Harness Data Engineer Interview Questions + Guide in 2025

Harness is a rapidly growing company that is redefining the software delivery landscape.

As a Data Engineer at Harness, you will play a pivotal role in designing, developing, and maintaining scalable data pipelines and infrastructure that support the company's innovative software delivery platform. Your key responsibilities will include collaborating with cross-functional teams—such as data scientists and analysts—to understand data requirements, implementing solutions, and ensuring data quality and reliability. You will be tasked with optimizing data processes to enhance performance and efficiency, troubleshooting data-related issues, and staying current with the latest technologies in the field.

To excel in this role, you will need a solid background in data warehousing, ETL/ELT processes, and data modeling, along with proficiency in programming languages like SQL, Python, Java, or Scala. Additionally, experience with big data technologies and cloud platforms is essential. Strong problem-solving and analytical skills, as well as effective communication and collaboration abilities, will also be crucial for your success.

This guide aims to equip you with tailored insights and strategies to prepare effectively for your interview at Harness, giving you a competitive advantage in showcasing your skills and fit for the Data Engineer role.

The interview process for a Data Engineer at Harness is designed to thoroughly assess both technical skills and cultural fit. It typically consists of several structured rounds, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Screening

The process begins with an initial screening, usually conducted by a recruiter. This conversation lasts about 30-45 minutes and focuses on your background, experience, and motivation for applying to Harness. The recruiter will also provide insights into the company culture and the specifics of the role, ensuring that you have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a take-home assignment or a coding challenge that you will have 48 hours to complete. The assessment typically tests your proficiency in SQL, data structures, and algorithms, as well as your ability to design and optimize data pipelines.

3. Technical Interviews

Candidates who successfully pass the technical assessment will move on to a series of technical interviews. These interviews usually consist of two to three rounds, where you will be asked to solve coding problems in real-time, often using platforms like LeetCode. Expect questions that cover data structures, algorithms, and system design, with a focus on practical applications relevant to data engineering tasks. Interviewers may also delve into your experience with big data technologies, cloud platforms, and data modeling.

4. Managerial and Behavioral Round

The final round typically includes a managerial interview, where you will meet with a hiring manager or senior team member. This round assesses your fit within the team and the company culture. Expect to discuss your previous experiences, how you handle challenges, and your approach to collaboration. Behavioral questions will be aimed at understanding your problem-solving skills and how you align with Harness's values.

5. Final Presentation

In some cases, candidates may be asked to prepare a presentation on a relevant topic, such as a project you have worked on or a data engineering concept. This presentation allows you to showcase your communication skills and technical knowledge while engaging with potential future colleagues.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during these rounds.

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

Prepare for Technical Rigor

Harness places a strong emphasis on technical skills, particularly in data structures and algorithms. Expect to encounter a variety of coding challenges, including those related to SQL, data modeling, and pipeline optimization. Brush up on your knowledge of algorithms, especially those that are commonly used in data engineering, such as graph traversal and dynamic programming. Practice coding problems on platforms like LeetCode or HackerRank to build your confidence and speed.

Understand the Data Lifecycle

As a data engineer, you will be responsible for the entire data lifecycle, from ingestion to storage and processing. Familiarize yourself with the concepts of ETL/ELT processes, data quality checks, and data integrity. Be prepared to discuss how you have implemented these processes in your previous roles, and think of specific examples that demonstrate your ability to optimize data pipelines for performance and efficiency.

Showcase Collaboration Skills

Harness values collaboration across teams, so be ready to discuss your experience working with data scientists, analysts, and other stakeholders. Highlight instances where you successfully gathered requirements, implemented solutions, and ensured data quality. Your ability to communicate effectively and work as part of a team will be crucial, so prepare to share examples that illustrate your collaborative approach.

Be Candid and Authentic

During the interview, be open about your background and expertise. The interviewers appreciate candidates who are genuine and transparent about their experiences. This will not only help you build rapport with the interviewers but also allow them to assess your fit within the team and company culture more accurately.

Prepare for Behavioral Questions

Expect to face behavioral questions that assess your problem-solving abilities and cultural fit. Reflect on your past experiences and be ready to discuss challenges you’ve faced, how you overcame them, and what you learned from those situations. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Research the Company and Its Products

Understanding Harness's mission and the products it offers will give you an edge in the interview. Familiarize yourself with their software delivery platform and how it supports developers in delivering code efficiently. Be prepared to discuss how your skills and experiences align with the company’s goals and how you can contribute to their success.

