Hewlett Packard Enterprise Data Engineer Interview Questions + Guide in 2025

Hewlett Packard Enterprise (HP) is a leading global provider of innovative technology solutions and services for a wide range of industries. With operations in over 170 countries, HPE is committed to improving life through technology.

Hewlett Packard Enterprise (HPE) is a global technology company that delivers innovative and secure solutions to help businesses accelerate their digital transformation.

The Data Engineer role at HPE is pivotal in designing, building, and maintaining data pipelines that facilitate the effective processing and storage of large datasets. Key responsibilities include collaborating with data scientists and analysts to understand data needs, implementing ETL (Extract, Transform, Load) processes, and ensuring the integrity and quality of data throughout its lifecycle. Candidates should possess strong programming skills in languages such as Python and SQL, along with a solid understanding of cloud technologies like AWS and data warehousing concepts. A successful Data Engineer at HPE is not only technically proficient but also has excellent problem-solving abilities, a strong attention to detail, and the capacity to communicate effectively across teams. This role aligns with HPE's commitment to innovation and customer-centric solutions by enabling data-driven decision-making and enhancing operational efficiency.

This guide aims to equip you with insights into the expectations and requirements of the Data Engineer role at HPE, as well as tips for showcasing your skills and experiences effectively during the interview process.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Hewlett Packard Enterprise. The interview process will likely assess your technical skills, problem-solving abilities, and understanding of data architecture and engineering principles. Be prepared to demonstrate your knowledge of programming languages, database management, and cloud technologies, as well as your ability to work collaboratively within a team.

Technical Skills

1. What are the key differences between SQL and NoSQL databases?

Understanding the differences between SQL and NoSQL databases is crucial for a Data Engineer, as it impacts data modeling and storage decisions.

How to Answer

Discuss the fundamental differences in structure, scalability, and use cases for each type of database. Highlight scenarios where one might be preferred over the other.

Example

“SQL databases are structured and use a predefined schema, making them ideal for complex queries and transactions. In contrast, NoSQL databases are more flexible, allowing for unstructured data and horizontal scaling, which is beneficial for handling large volumes of data in real-time applications.”

2. Can you explain the ETL process and its importance?

The ETL (Extract, Transform, Load) process is a core function in data engineering, and understanding it is essential for managing data pipelines.

How to Answer

Outline the steps involved in the ETL process and explain how it contributes to data integration and quality.

Example

“ETL involves extracting data from various sources, transforming it into a suitable format, and loading it into a target database. This process is vital for ensuring that data is accurate, consistent, and accessible for analysis, enabling organizations to make informed decisions.”

3. Describe a time you optimized a data pipeline. What challenges did you face?

This question assesses your practical experience in improving data processes and your problem-solving skills.

How to Answer

Share a specific example, focusing on the challenges you encountered and the strategies you employed to overcome them.

Example

“I worked on a data pipeline that was experiencing latency issues. I identified bottlenecks in the data transformation stage and implemented parallel processing, which reduced processing time by 40%. The challenge was ensuring data integrity during this optimization, which I addressed through rigorous testing.”

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

Data quality is critical in data engineering, and interviewers want to know your approach to maintaining it.

How to Answer

Discuss the methods and tools you use to validate and clean data, as well as any frameworks you follow.

Example

“I implement data validation checks at various stages of the ETL process, using tools like Apache Airflow for orchestration. Additionally, I conduct regular audits and use automated testing to ensure data accuracy and consistency throughout the pipeline.”

5. What is your experience with cloud technologies, specifically AWS or Azure?

Familiarity with cloud platforms is essential for modern data engineering roles.

How to Answer

Highlight your experience with specific services and how you have utilized them in your projects.

Example

“I have extensive experience with AWS, particularly with services like S3 for data storage and Redshift for data warehousing. I’ve used these tools to build scalable data solutions that support analytics and reporting for various business units.”

Programming and Algorithms

1. Write a Python function to find the nth maximum element in a list.

This question tests your programming skills and understanding of algorithms.

How to Answer

Explain your thought process before writing the code, and ensure your solution is efficient.

Example

“To find the nth maximum element, I would first sort the list in descending order and then return the element at the nth index. Here’s a simple implementation: def nth_max(lst, n): return sorted(set(lst), reverse=True)[n-1].”

2. How would you handle missing data in a dataset?

Handling missing data is a common challenge in data engineering.

How to Answer

Discuss various strategies for dealing with missing data, including imputation and removal.

Example

“I typically assess the extent of missing data and choose an appropriate strategy. For small amounts, I might impute values based on the mean or median. If a significant portion is missing, I may consider removing those records or using algorithms that can handle missing values effectively.”

3. Explain the concept of data normalization and its benefits.

Normalization is a key concept in database design, and understanding it is important for a Data Engineer.

How to Answer

Define normalization and discuss its advantages in terms of data integrity and efficiency.

