Exl Data Engineer Interview Questions + Guide in 2025

EXL is a leading global analytics and digital solutions company that leverages data and AI to drive better business outcomes for its clients across various industries.

The Data Engineer role at EXL is pivotal in shaping the data landscape of the organization. This position is responsible for designing, developing, and optimizing ETL pipelines and ensuring the integrity and efficiency of data processing systems. Data Engineers at EXL will work extensively with technologies such as PySpark, SQL, and various cloud services including AWS and GCP, to enable data-driven decision-making. Key responsibilities include conducting end-to-end data migrations, collaborating with cross-functional teams to gather requirements, automating data workflows, and maintaining data quality standards. Strong problem-solving skills, proficiency in data modeling and data governance, and a passion for data engineering will make you an excellent fit for this role at EXL, aligning with the company’s commitment to innovation, collaboration, and integrity.

This guide will help you prepare strategically for your interview by providing insights into the expectations and requirements for the Data Engineer role at EXL.

The interview process for a Data Engineer position at EXL is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes several rounds of interviews, focusing on various aspects of data engineering, problem-solving abilities, and past experiences.

1. Initial Screening

The first step typically involves a phone interview with a recruiter. This conversation is designed to gauge your interest in the role, discuss your background, and evaluate your fit for EXL’s culture. The recruiter may ask about your experience with data engineering concepts, tools, and technologies relevant to the position.

2. Technical Assessment

Following the initial screening, candidates usually undergo one or more technical interviews. These interviews may be conducted via video call and focus heavily on practical skills. Expect to answer questions related to SQL, Python, PySpark, and other relevant technologies. You may also be asked to solve coding problems or complete live coding exercises that demonstrate your ability to work with data pipelines and ETL processes.

3. Project Discussion

In this round, candidates are often required to discuss their previous projects in detail. Interviewers will want to understand your role in these projects, the technologies you used, and the challenges you faced. Be prepared to explain your thought process and the impact of your work on the overall project outcomes.

4. Managerial Interview

The next step typically involves a managerial interview, where you will meet with a hiring manager or team lead. This round focuses on your soft skills, teamwork, and how you handle various work situations. Expect questions about your experience working in cross-functional teams, your approach to problem-solving, and how you prioritize tasks in a fast-paced environment.

5. Final Interview

The final stage may include additional technical assessments or a combination of technical and behavioral questions. This round is often more in-depth and may involve discussions about data governance, data modeling, and best practices in data engineering. Candidates may also be asked to demonstrate their understanding of cloud platforms and data services.

Throughout the process, candidates should be prepared for a variety of question types, including scenario-based questions that assess logical thinking and analytical skills.

Now that you have an overview of the interview process, let’s delve into the specific questions that candidates have encountered during their interviews at EXL.

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

Understand the Technical Landscape

Given the emphasis on SQL, PySpark, and cloud technologies like AWS and GCP, ensure you have a solid grasp of these tools. Review your past projects and be prepared to discuss how you utilized these technologies to solve real-world problems. Familiarize yourself with the latest trends in data engineering, as EXL values innovation and staying ahead of the curve.

Prepare for Hands-On Coding Challenges

Expect live coding sessions that may include SQL queries, PySpark scripts, and possibly even Python coding. Practice writing code on a whiteboard or in a shared document, as this format is common in technical interviews. Focus on optimizing your code for performance and scalability, as these are key aspects of the role.

Showcase Your Problem-Solving Skills

During the interview, you may encounter scenario-based questions that assess your logical thinking and problem-solving abilities. Be ready to walk through your thought process step-by-step. Use the STAR (Situation, Task, Action, Result) method to structure your responses, particularly when discussing past projects or challenges you’ve faced.

Emphasize Collaboration and Communication

EXL values teamwork and collaboration, so be prepared to discuss how you’ve worked with cross-functional teams in the past. Highlight your ability to communicate complex technical concepts to non-technical stakeholders. This will demonstrate your fit within the company culture, which prioritizes collaboration and respect.

Be Ready for Behavioral Questions

In addition to technical questions, expect behavioral questions that explore your past experiences and how you handle various situations. Reflect on your previous roles and prepare examples that showcase your adaptability, leadership, and commitment to quality. This will help you align with EXL’s core values of innovation, collaboration, and excellence.

Stay Informed About Company Culture

Understanding EXL’s mission and values will give you an edge. Familiarize yourself with their focus on data-driven solutions and their commitment to client success. This knowledge will allow you to tailor your responses to align with the company’s goals and demonstrate your enthusiasm for contributing to their mission.

Follow Up Professionally

After the interview, send a thank-you email to express your appreciation for the opportunity. Reiterate your interest in the role and briefly mention a key point from the interview that resonated with you. This not only shows professionalism but also reinforces your enthusiasm for the position.

