Epikso Data Engineer Interview Questions + Guide in 2025

Epikso is an innovative company specializing in cloud-based solutions and data-driven technologies aimed at enhancing business processes and outcomes.

As a Data Engineer at Epikso, you will play a crucial role in designing, coding, and implementing data solutions primarily focused on cloud-based applications using technologies such as Spark and Pyspark. Your responsibilities will encompass maintaining and enhancing existing code, ensuring adherence to CI/CD practices, and actively collaborating with business analysts to understand and fulfill data requirements. You will also be expected to mentor less experienced team members while leading innovation through the exploration and implementation of emerging data-centric technologies.

Key skills required for this role include a strong proficiency in SQL and Python (Pyspark), experience with AWS services (such as Glue and S3), and familiarity with version control systems like Git. A background in Data Warehousing and DevOps practices will further enhance your fit for this position. Additionally, strong communication skills and the ability to thrive in a dynamic team environment are essential traits that align with Epikso's collaborative culture.

This guide will help you prepare effectively for your interview by providing insights into the key competencies and expectations for the Data Engineer role at Epikso.

The interview process for a Data Engineer at Epikso is structured to assess both technical skills and cultural fit within the team. It typically consists of several key stages:

1. Initial Screening

The process begins with an initial screening, which is usually a phone interview with a recruiter. This conversation lasts about 30 minutes and focuses on your background, experience in data engineering, and understanding of the role. The recruiter will also gauge your communication skills and discuss your expectations regarding compensation and work environment.

2. Technical Assessment

Following the initial screening, candidates undergo a technical assessment. This may be conducted via a video call with a senior data engineer or technical lead. During this session, you will be asked to solve practical problems related to SQL, data architecture, and cloud technologies, particularly AWS services like Glue and S3. Expect to discuss your previous projects, including the architecture and technologies used, as well as your experience with Python and Pyspark.

3. Onsite Interview

The onsite interview typically consists of multiple rounds, each lasting around 45 minutes. You will meet with various team members, including data engineers and project managers. These interviews will cover a mix of technical and behavioral questions. You may be asked to demonstrate your knowledge of data warehousing, CI/CD practices, and version control using Git. Additionally, you will have the opportunity to showcase your problem-solving skills through case studies or coding exercises.

4. Final Interview

The final interview is often with senior management or team leads. This round focuses on your fit within the company culture and your ability to collaborate with cross-functional teams. You may be asked about your leadership experiences, mentoring capabilities, and how you handle dynamic work environments. This is also a chance for you to ask questions about the team and the projects you would be working on.

As you prepare for your interview, consider the specific skills and experiences that will be relevant to the questions you may encounter.

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, particularly AWS services like Glue, S3, and Lambda, as well as Spark and PySpark. Be prepared to discuss your hands-on experience with these technologies, including any projects where you implemented them. This will demonstrate your technical proficiency and your ability to contribute to the team from day one.

Master SQL and Data Warehousing Concepts

Given the emphasis on SQL and data warehousing in the role, ensure you can confidently explain complex SQL queries, including inner joins and other advanced functions. Prepare to discuss your experience with data modeling and how you have structured data warehouses in previous roles. This will showcase your analytical skills and your understanding of data architecture.

Showcase Your DevOps Knowledge

Since the role requires familiarity with CI/CD practices and tools like Jenkins and Git, be ready to discuss your experience in these areas. Highlight any specific instances where you implemented CI/CD pipelines or managed source code in Git. This will illustrate your ability to work in a collaborative environment and your commitment to maintaining high-quality code.

Communicate Effectively

Strong communication skills are essential for this role, especially since you will be collaborating with business analysts and mentoring less experienced team members. Practice articulating your thoughts clearly and concisely. Be prepared to discuss how you have effectively communicated complex technical concepts to non-technical stakeholders in the past.

Prepare for Behavioral Questions

Expect questions that assess your teamwork and problem-solving abilities. Reflect on past experiences where you faced challenges in a team setting and how you contributed to overcoming them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your role in the success of the project.

Align with Company Culture

Research Epikso’s company culture and values. Be prepared to discuss how your personal values align with those of the company. This could include your approach to innovation, collaboration, and continuous learning. Demonstrating cultural fit can be just as important as technical skills in the hiring process.

Ask Insightful Questions

At the end of the interview, take the opportunity to ask thoughtful questions about the team dynamics, ongoing projects, and the company’s future direction. This not only shows your genuine interest in the role but also helps you assess if the company is the right fit for you.

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

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

SQL and Data Warehousing

1. Can you explain the difference between INNER JOIN and LEFT JOIN in SQL?

Understanding SQL joins is crucial for data manipulation and retrieval.

How to Answer

Clearly define both types of joins and provide examples of when you would use each.

