Excella Data Engineer Interview Questions + Guide in 2025

Excella is a leading provider of Agile software development and data analytics solutions, serving clients across federal, commercial, and non-profit sectors.

The Data Engineer role at Excella entails designing and building modern data products, including data lakes and cleansed data repositories. This position requires you to collaborate closely with cross-functional teams and stakeholders to architect, build, and deploy various data acquisition initiatives. You will be engaged in all stages of data pipeline development, from brainstorming and coding to bug fixing. Proficiency in programming languages such as SQL and Python is essential, as well as familiarity with modern data orchestration tools like AWS Glue and Apache NiFi.

Key responsibilities include designing, developing, deploying, and maintaining data services or pipelines, with a strong emphasis on continuous process automation for data ingestion and workflow management. A successful candidate will demonstrate solid problem-solving skills, an ability to adapt under changing requirements, and the capability to present proof of concepts and solutions to stakeholders, including executives.

Excella values innovation and empathy, expecting Data Engineers to advocate for the adoption of industry tools and practices, listen to stakeholders' needs, and work collaboratively to find optimal solutions. This role aligns with Excella's commitment to creating a positive impact through technology while fostering a culture of excellence and continuous improvement.

This guide is designed to equip you with the insights and tools needed to excel in your interview for the Data Engineer role at Excella, helping you to showcase your technical expertise and alignment with the company's values.

The interview process for a Data Engineer at Excella is structured to assess both technical skills and cultural fit within the organization. It typically consists of multiple rounds, each designed to evaluate different aspects of your qualifications and experience.

1. Initial Screening

The process begins with a 30-minute phone interview with a recruiter. This initial screening focuses on your resume, work experience, and general fit for the company culture. The recruiter will also provide insights into the role and the expectations at Excella, allowing you to gauge if this is the right opportunity for you.

2. Technical Assessment

Following the initial screening, candidates usually undergo a technical assessment. This may involve a practical interview where you are asked to solve problems related to data engineering, such as designing data pipelines or discussing your experience with SQL and Python. Expect to demonstrate your understanding of data architectures, including data lakes and warehouses, as well as your familiarity with modern data orchestration tools like DBT, AWS Glue, or Apache NiFi.

3. Behavioral Interview

The next round often includes a behavioral interview with senior team members. This session focuses on your past experiences and how you handle various scenarios, particularly in collaborative environments. You may be asked to provide examples of how you've worked with stakeholders, managed project timelines, or navigated challenges in previous roles. This round is crucial for assessing your soft skills and alignment with Excella's values.

4. Final Interview

The final stage typically involves a conversation with higher-level executives or team leads. This interview may cover strategic thinking and your ability to contribute to the company's long-term goals. You might be asked to present a proof of concept or discuss your approach to data governance and compliance. This round is designed to evaluate your leadership potential and how well you can communicate complex technical concepts to non-technical stakeholders.

Throughout the process, candidates are encouraged to ask questions and engage with their interviewers to better understand the company culture and expectations.

Next, let's explore the specific interview questions that candidates have encountered during this process.

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

Prepare for a Multi-Round Process

Expect a thorough interview process that may include multiple rounds, starting with an HR screening followed by technical interviews with senior team members. Familiarize yourself with the structure of the interview, as it often involves both technical and behavioral assessments. Be ready to discuss your past experiences in detail, as interviewers will likely ask for specific examples of your work and how it relates to the role.

Master the Technical Skills

Given the emphasis on SQL and algorithms, ensure you have a solid grasp of these areas. Brush up on your SQL skills, focusing on complex queries, data manipulation, and performance optimization. Additionally, be prepared to discuss algorithms and their applications in data engineering. Practice coding problems that require you to think critically and solve problems efficiently, as this will be a key focus during technical interviews.

Emphasize Your Problem-Solving Abilities

Interviewers at Excella are looking for candidates who can abstract complex business problems into actionable technical solutions. Be prepared to discuss how you approach problem-solving, including any frameworks or methodologies you use. Highlight your experience in designing and implementing data architectures, and be ready to provide examples of how you've successfully navigated challenges in past projects.

Showcase Your Communication Skills

Excella values strong communication skills, especially when it comes to engaging with stakeholders. Be prepared to articulate your thought process clearly and concisely, both in technical discussions and when presenting your ideas. Practice explaining complex concepts in a way that is accessible to non-technical audiences, as this will demonstrate your ability to bridge the gap between technical and business teams.

Be Ready for Behavioral Questions

Expect scenario-based questions that assess your interpersonal skills and ability to work collaboratively. Prepare to discuss how you handle conflicts, work under pressure, and adapt to changing requirements. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and relevant examples from your experience.