Stay Updated on Industry Trends

The data engineering field is constantly evolving, so it’s essential to stay informed about the latest technologies and best practices. Be ready to discuss any new tools or methodologies you’ve learned about and how they could be applied to improve existing systems at Harness. This demonstrates your commitment to continuous learning and improvement.

Follow Up Thoughtfully

After the interview, consider sending a thank-you note to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your enthusiasm for the role and the company, and to mention any key points from the interview that you found particularly engaging. This can leave a positive impression and keep you top of mind as they make their decision.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Harness. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data systems. Be prepared to discuss your past projects, demonstrate your coding skills, and explain your approach to data engineering challenges.

Data Structures and Algorithms

1. Can you explain the concept of a stack and how it can be used to check the validity of parentheses in an expression?

Understanding data structures is crucial for a data engineer, and this question tests your knowledge of stacks.

How to Answer

Explain the stack's Last In First Out (LIFO) principle and how it can be applied to track opening and closing parentheses.

Example

“A stack can be used to check the validity of parentheses by pushing each opening parenthesis onto the stack and popping it when a closing parenthesis is encountered. If the stack is empty at the end of the expression, the parentheses are valid.”

2. Describe how you would implement a binary search algorithm.

This question assesses your understanding of algorithms and their efficiency.

How to Answer

Discuss the binary search process, including how it divides the search space in half with each iteration.

Example

“Binary search works on sorted arrays by comparing the target value to the middle element. If the target is less than the middle element, the search continues in the left half; otherwise, it continues in the right half, repeating this until the target is found or the search space is exhausted.”

3. What is Morris Order Traversal, and how would you implement it?

This question tests your knowledge of tree traversal techniques.

How to Answer

Explain the Morris traversal method, which uses threading to traverse a binary tree without using additional space.

Example

“Morris Order Traversal allows us to traverse a binary tree in-order without using a stack or recursion by temporarily modifying the tree structure. We create links to the predecessor nodes and restore the tree after traversal.”

4. How would you find the number of connected components in a graph?

This question evaluates your understanding of graph theory.

How to Answer

Discuss the use of Depth First Search (DFS) or Breadth First Search (BFS) to explore the graph and count components.

Example

“To find the number of connected components, I would perform a DFS on each unvisited node, marking all reachable nodes as visited. Each time I initiate a DFS, I increment the component count.”

5. Can you explain the difference between a linked list and an array?

This question assesses your understanding of data structures and their use cases.

How to Answer

Discuss the key differences in terms of memory allocation, access time, and use cases.

Example

“Arrays have a fixed size and allow for O(1) access time, while linked lists are dynamic and allow for efficient insertions and deletions but have O(n) access time due to their sequential nature.”

Data Engineering Concepts

1. Describe your experience with ETL processes. What tools have you used?

This question focuses on your practical experience with data pipelines.

How to Answer

Discuss specific ETL tools you have used and the processes you implemented.

Example

“I have extensive experience with ETL processes using tools like Apache NiFi and Talend. I designed data pipelines to extract data from various sources, transform it for analysis, and load it into a data warehouse.”

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

This question evaluates your approach to maintaining data standards.

How to Answer

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

Example

“I implement data quality checks at each stage of the pipeline, including schema validation, duplicate detection, and anomaly detection. I also use logging and monitoring to catch issues early.”

3. Can you explain the differences between SQL and NoSQL databases?

This question tests your understanding of database technologies.

How to Answer

Discuss the key differences in structure, scalability, and use cases.

Example

“SQL databases are relational and use structured query language for data manipulation, while NoSQL databases are non-relational and can handle unstructured data. SQL is ideal for complex queries, while NoSQL is better for scalability and flexibility.”

4. What strategies do you use to optimize data pipelines for performance?

This question assesses your ability to improve efficiency in data processing.

How to Answer

Discuss specific techniques you have used to enhance pipeline performance.

Example

“I optimize data pipelines by implementing partitioning, indexing, and caching strategies. I also monitor performance metrics to identify bottlenecks and adjust resource allocation accordingly.”

5. Describe a challenging data-related issue you faced and how you resolved it.

This question evaluates your problem-solving skills in real-world scenarios.

How to Answer

Provide a specific example of a challenge and the steps you took to resolve it.

Example

“I once faced a significant delay in data processing due to a poorly optimized query. I analyzed the query execution plan, identified missing indexes, and restructured the query, which reduced processing time by over 50%.”