Example

“An INNER JOIN returns only the rows where there is a match in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. For instance, if I have a table of customers and a table of orders, an INNER JOIN would show only customers who have placed orders, whereas a LEFT JOIN would show all customers, including those who haven’t placed any orders.”

2. Describe a complex SQL query you wrote and the problem it solved.

This question assesses your practical experience with SQL.

How to Answer

Discuss the context of the problem, the SQL functions you used, and the outcome.

Example

“I once wrote a complex SQL query to analyze customer purchase patterns. I used multiple JOINs and aggregate functions to summarize data by customer segments. This helped the marketing team tailor their campaigns effectively, resulting in a 20% increase in engagement.”

3. How do you optimize SQL queries for performance?

Performance optimization is key in data engineering.

How to Answer

Mention techniques such as indexing, query restructuring, and analyzing execution plans.

Example

“To optimize SQL queries, I focus on indexing frequently queried columns, avoiding SELECT *, and using EXPLAIN to analyze execution plans. For instance, I improved a slow-running report by adding indexes on the join columns, which reduced the execution time from several minutes to under 30 seconds.”

4. What is your experience with data warehousing solutions?

This question gauges your familiarity with data storage and retrieval systems.

How to Answer

Discuss specific data warehousing technologies you’ve used and your role in implementing them.

Example

“I have extensive experience with AWS Redshift for data warehousing. In my last project, I designed the data model and implemented ETL processes to load data from various sources, ensuring data integrity and availability for analytics.”

Python and Data Processing

1. How do you handle missing data in a dataset using Python?

Handling missing data is a common challenge in data engineering.

How to Answer

Explain the methods you use to identify and address missing values.

Example

“I typically use Pandas to handle missing data. I first identify missing values using the isnull() function, then decide whether to fill them with a mean/median or drop the rows, depending on the context. For instance, in a sales dataset, I filled missing sales figures with the average of the respective product category to maintain data integrity.”

2. Can you explain how you would implement a data pipeline using Python?

This question tests your understanding of data engineering workflows.

How to Answer

Outline the steps involved in creating a data pipeline, including data extraction, transformation, and loading.

Example

“I would start by using Python scripts to extract data from APIs or databases. Then, I would use libraries like Pandas for data transformation, cleaning, and validation. Finally, I would load the processed data into a data warehouse using SQLAlchemy or similar libraries, ensuring the pipeline is automated with scheduling tools like Airflow.”

3. What libraries do you commonly use for data manipulation in Python?

This question assesses your familiarity with Python libraries.

How to Answer

Mention libraries you’ve used and their specific applications.

Example

“I frequently use Pandas for data manipulation due to its powerful DataFrame structure. I also use NumPy for numerical operations and Matplotlib for data visualization. For instance, I used Pandas to clean and analyze a large dataset, which significantly improved our reporting capabilities.”

4. Describe a project where you used PySpark. What challenges did you face?

This question evaluates your experience with big data processing.

How to Answer

Discuss the project context, your role, and how you overcame challenges.

Example

“In a recent project, I used PySpark to process large datasets for real-time analytics. One challenge was managing memory usage, so I optimized the code by using DataFrames instead of RDDs and partitioned the data effectively. This improved processing speed and allowed us to deliver insights faster.”

Cloud Technologies and DevOps

1. What experience do you have with AWS services relevant to data engineering?

This question assesses your knowledge of cloud platforms.

How to Answer

Discuss specific AWS services you’ve used and their applications in your projects.

Example

“I have hands-on experience with AWS Glue for ETL processes, S3 for data storage, and Lambda for serverless computing. In one project, I used Glue to automate data extraction and transformation, which streamlined our data pipeline and reduced manual effort.”

2. How do you implement CI/CD practices in your data engineering projects?

This question evaluates your understanding of DevOps practices.

How to Answer

Explain your approach to integrating CI/CD in data workflows.

Example

“I implement CI/CD by using Jenkins to automate testing and deployment of data pipelines. I ensure that every change is tested in a staging environment before going live, which minimizes errors and maintains data integrity. For instance, I set up automated tests for our ETL processes, which significantly reduced deployment issues.”

3. Can you describe your experience with version control systems like Git?

This question assesses your familiarity with code management.

How to Answer

Discuss how you use version control in your projects.

Example

“I use Git for version control to manage code changes and collaborate with team members. I follow best practices like branching for features and pull requests for code reviews. This approach has helped maintain code quality and streamline collaboration in our projects.”

4. How do you ensure data security and compliance in your projects?

This question evaluates your understanding of data governance.

How to Answer

Discuss the measures you take to protect data and comply with regulations.

Example

“I ensure data security by implementing encryption for data at rest and in transit. I also follow compliance guidelines such as GDPR by anonymizing sensitive data and maintaining proper access controls. In my last project, I conducted regular audits to ensure compliance, which helped us avoid potential legal issues.”