Deloitte Data Engineer Interview Questions + Guide in 2024

Deloitte is a high-profile “Big Four” accounting and “professional services” organization, known for providing audit, consulting, risk advisory, tax, and legal services. With a diverse client base across industries and around the world, Deloitte heavily relies on its Data Engineers to build pipelines, maintain databases, and collaborate with analysts to provide clean data and deploy models.

Being a Data Engineer at Deloitte offers several perks, such as exposure to high-impact projects across sectors, opportunities for professional growth through continuous learning, and access to a global network.

This interview guide provides a detailed overview of the Deloitte Data Engineer interview process. It includes commonly asked interview questions and practical tips to enhance your chances of securing the position.

Deloitte’s interview process usually consists of three or four interview rounds, but may differ based on the team and seniority of the position.

1. Application

You can apply for jobs through their website, recruiters, or trusted online platforms. You can consider asking for an employee referral through your network when you apply as well. Take some extra time to check that your resume highlights projects that are relevant to the role, focusing on quantifying the success of key projects, as well as some of your soft skills. These are both qualities that Deloitte looks for in promising candidates.

2. Recruiter Screening

In this first step of the selection process, a recruiter assesses your background, experience, and fit for the role. Make sure to use this opportunity to ask the recruiter questions to understand the position, and prepare some canned responses to advocate for yourself as a strong candidate.

3. Pre-recorded Interview

This is often a HireVue round where you are asked to record your answers to several interview questions (usually technical) and respond to coding problems. You may also be asked to solve SQL and Python challenges on a platform like Codility. This round aims to assess your expertise virtually to ensure you are a good fit for the in-person rounds later.

4. Video Interview(s)

If it is a good fit, you will be invited to meet a partner and/or a senior manager over a video call, although in some cases you may be asked to visit on-site. There are usually three or four rounds of these discussions, involving a mix of technical, behavioral, and situational interview questions.

Interview tips from Deloitte’s careers page:

1. We’re impressed when candidates have taken the time to do some research and learn about us. 

2. Recent trends have dictated business casual attire, but it’s still appropriate to wear a business suit.

3. When you want to learn more about who we are and what we do, it lets us know you’re interested. 

4. Specific examples of how you’ve contributed to an organization or learned something exciting are of interest to us.

You will be expected to be technically sound in SQL, big data tools like Spark, basic data structures, and Azure. Questions will also cover topics like data normalization, database design, data warehousing schemas, and an understanding of data pipelines. Considering Deloitte’s emphasis on teamwork and client interaction, expect behavioral questions about your past experiences working on teams, handling tight deadlines, and your approach to client communication.

For a more in-depth discussion, look through our list below as we’ve hand-picked popular questions that have been asked in Deloitte’s Data Engineer interviews in the past:

1. Describe a challenging data engineering project you handled. How did you manage the complexities and what was the outcome?

As a Deloitte Data Engineer, you may have to work on projects with large, unstructured datasets from multiple sources, requiring intricate ETL processes. This question is typically asked to assess your experience in such challenges.

How to Answer

Focus on a specific project, outlining the challenge, your approach, the technologies used, and the impact of your solution. Use the STAR method of storytelling - discuss the Specific situation you were challenged with, the Task you decided on, the Action you took, and the Result of your efforts.

Example

“In my previous role, I led a project to integrate real-time data from multiple IoT devices into our existing data warehouse. This presented challenges in terms of data volume and variety. I designed a scalable ETL pipeline using Apache Kafka for real-time data ingestion and Apache Spark for processing. My solution streamlined data flow, reduced latency by 40%, and enabled more accurate real-time analytics, significantly enhancing our predictive maintenance capabilities.”

2. Why do you want to join Deloitte?

Interviewers will want to know why you specifically chose the Data Engineer role at Deloitte. They want to establish if you’re passionate about the company’s culture and values, or if your interest is much more opportunistic.

How to Answer

Your answer should reflect knowledge of Deloitte’s work, culture, and the specific opportunities that attract you to the company. It’s important to be honest and specific about how Deloitte’s offerings align with your career goals.

Example

“I am eager to join Deloitte because of its reputation for fostering a culture of continuous learning and innovation. I’m particularly attracted to the opportunity to work on diverse and challenging projects across various industries, which aligns perfectly with my goal of developing a versatile skill set in data engineering. Additionally, Deloitte’s commitment to social impact resonates with my values, and I am excited to contribute to projects that have a meaningful impact on society.”

