TEKsystems Data Engineer Interview Questions + Guide in 2025

TEKsystems is a leading provider of full-stack technology services, partnering with over 6,000 clients, including 80% of the Fortune 500, to drive transformation and innovation in various industries.

As a Data Engineer at TEKsystems, you will play a crucial role in developing robust data infrastructure and solutions that meet the business needs of clients. Key responsibilities include collaborating with product managers, business analysts, and solutions architects to design and implement scalable data pipelines and architectures that leverage cloud technologies, particularly AWS. You will utilize programming languages such as Python and SQL, and frameworks like Apache Airflow to build and optimize data processing workflows. A successful candidate should have extensive experience in data warehousing, API integration, and applying best practices in data management.

To excel in this role, you must possess strong analytical and problem-solving skills, be self-motivated, organized, and able to communicate complex technical concepts to non-technical stakeholders. You should also be adaptable to changing technologies and methodologies, with a proactive approach to mentoring junior team members and promoting a culture of collaboration.

This guide will help you prepare effectively for your interview by equipping you with a deeper understanding of the role's expectations and the specific skills that TEKsystems values in its data engineers.

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

1. Initial Screening

The process begins with an initial screening, usually conducted by a recruiter. This conversation typically lasts around 30 minutes and focuses on your background, experience, and motivation for applying. 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 are often required to complete a technical assessment. This may include an online assessment (OA) that tests your knowledge in areas such as SQL, statistics, and programming concepts. Expect questions that challenge your understanding of data structures, data warehousing, and relevant technologies like Python and AWS. The technical assessment is crucial as it helps gauge your problem-solving abilities and technical proficiency.

3. Technical Interview

Candidates who perform well in the technical assessment will move on to a technical interview. This round typically involves one or more technical interviewers who will delve deeper into your technical skills. You may be asked to solve coding problems, discuss your previous projects, and explain your approach to data engineering challenges. Be prepared to demonstrate your knowledge of data pipelines, ETL processes, and cloud technologies.

4. Managerial Interview

The next step often includes a managerial interview, where you will meet with a program manager or team lead. This round focuses on your ability to work within a team, your communication skills, and how you handle project management and stakeholder interactions. Expect questions about your experience collaborating with cross-functional teams and how you prioritize tasks in a fast-paced environment.

5. HR Interview

The final round is typically an HR interview, which may cover topics such as your career goals, salary expectations, and fit within the company culture. This is also an opportunity for you to ask questions about the company’s values, work environment, and growth opportunities.

Throughout the interview process, it’s essential to showcase not only your technical expertise but also your ability to adapt and learn new technologies, as well as your commitment to collaboration and teamwork.

Now that you have an understanding of the interview process, let’s explore the specific questions that candidates have encountered during their interviews at TEKsystems.

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

Understand the Technical Landscape

Familiarize yourself with the specific technologies and tools mentioned in the job description, such as Python, SQL, AWS, and data warehousing concepts. Given the emphasis on cloud-based solutions, ensure you have a solid grasp of AWS services like Lambda, Glue, and Redshift. Be prepared to discuss your experience with these technologies and how you've applied them in past projects.

Prepare for Hands-On Assessments

Many candidates have reported a hands-on assessment as part of the interview process. This may include coding challenges or practical scenarios involving SQL queries, data transformations, or building data pipelines. Practice coding problems that focus on data manipulation and retrieval, and be ready to explain your thought process as you work through these challenges.

Brush Up on Data Structures and Algorithms

Expect questions related to data structures and algorithms, as these are fundamental to data engineering roles. Review common data structures (like arrays, linked lists, trees, and graphs) and algorithms (such as sorting and searching). Be prepared to discuss how you would use these in the context of data processing and pipeline development.

Communicate Effectively

TEKsystems values effective communication, especially when translating complex technical concepts to non-technical stakeholders. Practice articulating your past experiences and technical knowledge in a clear and concise manner. Be ready to discuss how you’ve collaborated with cross-functional teams, including product managers and business analysts, to deliver data solutions.