Example

“Data normalization involves organizing a database to reduce redundancy and improve data integrity. By structuring data into related tables, we can ensure that updates are consistent and that the database performs efficiently during queries.”

4. What are routing tables, and how do they work?

This question tests your knowledge of networking, which is relevant for data engineers working with distributed systems.

How to Answer

Explain the purpose of routing tables and how they facilitate data transmission across networks.

Example

“Routing tables store information about the paths data packets take through a network. They help routers determine the best route for data transmission, ensuring efficient communication between devices.”

5. Can you discuss the trade-offs between bias and variance in machine learning models?

Understanding bias and variance is crucial for building effective predictive models.

How to Answer

Explain the concepts of bias and variance, and discuss how they impact model performance.

Example

“Bias refers to the error due to overly simplistic assumptions in the learning algorithm, while variance refers to the error due to excessive complexity. A good model strikes a balance between the two, minimizing both bias and variance to achieve optimal performance.”

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

Understand the Technical Landscape

As a Data Engineer at Hewlett Packard Enterprise, you will be expected to have a solid grasp of various technologies, including SQL, Python, and cloud platforms like AWS and Databricks. Make sure to familiarize yourself with the specific tools and technologies mentioned in the job description. Brush up on your knowledge of data architecture, data modeling, and ETL processes. Being able to discuss your experience with these technologies in detail will demonstrate your readiness for the role.

Prepare for Technical Questions

Expect a mix of theoretical and practical questions during the technical round. You may be asked to solve problems related to data structures, algorithms, and database management. Practice coding challenges in Python and SQL, and be prepared to write code from the command line rather than in a notebook environment. Familiarize yourself with common data engineering tasks, such as writing queries to extract specific data or optimizing data pipelines.

Showcase Your Problem-Solving Skills

During the interview, you may encounter scenario-based questions that assess your problem-solving abilities. Be prepared to discuss how you would approach complex data challenges, such as integrating disparate data sources or optimizing data workflows. Use the STAR (Situation, Task, Action, Result) method to structure your responses, highlighting your analytical thinking and decision-making process.

Communicate Effectively

Effective communication is crucial in this role, as you will need to collaborate with various stakeholders, including data scientists and marketing teams. Practice articulating your thoughts clearly and concisely. When discussing your past experiences, focus on how you communicated technical concepts to non-technical team members and how you ensured alignment with project goals.

Emphasize Collaboration and Teamwork

Hewlett Packard Enterprise values collaboration across teams. Be prepared to discuss your experiences working in cross-functional teams and how you contributed to achieving shared objectives. Highlight any instances where you took the initiative to foster collaboration or resolve conflicts within a team setting.

Be Ready for Behavioral Questions

Expect behavioral questions that explore your strengths, weaknesses, and how you handle challenges. Reflect on your past experiences and be honest about areas for improvement. Use specific examples to illustrate your points, and focus on how you have learned and grown from those experiences.

Research the Company Culture

Understanding Hewlett Packard Enterprise's culture will give you an edge in the interview. Familiarize yourself with their values, mission, and recent initiatives. Be prepared to discuss how your personal values align with the company's culture and how you can contribute to their goals.

Follow Up After the Interview

Given the feedback regarding communication post-interview, consider sending a follow-up email thanking your interviewers for their time and reiterating your interest in the role. This not only shows professionalism but also keeps you on their radar.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at Hewlett Packard Enterprise. Good luck!

The interview process for a Data Engineer role at Hewlett Packard Enterprise is structured to assess both technical skills and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different competencies relevant to the role.

1. Initial Screening

The process begins with an initial screening, which is usually a phone interview with a recruiter. This conversation focuses on your background, experiences, and motivations for applying to Hewlett Packard Enterprise. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you understand the expectations and responsibilities.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may involve a written test or a coding challenge that evaluates your proficiency in programming languages such as Python and SQL, as well as your understanding of data structures, algorithms, and database management. Expect questions that test your knowledge of operating systems, networking, and data engineering principles. You may also be asked to solve practical problems, such as writing SQL queries or performing data manipulations.

3. Technical Interview

Candidates who pass the technical assessment will move on to one or more technical interviews. These interviews are conducted by experienced data engineers or technical leads and focus on your ability to design and implement data solutions. You may be asked to discuss your previous projects, explain your approach to data modeling, and demonstrate your problem-solving skills through real-world scenarios. Be prepared to answer questions related to cloud technologies, data pipelines, and big data frameworks.

4. Managerial Round

The next step in the process is often a managerial round, where you will meet with a hiring manager or team lead. This interview is less technical and more focused on your soft skills, such as communication, teamwork, and conflict resolution. The interviewer will assess how well you align with the company's values and how you handle various workplace situations. Expect questions about your experiences working in teams, managing deadlines, and adapting to changing project requirements.

5. Final Interview

In some cases, there may be a final interview round, which could involve higher-level executives or cross-functional team members. This round is an opportunity for you to demonstrate your strategic thinking and how you can contribute to the organization’s goals. You may be asked to discuss your vision for data engineering within the company and how you would approach collaboration with other departments.

