DXC Technology Data Engineer Interview Questions + Guide in 2025

DXC Technology is a global leader in IT services, helping organizations modernize their IT infrastructures and optimize data architectures across various cloud environments.

As a Data Engineer at DXC Technology, you will be responsible for developing and maintaining scalable data pipelines that enable advanced analytics and operational intelligence. Key responsibilities include optimizing data workflows for capture, ingestion, and processing, as well as collaborating with Data Platform Architects and Machine Learning Engineers to integrate feature stores and manage model deployments. You will also provide technical leadership and mentorship to team members, ensuring that engineering processes are designed for high performance, scalability, and efficiency. A strong background in cloud solutions, particularly in secure and efficient data architecture, is essential, along with proficiency in relevant technologies such as Databricks and Agile methodologies. Furthermore, ideal candidates will possess excellent problem-solving and communication skills, as well as a proactive mindset towards continuous improvement and innovation.

This guide will equip you with insights and tailored preparation strategies to excel in your interview at DXC Technology, ensuring you present your best self and align your skills with the company's objectives.

The interview process for a Data Engineer position at DXC Technology is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the role. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, often conducted by a recruiter. This conversation usually lasts around 30 minutes and focuses on your background, experience, and motivation for applying to DXC Technology. The recruiter will also assess your fit for the company culture and discuss the role's expectations.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may include an online coding test or a technical interview where you will be asked to solve problems related to data structures, algorithms, and specific technologies relevant to the role, such as SQL, Python, and cloud services. Expect questions that evaluate your understanding of data pipelines, data ingestion, and processing workflows.

3. Technical Interview

The next stage is a more in-depth technical interview, which may involve one or more rounds. During this phase, you will engage with technical leads or senior engineers who will ask questions about your previous projects, technical skills, and problem-solving abilities. You may be required to demonstrate your knowledge of object-oriented programming, database management, and data engineering concepts. Be prepared to discuss your experience with tools like Databricks and your approach to building scalable data solutions.

4. Behavioral Interview

In addition to technical skills, DXC Technology places a strong emphasis on cultural fit and teamwork. A behavioral interview will likely follow the technical assessments, where you will be asked situational questions to gauge your interpersonal skills, leadership qualities, and how you handle challenges in a team environment. Questions may revolve around conflict resolution, collaboration, and your approach to mentorship.

5. Final Interview

The final stage often includes a managerial or HR interview. This round focuses on your long-term career goals, alignment with the company's values, and your understanding of DXC Technology's mission. You may also discuss your expectations regarding salary and benefits during this stage.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked in each of these stages.

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

Understand the Interview Structure

The interview process at DXC Technology typically consists of multiple rounds, including technical assessments and HR interviews. Familiarize yourself with the common structure: an initial HR screening, followed by technical interviews that may include coding challenges and questions related to your past projects. Knowing this will help you prepare accordingly and manage your time effectively during the interview.

Prepare for Technical Questions

As a Data Engineer, you can expect questions on data pipeline design, SQL queries, and cloud solutions. Brush up on your knowledge of data ingestion patterns, data processing workflows, and tools like Databricks and PowerBI. Be ready to discuss your experience with Agile methodologies and how you have applied them in past projects. Additionally, practice coding problems that involve data structures and algorithms, as these are often part of the technical assessment.

Showcase Your Problem-Solving Skills

During the interview, you may be presented with real-world scenarios or case studies. Be prepared to demonstrate your analytical thinking and problem-solving abilities. Use the STAR (Situation, Task, Action, Result) method to structure your responses, clearly outlining the challenges you faced, the actions you took, and the outcomes of your efforts. This will help interviewers see your thought process and how you approach complex problems.

Highlight Collaboration and Leadership Experience

DXC Technology values teamwork and collaboration. Be ready to discuss your experience working with cross-functional teams, especially with Data Platform Architects and ML Engineers. If you have led projects or mentored junior engineers, share those experiences to demonstrate your leadership skills and ability to foster a collaborative environment.

Align with Company Values

Research DXC Technology’s mission and values, and think about how your personal values align with theirs. Be prepared to articulate why you want to work for DXC and how you can contribute to their goals. This alignment can set you apart from other candidates and show that you are genuinely interested in the company.

Prepare for Behavioral Questions

Expect behavioral questions that assess your soft skills, such as communication, adaptability, and conflict resolution. Reflect on past experiences where you faced challenges or conflicts in a team setting and how you resolved them. This will help you convey your interpersonal skills and ability to work effectively in a team-oriented environment.

Ask Insightful Questions

At the end of the interview, you will likely have the opportunity to ask questions. Prepare thoughtful questions that demonstrate your interest in the role and the company. Inquire about the team dynamics, ongoing projects, or the company’s approach to innovation and technology. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.

