements of set ss are all ugly powers.

10. Let’s say we have a table representing vacation bookings. Write a query that returns columns representing the total number of bookings in the last 90 days, last 365 days, and overall.

11. Why is it standard practice to explicitly put foreign key constraints on related tables instead of creating a normal BIGINT field? When considering foreign key constraints, when should you consider a cascade delete or a set null?

12. Let’s say you’re tasked with building the YouTube video recommendation algorithm. How would you design the recommendation system? What are important factors to keep in mind when building the recommendation algorithm?

13. Write a query to create a metric that can validate and rank the queries by their search result precision. Round the metric (avg_rating column) to 2 decimal places.

14. Given two sorted lists, write a function to merge them into one sorted list.

15. How would you design a data pipeline to handle a sudden surge in data volume, ensuring minimal performance degradation?

16. Describe a strategy for identifying and correcting data anomalies, inconsistencies, or missing values in a large-scale dataset.

17. You’re tasked with optimizing an existing ETL process that’s experiencing performance bottlenecks. How would you approach this, and what tools or techniques would you consider?

18. Explain how you would implement a real-time data processing pipeline to analyze streaming data from IoT devices and provide immediate insights.

19. Design a data architecture for a microservices-based application, considering factors like data consistency, scalability, and fault tolerance.

20. Given two paragraphs, return the top 3 stop words with the most occurrences. Stop words are basically a set of commonly used words.

from collections import Counter

import re

def findStopWords(paragraphs):

    p = paragraphs.split()

    p1 = []

    for w in p:

        w1 = re.findall(r”[\w’]+”, w)

        p1.append(w1)

    d = Counter(p1)

    d_sort = sorted(d.items(), key = lambda x: x[1], reverse = True)

    res = [i[0] for i in d_sort[:3]]

    return res

Preparing for a Data Engineer interview at Accenture requires a strategic approach to demonstrate your technical skills, problem-solving abilities, and understanding of data engineering concepts. Here’s a step-by-step guide to help you get ready:

Understand Accenture’s Business and Role

Gain a deep understanding of Accenture’s business model, its services, and its focus on digital transformation, technology consulting, and data-driven solutions.

Review your job description carefully to understand the specific responsibilities, required skills, and technologies used in the data engineer role at Accenture. Moreover, personalize your resume according to the information.

Ensure a Strong Foundation on Core Data Engineering Concepts

Brush up on concepts related to data structures and algorithms. Practice coding problems related to these topics. Review the concepts of Extract, Transform, and Load (ETL) processes, including data cleaning, data transformation, and data loading techniques. Understand how ETL fits into the broader data pipeline and how it’s used in Accenture.

Moreover, be proficient in writing complex SQL queries, including joins, subqueries, window functions, and performance optimization techniques.

Familiarize Yourself With Big Data Technologies

Understand the fundamentals of Hadoop and Apache Spark, including how they handle big data processing. Know how to work with Spark’s RDDs and DataFrames. Furthermore, learn the basics of distributed computing, including concepts like data partitioning, sharding, and fault tolerance.

Refine Your Coding Skills

Practice coding problems on our platform. Focus on problems related to data manipulation, algorithmic challenges, and real-time processing. Work on complex SQL exercises that require multiple joins, aggregations, and nested queries. Also, practice optimizing queries for performance. If possible, work on data engineering projects to enhance our resume.

Prepare Product Sense and Behavioral Questions

Prepare to solve real-world problems related to data engineering, such as optimizing a slow data pipeline, handling large-scale data processing, or designing a scalable data solution.

Ensure that you have an understanding of the STAR method and how to use it to structure our answers to behavioral questions.

Participate in Mock Interviews

Conduct mock interviews focused on technical problem-solving, system design, and coding challenges through our P2P Mock Interview Portal and AI Interviewer. This helps simulate the actual interview environment and improve your performance.

What is the average salary for a Data Engineer role at Accenture?

The average base data engineer salary at Accenture revolves around $108K, with the total compensation averaging at $88K in the forms of bonuses and shares. Learn more about industry-wide Data Engineer salaries to negotiate better.

What other companies are hiring Data Engineers besides Accenture?

Many companies across various industries are actively hiring Data Engineers. Some of the more prominent ones include Walmart, Google, and Deloitte.

Does Interview Query have job postings for the Accenture Data Engineer role?

While it’s a good idea to check specific career pages of the companies, we feature job postings related to data engineering on our Job Board.

Preparing for a Data Engineer interview at Accenture in 2024 requires a solid understanding of core data engineering concepts, hands-on experience with big data technologies, and strong problem-solving skills. By focusing on the key areas covered in this guide, you can increase your chances of success.