3. What are your thoughts on work-life balance?

Companies like Deloitte are interested in candidates who can maintain high performance while also ensuring personal well-being, which is crucial for long-term job satisfaction and productivity.

How to Answer

Be honest about your preferences. Emphasize your ability to manage time efficiently, and your understanding of the importance of maintaining a healthy balance. It’s also beneficial to acknowledge the demanding nature of the job and how you plan to navigate it.

Example

“I believe that maintaining a healthy work-life balance is essential for long-term professional success and personal well-being. In my experience, being organized and prioritizing tasks effectively allows me to maintain high productivity, while also dedicating time to rest and personal activities. I understand that there will be peak times when longer hours are necessary, especially in a dynamic environment like Deloitte’s, but I also value the importance of recharging to sustain consistent performance. I’m keen on being part of a team that values efficiency and productivity, as well as the well-being of its members.”

4. How do you prioritize multiple deadlines?

You may need to work across teams, projects, and even geographies in a global organization like Deloitte. Time management and organization are essential skills to succeed.

How to Answer

Emphasize your ability to differentiate between urgent and important tasks. Mention any tools or frameworks you use for time management. It’s also important to showcase your ability to adjust priorities.

Example

“In a previous role, I often juggled multiple projects with tight deadlines. I prioritized tasks based on their impact and deadlines using a combination of the Eisenhower Matrix and Agile methodologies. I regularly reassessed priorities to accommodate any changes and communicated proactively with stakeholders about progress and any potential delays.”

5. Where do you see yourself in five years?

Your interviewer needs to know if your professional growth goals align with the opportunities they offer, and how you plan to contribute to the company’s success while advancing your own skills.

How to Answer

Discuss specific skills and experiences you hope to gain, and how they align with the role at Deloitte. Mention your commitment to continuous learning and how you plan to keep up with industry trends and advancements.

Example

“Over the next five years, I aim to evolve into a more strategic role in data engineering. I plan to deepen my expertise in cloud computing and big data technologies, which I know are key focus areas at Deloitte. I also intend to develop my leadership skills by mentoring junior team members and leading project teams. With Deloitte’s diverse client portfolio and emphasis on professional development, I see a tremendous opportunity to achieve these goals while contributing significantly to the company’s innovative projects.”

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

You will need to demonstrate your understanding of window functions in solving specific data retrieval problems, as such operations are necessary for the day-to-day coding requirements for a Data Engineer at Deloitte.

How to Answer

Choose the function that best fits the requirement of the query. In this case, DENSE_RANK or RANK would be appropriate, as they can handle ties in salary values. Explain how you would use the chosen function in a subquery to achieve the desired result.

Example

“I would use the DENSE_RANK function. This function will assign ranks to salaries within the department while handling ties appropriately. I would create a subquery that assigns a rank to each salary using DENSE_RANK, ordered in descending order, and then in the outer query, I would select the salary where the rank is 2. This approach ensures that if multiple employees share the highest salary, the query will still return the true 2nd highest salary.”

7. What is the difference between head() and take() in Spark?

It’s important in data engineering roles to know the nuances of these methods, as they are used for retrieving data from distributed collections like RDDs or DataFrames.

How to Answer

Clearly explain the main differences between head and take in terms of functionality and usage. It’s also helpful to mention any performance implications or scenarios where one method is preferred over the other.

Example

“In Apache Spark, both head and take are used to retrieve elements from an RDD or DataFrame. The primary difference is in their return types and usage. head() retrieves the first element of the dataset and returns it as a single object, similar to first(), while take(n) returns an array of the first ‘n’ elements. Additionally, head is more commonly used when you’re sure your dataset contains only one element or when you’re only interested in the first record, whereas take is useful for retrieving a specified number of elements for analysis or inspection. In terms of performance, both methods trigger action on the RDD or DataFrame and can be costly if not used judiciously on large datasets.”

8. Given a list of sorted integer lists, write a function to create a combined list while maintaining sorted order without importing any libraries or using sort functions in Python.

To streamline logistics for clients, Data Engineers at Deloitte need to understand concepts like merging sorted integer lists without resorting to Python’s built-in functions.