Understand the Company Culture

Excella places a strong emphasis on innovation, collaboration, and a positive impact on clients and the community. Familiarize yourself with their values and mission, and be prepared to discuss how your personal values align with those of the company. Show enthusiasm for their commitment to diversity and inclusion, and be ready to share how you can contribute to fostering a positive work environment.

Practice Mental Math and Speed

Some candidates have noted that mental math can be a challenging aspect of the interview process. To prepare, practice mental math exercises to improve your speed and accuracy. This will help you feel more confident when faced with questions that require quick calculations or data analysis.

Follow Up Thoughtfully

After your interview, consider sending a thank-you note to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your interest in the role and briefly mention any key points from the interview that you found particularly engaging. This not only shows professionalism but also reinforces your enthusiasm for the position.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Excella. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data architecture and pipelines. Be prepared to discuss your past projects, technical methodologies, and how you approach collaboration with stakeholders.

Technical Skills

1. Can you explain the difference between a data lake and a data warehouse?

Understanding the distinctions between these two data storage solutions is crucial for a Data Engineer.

How to Answer

Discuss the characteristics of each, including their purposes, data types they handle, and how they are structured.

Example

“A data lake is designed to store vast amounts of raw data in its native format, allowing for flexibility and scalability. In contrast, a data warehouse is optimized for structured data and is used for reporting and analysis, often requiring data to be cleaned and transformed before storage.”

2. Describe your experience with building data pipelines. What tools have you used?

This question assesses your hands-on experience with data pipeline development.

How to Answer

Mention specific tools and technologies you have used, and describe a project where you built a data pipeline from start to finish.

Example

“I have built data pipelines using AWS Glue and Apache Airflow. In my last project, I designed a pipeline that ingested data from various sources, transformed it using ETL processes, and loaded it into a data warehouse for analytics.”

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

Data quality is paramount in data engineering, and interviewers want to know your strategies.

How to Answer

Discuss methods you use for validation, error handling, and monitoring data quality throughout the pipeline.

Example

“I implement data validation checks at each stage of the pipeline, using automated tests to catch errors early. Additionally, I set up monitoring tools to alert me of any anomalies in data flow or quality metrics.”

4. What is your experience with SQL and how do you use it in your data engineering tasks?

SQL is a fundamental skill for data engineers, and this question gauges your proficiency.

How to Answer

Provide examples of how you have used SQL for data manipulation, querying, and integration in your projects.

Example

“I frequently use SQL to extract and manipulate data from relational databases. For instance, I wrote complex queries to join multiple tables and aggregate data for reporting purposes, ensuring that the data was ready for analysis.”

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

Understanding ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) is essential for data pipeline design.

How to Answer

Clarify the processes involved in each and when you would use one over the other.

Example

“ETL involves extracting data, transforming it into a suitable format, and then loading it into a data warehouse. ELT, on the other hand, loads raw data into the warehouse first and then transforms it as needed. I prefer ELT for large datasets where transformation can be done post-load to leverage the power of the data warehouse.”

Problem-Solving and Design

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

This question assesses your problem-solving skills and ability to handle complex situations.

How to Answer

Share a specific example, detailing the problem, your approach, and the outcome.

Example

“I encountered a performance issue with a data pipeline that was causing delays in data availability. I analyzed the bottlenecks and optimized the data transformation processes by parallelizing tasks and using more efficient algorithms, which reduced the processing time by 50%.”

7. How do you approach designing a data architecture for a new project?

This question evaluates your strategic thinking and design skills.

How to Answer

Discuss your methodology for gathering requirements, evaluating technologies, and creating a scalable architecture.

Example

“I start by collaborating with stakeholders to understand their data needs and business goals. Then, I evaluate various technologies and design a flexible architecture that can accommodate future growth, ensuring it aligns with best practices in data governance and security.”

8. What strategies do you use for data ingestion from various sources?

This question focuses on your experience with data acquisition techniques.

How to Answer

Mention the tools and methods you use for data ingestion, including batch and streaming approaches.

Example

“I utilize tools like Apache NiFi for real-time data ingestion and AWS Glue for batch processing. I also implement APIs for integrating data from third-party services, ensuring that the ingestion process is efficient and reliable.”

9. How do you handle schema evolution in your data models?

Schema evolution is a common challenge in data engineering, and interviewers want to know your approach.

How to Answer

Discuss your strategies for managing changes in data structure while maintaining data integrity.

Example

“I adopt a versioning strategy for my schemas, allowing for backward compatibility. I also use automated migration scripts to update existing data to align with the new schema, ensuring a smooth transition without data loss.”

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

Data governance is critical for compliance and data quality, and this question assesses your understanding of its significance.

How to Answer

Discuss the principles of data governance and how you implement them in your projects.

Example

“Data governance ensures that data is accurate, secure, and compliant with regulations. I implement governance frameworks by establishing data ownership, creating data quality metrics, and ensuring that all data handling processes adhere to industry standards.”