Showcase Your Problem-Solving Skills

During the interview, you may be presented with hypothetical scenarios or real-world problems to solve. Approach these questions methodically: clarify the problem, outline your thought process, and discuss potential solutions. Highlight your analytical skills and how you’ve tackled similar challenges in your previous roles.

Emphasize Adaptability and Continuous Learning

Given the fast-paced nature of technology, demonstrate your willingness to learn and adapt to new tools and methodologies. Share examples of how you’ve kept your skills current, whether through formal training, self-study, or hands-on projects. This will resonate well with TEKsystems' culture of innovation and transformation.

Build Rapport with Interviewers

Candidates have noted the importance of building a good rapport with interviewers, particularly with HR and managerial staff. Be personable and engage in conversation beyond just technical topics. Show genuine interest in the company and the role, and don’t hesitate to ask insightful questions about the team dynamics and company culture.

Prepare for Multiple Rounds

Be ready for a multi-stage interview process, which may include technical, managerial, and HR rounds. Each round may focus on different aspects of your experience and skills. Tailor your preparation accordingly, ensuring you can discuss both your technical expertise and your soft skills, such as teamwork and leadership.

By following these tips, you can position yourself as a strong candidate for the Data Engineer role at TEKsystems. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at TEKsystems. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data technologies. Be prepared to discuss your past projects, the tools you've used, and how you approach data engineering challenges.

Technical Skills

1. Can you explain the difference between SQL and NoSQL databases?

Understanding the differences between SQL and NoSQL databases is crucial for a Data Engineer, as it impacts how data is stored, retrieved, and managed.

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 applications with rapidly changing data requirements."

2. Describe your experience with data pipeline development. What tools have you used?

This question assesses your hands-on experience with building and maintaining data pipelines, which is a core responsibility of a Data Engineer.

How to Answer

Mention specific tools and technologies you've used, such as Apache Airflow, AWS Glue, or custom Python scripts. Provide examples of projects where you implemented these tools.

Example

"I have developed data pipelines using Apache Airflow for orchestration and AWS Glue for ETL processes. In my last project, I built a pipeline that ingested data from various sources, transformed it, and loaded it into a data warehouse, ensuring data quality and integrity throughout the process."

3. How do you handle data quality issues in your projects?

Data quality is critical in data engineering, and interviewers want to know your approach to ensuring data accuracy and reliability.

How to Answer

Discuss your strategies for identifying, monitoring, and resolving data quality issues. Mention any tools or frameworks you use for data validation.

Example

"I implement data validation checks at various stages of the pipeline to catch anomalies early. For instance, I use Python scripts to validate data types and ranges before loading data into the warehouse. Additionally, I set up alerts for any discrepancies to address issues proactively."

4. What is your experience with cloud-based data solutions?

Given the emphasis on cloud technologies in the job description, this question evaluates your familiarity with cloud platforms and services.

How to Answer

Highlight your experience with specific cloud providers (e.g., AWS, Google Cloud) and the services you have utilized, such as S3, Redshift, or BigQuery.

Example

"I have extensive experience with AWS, particularly with S3 for data storage and Redshift for data warehousing. I have also worked with AWS Lambda for serverless data processing, which allowed me to create scalable and cost-effective data solutions."

5. Can you explain the concept of ETL and its importance in data engineering?

This question tests your understanding of ETL processes, which are fundamental to data engineering.

How to Answer

Define ETL (Extract, Transform, Load) and explain its role in preparing data for analysis. Discuss the importance of each step in ensuring data is usable.

Example

"ETL stands for Extract, Transform, Load, and it's essential for integrating data from various sources into a centralized repository. The extraction phase gathers data, transformation cleans and formats it, and loading places it into a data warehouse for analysis. This process ensures that data is accurate, consistent, and ready for business intelligence."

Programming and Scripting

1. What programming languages are you proficient in, and how have you used them in data engineering?

This question assesses your programming skills, particularly in languages relevant to data engineering.

How to Answer

List the programming languages you are proficient in, such as Python or SQL, and provide examples of how you've applied them in your work.

