Sberbank Data Engineer Interview Questions + Guide in 2025

Sberbank is a leading financial services provider in Russia, leveraging technology to enhance customer experiences and streamline banking operations.

The role of a Data Engineer at Sberbank is centered around the design, construction, and maintenance of scalable data pipelines and architectures that facilitate the flow of data across the organization. Key responsibilities include developing robust ETL processes, ensuring data integrity, and collaborating with data scientists and analysts to provide accurate and actionable insights. A successful candidate will possess a strong foundation in programming languages such as Python and SQL, alongside proficiency in data modeling, database management, and cloud technologies. Excellent problem-solving skills, a keen attention to detail, and the ability to work collaboratively in a fast-paced environment are essential traits for this position.

This guide is designed to help you prepare effectively for your interview, providing insights into the expectations and skills that will help you stand out as a candidate at Sberbank.

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

1. Initial HR Screening

The first step involves a conversation with an HR representative. This initial screening is designed to gauge your interest in the role and the company, as well as to discuss your background and experiences. Expect to answer common questions about your technical skills, career motivations, and why you are seeking a new opportunity. This stage may also include a brief overview of the company culture and values.

2. Technical Interview

Following the HR screening, candidates will participate in a technical interview. This session focuses on assessing your proficiency in relevant technologies and programming languages, particularly SQL and Python. You may be asked to solve practical problems, such as writing algorithms or executing SQL queries on sample datasets. Additionally, expect questions related to data structures, algorithms, and possibly machine learning concepts, as these are integral to the role.

3. Non-Technical Interview

After the technical assessment, candidates typically engage in a non-technical interview. This round aims to evaluate your soft skills, including communication, teamwork, and problem-solving abilities. Interviewers may ask about your interests, motivations for pursuing a career in data engineering, and how you handle challenges in a work environment. This is also an opportunity for you to express your aspirations and how they align with Sberbank's goals.

4. Test Task

In some cases, candidates may be required to complete a test task that involves practical applications of their skills. This could include tasks related to SQL queries, data manipulation, or even a small project that demonstrates your understanding of data engineering principles. The test task is an essential part of the evaluation process, as it provides insight into your hands-on capabilities.

5. Final Interview

The final stage often involves a meeting with senior team members or project representatives. This interview may cover both technical and behavioral aspects, allowing you to showcase your expertise and fit within the team. Expect to discuss your previous experiences in detail and how they relate to the responsibilities of the Data Engineer role at Sberbank.

As you prepare for these stages, it's essential to familiarize yourself with the types of questions that may arise during the interviews.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Sberbank. The interview process will likely assess your technical skills in data management, programming, and machine learning, as well as your understanding of banking operations and your ability to work collaboratively in a team environment. Be prepared to demonstrate your knowledge of SQL, algorithms, and data processing frameworks.

Technical Skills

1. Can you explain the difference between REST and RPC?

Understanding the differences between these two architectural styles is crucial for data engineers, especially when dealing with APIs.

How to Answer

Discuss the fundamental principles of both REST and RPC, highlighting their use cases and advantages. Mention how REST is stateless and uses standard HTTP methods, while RPC is more about invoking methods on remote servers.

Example

“REST is an architectural style that uses standard HTTP methods and is stateless, making it scalable and easy to cache. In contrast, RPC allows for direct method calls on remote servers, which can be more efficient for certain applications but may introduce complexity in managing state.”

2. Describe how you would optimize a SQL query.

Optimizing SQL queries is a key skill for a data engineer, as it directly impacts performance.

How to Answer

Explain the techniques you would use, such as indexing, avoiding SELECT *, and using JOINs efficiently. Provide a brief example of a situation where you successfully optimized a query.

Example

“I would start by analyzing the execution plan to identify bottlenecks. Then, I would consider adding indexes on frequently queried columns and rewriting the query to eliminate unnecessary data retrieval, such as avoiding SELECT * and only selecting the required fields.”

3. What is your experience with data processing frameworks?

Familiarity with data processing frameworks is essential for handling large datasets.

How to Answer

Discuss your experience with frameworks like Apache Spark or Hadoop, including specific projects where you utilized these tools. Highlight any challenges you faced and how you overcame them.

Example

“I have worked extensively with Apache Spark for processing large datasets. In one project, I used Spark’s DataFrame API to clean and transform data, which significantly reduced processing time compared to traditional methods.”

4. How do you handle data quality issues?

Data quality is critical in banking, and your approach to ensuring data integrity will be scrutinized.

How to Answer

Describe your process for identifying and resolving data quality issues, including validation techniques and tools you use to monitor data quality.

Example

“I implement data validation checks at various stages of the data pipeline to catch anomalies early. For instance, I use automated scripts to check for duplicates and missing values, and I collaborate with data owners to resolve any discrepancies.”

5. Can you explain how neural networks work?

Understanding neural networks is increasingly important in data engineering, especially with the rise of machine learning applications.

How to Answer

Provide a high-level overview of neural networks, including their structure (neurons, layers) and how they learn from data through backpropagation.

Example

“Neural networks consist of interconnected layers of neurons that process input data. Each neuron applies a weighted sum followed by an activation function, allowing the network to learn complex patterns through backpropagation, where the model adjusts weights based on the error of its predictions.”

Machine Learning

1. What is random forest, and how does it work?

Random forests are a popular machine learning algorithm, and understanding them is essential for data engineers involved in predictive modeling.

How to Answer

Explain the concept of ensemble learning and how random forests combine multiple decision trees to improve accuracy and reduce overfitting.

Example

“Random forests use an ensemble of decision trees to make predictions. Each tree is trained on a random subset of the data, and the final prediction is made by averaging the outputs of all trees, which helps to reduce overfitting and improve model robustness.”

2. What metrics do you use to evaluate machine learning models?

Being able to assess model performance is crucial for data engineers working with machine learning.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I typically use accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall to ensure that the model performs well across all classes. The F1 score is also useful for finding a balance between precision and recall.”

3. Describe a machine learning project you have worked on.

This question allows you to showcase your practical experience with machine learning.

How to Answer

Outline the project’s objectives, the data you used, the algorithms implemented, and the results achieved.

Example

“I worked on a project to predict customer churn for a banking client. I used historical transaction data to train a logistic regression model, which helped identify at-risk customers. The model achieved an accuracy of 85%, allowing the client to implement targeted retention strategies.”

4. How do you deal with overfitting in machine learning models?

Overfitting is a common challenge in machine learning, and your strategies for addressing it will be evaluated.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning that you use to prevent overfitting.

Example

“To combat overfitting, I use cross-validation to ensure that my model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 and L2 to penalize overly complex models, and I prune decision trees to simplify them.”

5. What is your understanding of feature engineering?

Feature engineering is a critical step in the machine learning process, and your approach will be assessed.

How to Answer

Explain the importance of feature engineering and provide examples of techniques you have used to create or transform features.

Example

“Feature engineering is vital for improving model performance. I often create new features by combining existing ones, such as calculating the ratio of transactions to account balance. I also use techniques like one-hot encoding for categorical variables to make them suitable for modeling.”