How to Answer

Go through a step-by-step approach to merge the lists while maintaining sorted order. Focus on how you iterate through each of the lists to compare elements, choose the smallest element at each step, and add it to the final merged list.

Example

“In my solution, I’ll maintain an array of pointers to track the current element in each list. At each step, I’d look for the smallest element in each list and add it to the merged list. This way, we effectively merge all lists while maintaining their sorted order. This approach is efficient because it can minimize comparisons and eliminate the need for any external sorting library. Each iteration involves only a comparison of the heads of the remaining lists.”

9. How would you optimize a large-scale data warehouse in Azure Synapse Analytics for both performance and cost efficiency?

Deloitte would expect its Data Engineers to effectively balance data processing with operational costs.

How to Answer

Discuss strategies like partitioning data, indexing, choosing the right file format, managing resource classes, and scaling. Highlight the importance of monitoring and adjusting resources based on usage patterns.

Example

“Partitioning large tables can significantly improve query performance. I’d use columnstore indexing for faster query execution on large datasets. Choosing the right file format, like Parquet for analytical workloads, is also crucial. Additionally, managing and scaling resources based on workload demands can optimize costs. Utilizing Synapse’s tools to continuously monitor query performance and resource utilization is critical to fine-tune the balance between performance and cost.”

10. Write a function that takes an N-dimensional array as input and returns a 1D array.

This tests your algorithmic thinking and proficiency in handling complex data structures. It’s extremely relevant, as Data Engineers often need to process and transform multi-dimensional data into a more manageable form to analyze.

How to Answer

Describe a methodical approach to recursively traverse and flatten the nested lists. Emphasize the importance of considering various levels of nesting and different data types within the lists.

Example

“I’d write a function to check each element of the input array; if the element is a list, the function would recursively flatten it. If the element is not a list (i.e., an integer), it would be added directly to the output array. This method ensures that all nested lists, regardless of their depth, are properly flattened, and all integers are collected into a single, one-dimensional array.”

11. What is check-pointing in Spark?

The interviewer wishes to check your understanding of fault tolerance mechanisms in Apache Spark. It’s a technical skill required for Deloitte Data Engineers to ensure data processing reliability in distributed computing environments.

How to Answer

Explain what checkpointing is, its role in Spark, and how it contributes to fault tolerance. Highlight the difference between checkpointing and other persistence mechanisms like caching.

Example

“Checkpointing is a process used to truncate the lineage of RDDs (Resilient Distributed Datasets). It’s a fault tolerance technique where the RDD data is saved to a reliable storage system like HDFS. Checkpointing is particularly useful in long iterative algorithms or streaming applications, where the lineage of RDDs becomes too long, potentially leading to performance issues. By saving the RDD’s state at certain intervals, checkpointing helps in quick data recovery in case of node failures, as Spark can restart computations from the checkpointed data rather than recomputing from the beginning. This is different from caching or persistence, which primarily aim to improve performance by storing data in memory for quicker access.”

12. Given a list of strings, write a function from scratch to sort the list in ascending alphabetical order.

This is a foundational concept in Python that can be applied to your day-to-day coding requirements at Deloitte.

How to Answer

Describe a straightforward approach to sorting a list of strings through a basic algorithm like bubble sort, merge sort, etc.

Example

“I would implement a merge sort, which is efficient for larger lists and maintains a time complexity of O(n log n). This algorithm divides the list into halves, recursively sorts each half, and then merges the sorted halves back together.”

13. Given a sales database with two tables, Orders and Products, where Orders contains columns ‘OrderID’, ‘ProductID’, and ‘OrderDate’, and Products contains ‘ProductID’, ‘ProductName’, and ‘Price’, can you write a SQL query to find the total revenue generated by each product for the current year?

This tests your ability to work with SQL joins, aggregate functions, and date operations, skills that are essential in data engineering for generating insights from relational databases.

How to Answer

Explain your approach, starting with a join between the Orders and Products tables on column ‘ProductID’. Then, discuss using aggregate functions to calculate total revenue per product, and filtering the data for the current year using a date function.

Example

“I would join the Orders and Products tables using column ‘ProductID’. Then, I would use SUM to calculate the revenue per product, which is the product of the ‘Price’ from the Products table and the quantity of orders from the Orders table. To focus on the current year, I would use a WHERE clause with a date function to filter ‘OrderDate’ to only include dates from this year. The final query would group the results by ‘ProductID’ and ‘ProductName’ to provide a clear revenue breakdown per product.”