As you prepare for your interview, consider the types of questions that may arise in each of these rounds, focusing on both technical and behavioral aspects.

1. How much should we budget for the coupon initiative in total?

A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider. The app services (N) riders. Calculate the total budget needed for the coupon initiative.

2. What is the probability of both riders getting the coupon?

A driver using the app picks up two passengers. Determine the probability that both riders will receive the coupon.

3. What is the probability that only one of them will get the coupon?

A driver using the app picks up two passengers. Determine the probability that only one of the riders will receive the coupon.

4. What is a confidence interval for a statistic?

Explain what a confidence interval is, why it is useful, and how to calculate it.

5. What is the probability that item X would be found on Amazon’s website?

Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that item X is available at warehouse A (0.6) and warehouse B (0.8), calculate the probability that item X would be found on Amazon’s website.

6. Is this a fair coin?

You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair.

7. What are time series models, and why do we need them?

Describe what time series models are and explain why they are necessary when simpler regression models exist.

8. 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.

9. Write a function to find the maximum number in a list of integers.

Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

10. Create a function convert_to_bst to convert a sorted list into a balanced binary tree.

Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should be balanced, meaning the height difference between the left and right subtree of all the nodes should be at most one.

11. Write a function to simulate drawing balls from a jar.

Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

12. Develop a function can_shift to determine if one string can be shifted to become another.

Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

13. How would you explain linear regression to a child, a first-year college student, and a seasoned mathematician?

Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician, tailoring each explanation to their understanding level.

14. What happens when you run logistic regression on perfectly linearly separable data?

Given a dataset of perfectly linearly separable data, describe the outcome when logistic regression is applied.

15. How would you evaluate and deploy a decision tree model for predicting loan repayment?

As a data scientist at a bank, you need to build a decision tree model to predict loan repayment. Explain how you would evaluate if a decision tree is the right model and how you would assess its performance before and after deployment.

16. How would you justify using a neural network model to non-technical stakeholders?

If tasked with building a neural network model to solve a business problem, explain how you would justify the model’s complexity and explain its predictions to non-technical stakeholders.

17. How does random forest generate the forest, and why use it over logistic regression?

Describe the process by which random forest generates its forest and explain why it might be preferred over logistic regression for certain problems.

18. What are the drawbacks of the given data organization, and how would you reformat it for better analysis?

Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in “messy” datasets.

19. How would you locate a mouse in a 4x4 grid using the fewest scans?

You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. Describe a strategy to find the mouse using the fewest number of scans.

20. How would you select Dashers for Doordash deliveries in NYC and Charlotte?

Doordash is launching delivery services in New York City and Charlotte. Describe the process for selecting Dashers (delivery drivers) and discuss whether the criteria for selection should be the same for both cities.

21. What factors could bias Jetco’s study on boarding times, and what would you investigate?

Jetco, a new airline, claims to have the fastest average boarding times based on a study. Identify potential biases in this result and describe what factors you would investigate to validate the study.

22. How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company?

A B2B SAAS company wants to test different subscription pricing levels. Design a two-week A/B test to evaluate a pricing increase and determine whether it is a good business decision.

Here are some quick tips to prepare for data engineer at HP:

• Prepare for Extemporaneous Speaking: Since HP includes a group discussion round, practice your extemporaneous speaking skills. Be ready to articulate your thoughts clearly and confidently on various topics.
• Brush Up on Technical Fundamentals: Review fundamental Java, C++, and OOP concepts. Be prepared to write code on the spot, particularly for common algorithm problems like the Fibonacci series.
• Understand the Company Culture: HP values quick, agile, and transparent communication. Demonstrate you align with these values by being straightforward and clear in your interactions.
• Do Mock Interviews: A great way to improve and prepare for interviews is through P2P mock interviews or AI interviewer. These practice interviews simulate real-life scenarios, helping you get comfortable with the interview format, improve your communication, and receive feedback on your technical and behavioral responses.

What is the average salary for a Data Engineer at Hewlett Packard Enterprise?

What is the company culture like at Hewlett Packard Enterprise?

Hewlett Packard Enterprise fosters a unique culture of diversity, equity, and inclusion. Employees are encouraged to bring their authentic selves to work, which boosts innovation and company growth. The culture is highly collaborative, with a strong emphasis on respect and individuality.

Are there any openings at HP?

As of writing, there are LOTS of openings at HP! Check out our Job Board to learn more.

As an aspiring Data Engineer at HP, it’s exciting to know that you’re about to join a team that values innovation, technical prowess, and a spirit of collaboration. HP’s thorough and dynamic interview process, which includes a group discussion, technical rounds, and detailed managerial interviews, ensures that only the most diligent and skilled candidates will thrive here.

Remember, at HP, you’re not just taking a job; you’re building a future full of possibilities! Good luck with your interview!