By following these tips, you’ll be well-prepared to showcase your skills and fit for the Data Engineer role at EXL. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at EXL. The interview process will likely focus on your technical skills in data engineering, including your proficiency in SQL, Python, PySpark, and cloud technologies. Be prepared to discuss your past projects and how you have applied your skills in real-world scenarios.

Technical Skills

1. Can you explain the ETL process and how you have implemented it in your previous projects?

Understanding the ETL process is crucial for a Data Engineer, as it forms the backbone of data integration and processing.

How to Answer

Discuss your experience with ETL tools and frameworks, emphasizing specific projects where you designed or optimized ETL pipelines. Highlight any challenges you faced and how you overcame them.

Example

“In my previous role, I designed an ETL pipeline using Apache NiFi to extract data from various sources, transform it using PySpark, and load it into a Snowflake data warehouse. I faced challenges with data quality, which I addressed by implementing validation checks at each stage of the pipeline, ensuring that only clean data was loaded.”

2. How do you optimize SQL queries for performance?

Optimizing SQL queries is essential for ensuring efficient data retrieval and processing.

How to Answer

Explain the techniques you use to optimize queries, such as indexing, query restructuring, and analyzing execution plans. Provide examples of how these techniques improved performance in your past work.

Example

“I often start by analyzing the execution plan of a query to identify bottlenecks. For instance, I once optimized a slow-running report by adding appropriate indexes and rewriting the query to reduce the number of joins, which improved the execution time by over 50%.”

3. Describe your experience with cloud platforms, specifically AWS or GCP.

Cloud platforms are integral to modern data engineering, and familiarity with them is often a requirement.

How to Answer

Discuss your hands-on experience with specific services (like AWS Glue, S3, or GCP BigQuery) and how you have utilized them in your projects. Mention any relevant certifications if applicable.

Example

“I have extensive experience with AWS, particularly with services like S3 for data storage and Glue for ETL processes. In a recent project, I used AWS Glue to automate the ETL process, which significantly reduced the time required for data ingestion and transformation.”

4. What is your approach to data quality and validation in data pipelines?

Ensuring data quality is critical for reliable analytics and reporting.

How to Answer

Describe the methods you use to validate data at various stages of the pipeline, including automated checks and manual reviews. Provide examples of how you have implemented these practices.

Example

“I implement data validation checks at each stage of the ETL process. For example, I use PySpark to create data quality checks that flag any anomalies in the data before it is loaded into the warehouse. This proactive approach has helped maintain high data integrity in my projects.”

5. Can you explain the differences between batch processing and stream processing?

Understanding the differences between these two processing methods is essential for a Data Engineer.

How to Answer

Define both terms and discuss scenarios where each method is appropriate. Highlight any experience you have with both types of processing.

Example

“Batch processing involves processing large volumes of data at once, which is suitable for tasks like monthly reporting. In contrast, stream processing handles data in real-time, making it ideal for applications like fraud detection. I have implemented both methods, using Apache Spark for batch processing and Apache Kafka for real-time data streams.”

Programming and Tools

6. What is your experience with PySpark, and how have you used it in your projects?

PySpark is a key tool for data processing in big data environments.

How to Answer

Discuss specific projects where you utilized PySpark, focusing on the challenges you faced and how you leveraged its capabilities.

Example

“I used PySpark extensively to process large datasets for a marketing analytics project. I implemented transformations and aggregations that reduced processing time from hours to minutes, allowing for near real-time insights.”

7. How do you handle version control in your data engineering projects?

Version control is crucial for collaboration and maintaining code quality.

How to Answer

Explain your experience with version control systems, particularly Git, and how you manage code changes and collaboration.

Example

“I use Git for version control in all my projects. I follow best practices by creating feature branches for new developments and regularly merging them into the main branch after thorough code reviews. This approach has helped maintain a clean and organized codebase.”

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

Problem-solving skills are essential for a Data Engineer.

How to Answer

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

Example

“I once faced a challenge with a data pipeline that was failing due to schema changes in the source data. I quickly implemented a schema evolution strategy using Apache Avro, which allowed the pipeline to adapt to changes without manual intervention, ensuring continuous data flow.”

9. What tools do you use for monitoring and optimizing data pipelines?

Monitoring is key to maintaining the health of data pipelines.

How to Answer

Discuss the tools and techniques you use for monitoring performance and troubleshooting issues in data pipelines.

Example

“I use tools like AWS CloudWatch and Apache Airflow to monitor data pipelines. I set up alerts for any failures or performance degradation, allowing me to address issues proactively before they impact downstream processes.”

10. How do you ensure compliance with data governance and security policies?

Data governance and security are critical in data engineering roles.

How to Answer

Explain your understanding of data governance principles and how you implement security measures in your projects.

Example

“I ensure compliance with data governance policies by implementing role-based access controls and encryption for sensitive data. I also regularly review data access logs to ensure that only authorized personnel have access to critical information.”