14. You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).

This task is important in scenarios where binary trees represent relationships within datasets, such as product hierarchies or organizational structures.

How to Answer

Discuss the properties of a valid BST and explain your approach to traversing the tree. You should talk about the use of recursion and the importance of considering edge cases.

Example “I would use a recursive approach to solve this problem. In a BST, a key property is that the left subtree of a node contains nodes with keys lesser than the node’s key, and the right subtree only nodes with keys greater. I would implement a function that traverses the tree in order and checks if the value of each node is greater than the previously visited node. It’s also important to handle edge cases, like ensuring the approach works for trees with all nodes on one side and considering how to handle duplicate values.”

15. What is the difference between MapReduce and Spark programming?

Knowledge of these frameworks is crucial in data engineering roles to choose the right tool for data processing tasks. Understanding the differences between MapReduce and Apache Spark is particularly relevant at Deloitte due to the variety of data projects the firm handles.

How to Answer

Focus on key differences such as execution methods, performance, ease of use, and real-time processing capabilities. Explain how these differences impact the choice of tool in various data processing scenarios.

Example

“MapReduce and Apache Spark are both big data processing frameworks, but they differ significantly in their execution and performance. MapReduce, part of the Hadoop ecosystem, operates linearly and sequentially, processing data in two stages - Map and Reduce. It writes intermediate results to disk, which can be less efficient for complex computations. On the other hand, Spark performs in-memory processing, which is faster, and it can handle iterative algorithms more efficiently. Spark also offers more than just Map and Reduce functions, including support for SQL queries, streaming data, and machine learning. This makes Spark the preferred choice for real-time analytics and iterative data processing.”

16. Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

This tests your understanding of one-to-one relationships between elements. At Deloitte, engineers often perform data synchronization between different systems for clients. Establishing a one-to-one correspondence between identifiers is crucial to ensure accurate mapping and data integrity.

How to Answer

Describe an approach that checks if each character in string1 maps uniquely to a character in string2, and vice versa. Mention the importance of considering the length of the strings and the use of data structures like hash maps for tracking character mappings.

Example

“My function would first check if both strings are of equal length; if not, a bijection isn’t possible. If they are of the same length, I would use two hash maps to track the mappings from string1 to string2 and from string2 to string1. The function iterates through the characters of the strings simultaneously, updating and checking the maps. If at any point, a character in one string maps to more than one character in the other, the function returns false, indicating no bijection exists. If the iteration completes without such conflicts, the function returns true.”

17. How would you handle data loss during a migration?

As a Data Engineer, you need to be able to handle unprecedented situations and mitigate risks associated with data migration. The interviewer wants to test your ability to implement robust data management practices.

How to Answer

You should talk about focusing on preventive measures, immediate response strategies, and long-term solutions. Emphasize the importance of comprehensive planning and creation of backup strategies before migration. Then, discuss the steps you would take to identify and assess the extent of data loss if it occurs. Finally, mention how you would restore the lost data from backups and implement measures to prevent future occurrences.

Example

”I would focus on preventive measures, such as ensuring thorough backups are made before starting the migration.

If data loss is detected during migration, the first step is to pause the migration process to prevent further loss. I would then assess the scope of the loss, using data reconciliation processes and integrity checks against the backup. Once the affected data is identified, I will restore it from the backups. After addressing the immediate issue, I would analyze the cause of the data loss to prevent future occurrences. Continuous monitoring and validation are key in subsequent migrations to ensure data integrity throughout the process.”

18. Let’s say we have a table with an id and name fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database. Write a query to randomly sample a row from this table.

It’s important in data engineering to know how to retrieve data samples without imposing a significant load on the database, especially when dealing with very large tables.

How to Answer

Explain a method for randomly selecting a row in a way that minimizes the load on the database. Focus on SQL functions or methods that enable random sampling.

Example

“I’d generate a random row number (between 1 and the number of rows in the table). Then, I’d write a query to return the first row that exists in the table after the random row generated in step 1.”

19. What will happen if your Spark job fails at 40% completion?

This question is aimed at assessing your understanding of fault tolerance and error handling.

How to Answer

Discuss the mechanisms of fault tolerance in the context of Spark. Explain how these systems handle partial job failures and the processes for job recovery or restart.

