Unisys Data Engineer Interview Questions + Guide in 2025

Unisys is a global technology company that specializes in providing IT services and solutions to various industries, focusing on innovation and customer success.

The Data Engineer role at Unisys is vital for designing, implementing, and maintaining data pipelines and architectures that support the company's data-driven initiatives. Key responsibilities include developing ETL processes, ensuring data integrity, and collaborating with cross-functional teams to build data solutions. Candidates should have a solid understanding of SQL, Python, or Scala, in addition to experience with big data technologies like Apache Spark. A problem-solving mindset and a test-driven approach are essential traits, as the role may also encompass some aspects of software testing and quality assurance. Familiarity with cloud environments, particularly AWS, and knowledge of financial markets can be advantageous. This role aligns with Unisys's commitment to fostering innovation and collaboration, making it a great fit for self-motivated individuals eager to contribute to cutting-edge projects.

This guide will help candidates prepare for their interviews by providing insights into the expectations and requirements for the Data Engineer role at Unisys, ultimately enhancing their chances of success in the hiring process.

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

Understand the Role and Its Requirements

Before your interview, take the time to thoroughly understand the responsibilities and expectations of a Data Engineer at Unisys. Familiarize yourself with the technologies mentioned in the job description, such as SQL, Python, Spark, and cloud services like AWS. Be prepared to discuss how your experience aligns with these requirements and how you can contribute to the team’s goals.

Prepare for Technical Questions

Expect a mix of technical and behavioral questions during your interviews. Brush up on your knowledge of data engineering concepts, including ETL processes, data modeling, and big data technologies. Be ready to solve coding problems on the spot, as technical assessments are a common part of the interview process. Practice writing algorithms and working through data structure problems, as these are often focal points in technical interviews.

Showcase Problem-Solving Skills

Unisys values candidates who demonstrate a problem-solving mindset. Be prepared to discuss specific examples from your past experiences where you faced challenges and how you approached them. Highlight your ability to think outside the box and your willingness to try different solutions. This will show your potential to contribute innovative ideas to the team.

Emphasize Collaboration and Communication

Given the collaborative nature of the role, be ready to discuss how you work with others, especially in a team setting. Share examples of how you have successfully collaborated with cross-functional teams or mentored junior staff. Additionally, be prepared to articulate your communication style and how you ensure that all stakeholders are informed and engaged throughout a project.

Research Company Culture

Unisys has a reputation for being a supportive and accommodating workplace. Familiarize yourself with their values and culture, and think about how you can align your personal values with those of the company. During the interview, express your enthusiasm for being part of a team that encourages growth and innovation. This will help you stand out as a candidate who is not only technically qualified but also a good cultural fit.

Prepare Thoughtful Questions

At the end of your interview, you will likely have the opportunity to ask questions. Use this time to demonstrate your interest in the role and the company. Ask about the team dynamics, ongoing projects, or how success is measured in the role. This not only shows your engagement but also helps you assess if Unisys is the right fit for you.

Follow Up Professionally

After your 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 your conversation that reinforces your fit for the role. This small gesture can leave a positive impression and keep you top of mind as they make their decision.

By following these tips, you can approach your interview with confidence and clarity, positioning yourself as a strong candidate for the Data Engineer role at Unisys. Good luck!

The interview process for a Data Engineer position at Unisys is structured to assess both technical skills and cultural fit within the organization. It typically unfolds over several rounds, each designed to evaluate different competencies and experiences relevant to the role.

1. Initial Screening

The process usually begins with an initial screening, which may take place over the phone or via video call. During this stage, a recruiter will discuss your resume, gauge your interest in the position, and assess your basic qualifications. Expect questions about your background, relevant experiences, and your understanding of the Data Engineer role. This is also an opportunity for you to ask about the company culture and the specifics of the job.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may involve an online coding test or a technical interview where you will be asked to solve problems related to data structures, algorithms, and programming languages such as SQL or Python. You might also be required to demonstrate your knowledge of big data technologies like Apache Spark and your experience with ETL processes. This round is crucial as it evaluates your technical proficiency and problem-solving abilities.

3. Panel Interview

The next step often involves a panel interview, which includes multiple interviewers from different teams. This round is designed to assess both technical and behavioral competencies. You may be asked to discuss your previous projects, how you approached challenges, and your contributions to team efforts. Expect questions that explore your understanding of data architecture, cloud technologies, and your ability to work collaboratively in a team environment.

4. Final Interview

The final interview typically involves meeting with a senior manager or team lead. This round focuses on your long-term career goals, your fit within the company culture, and your potential contributions to Unisys. Behavioral questions will likely be prominent, as the interviewers will want to understand how you handle challenges, work under pressure, and interact with team members. This is also a chance for you to express your enthusiasm for the role and the company.