Follow Up

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the position and briefly mention a key point from the interview that resonated with you. This small gesture can leave a positive impression and keep you top of mind for the hiring team.

By following these tips, you can approach your interview with confidence and demonstrate that you are a strong candidate for the Data Engineer role at DXC Technology. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at DXC Technology. The interview process will likely assess your technical skills, problem-solving abilities, and understanding of data engineering principles. Be prepared to discuss your experience with data pipelines, cloud solutions, and collaboration with cross-functional teams.

Technical Skills

1. Can you explain the process of building a data pipeline for batch processing?

Understanding the steps involved in creating a data pipeline is crucial for this role, as it directly relates to the responsibilities of optimizing data workflows.

How to Answer

Discuss the stages of data ingestion, transformation, and storage, emphasizing the tools and technologies you would use at each step.

Example

“I would start by identifying the data sources and then use tools like Apache NiFi for ingestion. After that, I would transform the data using Apache Spark, ensuring it’s clean and structured before loading it into a data warehouse like Amazon Redshift for analysis.”

2. What are the key differences between batch processing and stream processing?

This question tests your understanding of different data processing methodologies, which is essential for designing efficient data architectures.

How to Answer

Highlight the characteristics of each processing type, including use cases and performance considerations.

Example

“Batch processing handles large volumes of data at once, making it suitable for periodic reports, while stream processing deals with real-time data, allowing for immediate insights. For instance, I would use batch processing for monthly sales reports and stream processing for monitoring live user interactions on a website.”

3. Describe your experience with cloud platforms and how you have utilized them in your projects.

Given the emphasis on cloud solutions in the job description, this question assesses your familiarity with cloud technologies.

How to Answer

Mention specific cloud platforms you’ve worked with, the services you utilized, and the impact on your projects.

Example

“I have extensive experience with AWS, particularly with services like S3 for storage and Lambda for serverless computing. In a recent project, I used AWS Glue to automate data extraction and transformation, which significantly reduced processing time and costs.”

4. How do you ensure data quality and integrity in your data pipelines?

Data quality is critical in data engineering, and this question evaluates your approach to maintaining it.

How to Answer

Discuss the methods and tools you use to validate and clean data throughout the pipeline.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Great Expectations to ensure data meets predefined quality standards. Additionally, I set up alerts for any anomalies detected during processing to address issues proactively.”

5. Can you explain the concept of ETL and how it differs from ELT?

This question tests your knowledge of data integration processes, which are fundamental to data engineering.

How to Answer

Clarify the differences between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) and when to use each.

Example

“ETL involves transforming data before loading it into the target system, which is useful for structured data. In contrast, ELT loads raw data into the target system first, allowing for transformation later, which is beneficial for big data environments where flexibility is needed.”

Programming and Tools

6. What programming languages are you proficient in, and how have you applied them in your work?

This question assesses your technical skills and ability to implement solutions.

How to Answer

Mention the languages you are comfortable with and provide examples of how you’ve used them in data engineering tasks.

Example

“I am proficient in Python and SQL. I often use Python for data manipulation and automation tasks, while SQL is my go-to for querying databases. For instance, I wrote a Python script to automate data extraction from APIs and load it into our SQL database for analysis.”

7. Describe a challenging data engineering problem you faced and how you resolved it.

This question evaluates your problem-solving skills and ability to handle real-world challenges.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response.

Example

“In a previous project, we faced performance issues with our data pipeline due to high latency. I analyzed the bottlenecks and optimized our data transformation processes by implementing parallel processing, which reduced the overall processing time by 40%.”

8. How do you approach optimizing SQL queries for performance?

This question tests your knowledge of database optimization techniques.

How to Answer

Discuss specific strategies you use to improve query performance.

Example

“I focus on indexing key columns, avoiding SELECT *, and using JOINs efficiently. For example, I once optimized a slow-running report by creating indexes on frequently queried columns, which improved the query execution time from several minutes to under 10 seconds.”

9. What is your experience with data visualization tools, and how do you use them to communicate insights?

This question assesses your ability to present data effectively.

How to Answer

Mention the tools you’ve used and how you leverage them to convey data-driven insights.

Example

“I have experience with Power BI and Tableau. I use these tools to create interactive dashboards that visualize key performance indicators, making it easier for stakeholders to understand trends and make informed decisions.”

10. Can you explain the importance of data governance in data engineering?

This question evaluates your understanding of data governance principles and their relevance to the role.

How to Answer

Discuss the role of data governance in ensuring data quality, compliance, and security.

Example

“Data governance is crucial for maintaining data integrity and compliance with regulations. It involves establishing policies for data management, ensuring that data is accurate, accessible, and secure. In my previous role, I helped implement a data governance framework that improved data quality and reduced compliance risks.”