Example

"I am proficient in Python and SQL. I use Python for data manipulation and building ETL processes, while SQL is my go-to for querying databases and performing data analysis. For instance, I wrote a Python script to automate data extraction from APIs and load it into our data warehouse."

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

This question evaluates your problem-solving skills and ability to handle complex data challenges.

How to Answer

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

Example

"One challenge I faced was a significant performance issue with a data pipeline that processed large volumes of data. I analyzed the bottlenecks and optimized the SQL queries, implemented partitioning in the data warehouse, and adjusted the pipeline's scheduling to run during off-peak hours. This reduced processing time by 40%."

3. How do you ensure your code is maintainable and scalable?

This question assesses your coding practices and understanding of software development principles.

How to Answer

Discuss your approach to writing clean, modular code and any practices you follow to ensure scalability.

Example

"I follow best practices such as writing modular code, using clear naming conventions, and including comments for clarity. I also implement unit tests to ensure functionality and maintainability. For scalability, I design my data pipelines to handle increased data loads by using distributed processing frameworks like Apache Spark."

4. What is your experience with version control systems?

Version control is crucial for collaborative development, and this question gauges your familiarity with such systems.

How to Answer

Mention the version control systems you have used, such as Git, and describe how you utilize them in your projects.

Example

"I have extensive experience using Git for version control. I use it to manage code changes, collaborate with team members, and maintain a history of project development. I follow branching strategies to ensure that features are developed in isolation before merging them into the main codebase."

5. Can you explain the concept of data warehousing and its significance?

This question tests your understanding of data warehousing, a key component of data engineering.

How to Answer

Define data warehousing and discuss its role in data management and analytics.

Example

"Data warehousing is the process of collecting and managing data from various sources to provide meaningful business insights. It allows organizations to consolidate data for analysis, enabling better decision-making and reporting. A well-designed data warehouse supports efficient querying and data retrieval, which is crucial for business intelligence."

Statistics and Data Analysis

1. How do you approach statistical analysis in your data projects?

This question evaluates your understanding of statistics and its application in data engineering.

How to Answer

Discuss your approach to statistical analysis, including any tools or methods you use.

Example

"I approach statistical analysis by first defining the objectives and the data needed. I use Python libraries like Pandas and NumPy for data manipulation and statistical analysis. For instance, I performed regression analysis to identify trends in user behavior, which informed our data strategy."

2. Can you explain the concept of outliers and how they can affect data analysis?

Understanding outliers is important for data quality, and this question tests your knowledge in this area.

How to Answer

Define outliers and discuss their potential impact on data analysis and decision-making.

Example

"Outliers are data points that differ significantly from other observations. They can skew results and lead to incorrect conclusions if not handled properly. I typically analyze outliers to determine if they are errors or valid observations and decide whether to exclude them from analysis based on their impact."

3. What statistical methods do you find most useful in data engineering?

This question assesses your familiarity with statistical methods relevant to data engineering.

How to Answer

Mention specific statistical methods you use and how they apply to your work.

Example

"I find methods like regression analysis, hypothesis testing, and clustering particularly useful. For example, I use regression analysis to predict future trends based on historical data, which helps in capacity planning for our data infrastructure."

4. How do you assess the quality of data before analysis?

Data quality is crucial for accurate analysis, and this question evaluates your approach to ensuring data integrity.

How to Answer

Discuss the steps you take to assess data quality, including validation checks and data profiling.

Example

"I assess data quality by performing data profiling to understand its structure and content. I implement validation checks to identify missing values, duplicates, and inconsistencies. This ensures that the data is clean and reliable before any analysis is conducted."

5. Can you explain the importance of data normalization?

This question tests your understanding of data normalization and its role in data management.

How to Answer

Define data normalization and discuss its significance in reducing redundancy and improving data integrity.

Example

"Data normalization is the process of organizing data to minimize redundancy and dependency. It is important because it ensures data integrity and makes it easier to maintain and update. By normalizing data, we can avoid anomalies and ensure that our data models are efficient and scalable."