5. Offer and Negotiation

If you successfully navigate the previous rounds, you may receive a job offer. This stage will involve discussions about salary, benefits, and other employment terms. Be prepared to negotiate based on your research and understanding of industry standards.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Unisys. The interview process will likely assess your technical skills, problem-solving abilities, and your fit within the company culture. Be prepared to discuss your experience with data engineering tools, methodologies, and your approach to handling complex data challenges.

Technical Skills

1. Can you explain the process of creating an ETL pipeline?

Understanding ETL (Extract, Transform, Load) processes is crucial for a Data Engineer.

How to Answer

Discuss the steps involved in building an ETL pipeline, including data extraction from various sources, transformation processes, and loading data into a target system. Highlight any tools or technologies you have used.

Example

“I typically start by identifying the data sources and the required transformations. I use tools like Apache NiFi for extraction, apply transformations using Python or SQL, and finally load the data into a data warehouse like Amazon Redshift. I ensure to monitor the pipeline for any errors and optimize it for performance.”

2. What is your experience with Apache Spark?

Spark is a key technology for big data processing, and familiarity with it is often expected.

How to Answer

Share your experience with Spark, including any specific projects where you utilized it. Mention the types of data processing tasks you performed.

Example

“I have used Apache Spark extensively for processing large datasets. In my last project, I implemented Spark Streaming to process real-time data from IoT devices, which allowed us to analyze data on-the-fly and make immediate business decisions.”

3. How do you optimize SQL queries for performance?

Performance optimization is critical in data engineering roles.

How to Answer

Discuss techniques you use to optimize SQL queries, such as indexing, query restructuring, and analyzing execution plans.

Example

“I focus on indexing frequently queried columns and rewriting complex joins into simpler subqueries. I also analyze execution plans to identify bottlenecks and ensure that the database statistics are up-to-date for optimal performance.”

4. Describe a challenging data problem you faced and how you solved it.

This question assesses your problem-solving skills and technical expertise.

How to Answer

Provide a specific example of a data challenge, the steps you took to address it, and the outcome.

Example

“In a previous role, I encountered a significant data quality issue where duplicate records were affecting our analytics. I implemented a deduplication process using Python scripts and SQL queries, which improved our data accuracy and reliability for reporting.”

5. What tools do you use for data modeling?

Data modeling is a fundamental aspect of data engineering.

How to Answer

Mention the tools you are familiar with and how you have used them in your projects.

Example

“I primarily use tools like ER/Studio and Lucidchart for data modeling. I find them helpful for visualizing data relationships and ensuring that our database design aligns with business requirements.”

Problem-Solving and Analytical Skills

1. How do you approach debugging a data pipeline?

Debugging is an essential skill for a Data Engineer.

How to Answer

Explain your systematic approach to identifying and resolving issues in data pipelines.

Example

“I start by checking the logs for any error messages and then trace the data flow to identify where the issue occurred. I also validate the data at each stage to ensure it meets the expected format and quality before moving to the next step.”

2. Can you describe a time when you had to learn a new technology quickly?

Adaptability is important in the fast-evolving tech landscape.

How to Answer

Share an experience where you had to quickly learn a new tool or technology and how you applied it.

Example

“When our team decided to migrate to AWS, I took the initiative to learn about AWS services like S3 and Lambda. I completed several online courses and applied my knowledge by leading the migration project, which improved our data storage and processing capabilities.”

3. How do you ensure data quality in your projects?

Data quality is critical for effective data engineering.

How to Answer

Discuss the methods you use to maintain data quality throughout the data lifecycle.

Example

“I implement validation checks at various stages of the data pipeline, such as schema validation and data profiling. Additionally, I conduct regular audits and use automated testing frameworks to ensure data integrity.”

4. What strategies do you use for data governance?

Data governance is essential for compliance and data management.

How to Answer

Explain your understanding of data governance and the strategies you have implemented.

Example

“I advocate for clear data ownership and establish data stewardship roles within the team. I also implement data classification policies and ensure compliance with regulations like GDPR by maintaining proper documentation and access controls.”

5. How do you handle customer dissatisfaction regarding data issues?

Customer-facing roles require strong communication and problem-solving skills.

How to Answer

Describe your approach to addressing customer concerns and ensuring their satisfaction.

Example

“I prioritize open communication with customers to understand their concerns. I investigate the issue thoroughly and provide timely updates on the resolution process. After resolving the issue, I follow up to ensure they are satisfied with the outcome.”