Example

“If a job fails at 40% completion, the system’s fault tolerance mechanisms kick in. Spark uses lineage information of the RDDs to recompute only the lost data partitions. Due to its in-memory processing, Spark can quickly reprocess these partitions rather than restarting the job from scratch. The system will attempt to continue the job from the point of failure, leveraging its DAG (Directed Acyclic Graph) execution model to determine which parts of the data need to be recomputed. This approach ensures efficient recovery with minimal performance impact. I would also address the underlying cause of the failure to prevent future occurrences.”

20. Given a table of bank transactions with columns: id, transaction_value, and created_at, representing the date and time for each transaction, write a query to get the last transaction for each day.

You may need to quickly extract such insights for end-of-day reports or financial summaries for your clients at Deloitte.

How to Answer

Explain the benefits of using a window function here, and how the ORDER BY clause within it helps in determining the latest transaction.

Example

*“I would use a window function like ROW_NUMBER(), partitioning the data by the date portion of the created_at column and ordering by* created_at in descending order within each partition. This setup will assign a row number of 1 to the last transaction of each day. Then, I would wrap this query in a subquery or use a CTE to filter out the rows where the row number is 1. The final output would be ordered by the created_at datetime to display the transactions chronologically. This approach ensures we get the last transaction for each day without missing any days.“

Here are some tips to help you excel in your interview:

Research Deloitte and Its Projects

Understand Deloitte’s business model, the types of clients they work with, and the kinds of data projects they handle. Familiarize yourself with their culture, values, and any recent news or big projects.

You can also read Interview Query members’ experiences on our discussion board; although we do not have any Deloitte interview discussions yet, you can find plenty of insider tips for data engineering roles at similar firms.

Visit Deloitte’s career page to understand what they look for in potential candidates.

Review Core Engineering and Programming Concepts

Refresh your knowledge in areas like database management, ETL processes, data warehousing, big data technologies (like Hadoop and Spark), and cloud platforms (AWS, Azure, GCP). Be prepared to discuss data modeling, data pipeline design, and optimization. It goes without saying that you should be proficient in SQL and Python. Review SQL functions such as window functions and joins.

Check out our data engineering guides specifically for SQL and Python interview questions, or practice some cool data engineering projects to bolster your resume.

If you need further guidance, we also have a tailored data engineering learning path covering core topics and practical applications.

Prepare Behavioral Interview Answers

Deloitte values soft skills. Be ready to discuss your experiences with teamwork, communication, handling deadlines, and client interactions.

To test your current preparedness for the interview process, try a mock interview to improve your communication skills.

Understand Data Security and Compliance

At Deloitte, you will be expected to handle sensitive data. Depending on the exact role, you may have to know about data governance, privacy laws (like GDPR), and methods to secure data, such as encryption and access controls. Check the job description document to ensure that you’re covering all your bases.

What is the average salary for a Data Engineering role at Deloitte?

The average base salary for a Data Engineer at Deloitte is US$94,273, making the remuneration competitive for prospective applicants.

For more insights into the salary range of Data Engineers at various companies, check out our comprehensive Data Engineer Salary Guide.

Where can I read more discussion posts on the Deloitte Data Engineer role here in Interview Query?

Here is our discussion board where Interview Query members talk about their interview experiences, although we do not have any posts specifically on the Deloitte experience, yet. You can use the search bar and filter for data engineering posts, though - we have quite a few posts on those!

Are there job postings for Deloitte Data Engineer roles on Interview Query?

We have jobs listed for Data Engineer roles in Deloitte, which you can apply for directly through our job portal. You can also have a look at similar roles that are relevant to your career goals and skill set, by filtering on location, company, and position.

Succeeding in a Deloitte Data Engineer interview requires solid technical skills as well as the ability to demonstrate your collaborative and critical thinking persona.

If you’re considering opportunities at other companies, check out our Company Interview Guides. We cover a range of similar companies, so if you are looking for Data Engineer positions in the other Big 4 firms you can check our guides for PWC, EY, KPMG, and more.

For other data-related roles at Deloitte, consider exploring our Business Analyst, Data Scientist, and similar guides in our main Deloitte interview guide.

With a solid interview strategy, you can confidently approach the interview and showcase your potential as a valuable employee to Deloitte. Check out more of our content here at Interview Query, and we hope you’ll land your dream role very soon!