Synovus Data Engineer Interview Questions + Guide in 2025

Synovus is committed to expanding its digital footprint while creating a customer-centric experience that strengthens its brand promise.

In the role of a Data Engineer, you will play a pivotal part in the company’s digital data transformation initiatives. Your key responsibilities will include developing and maintaining scalable data pipelines, ensuring the analytics systems align with business requirements, and managing third-party vendors. You will be instrumental in applying analytical rigor to support business intelligence reporting and predictive modeling, while also collaborating with various stakeholders to foster a culture of data-informed decision-making.

To excel in this role, strong expertise in data integration, ETL processes, and data visualization is essential. You should possess advanced skills in SQL, Python, and data modeling concepts, while also demonstrating innovation in data strategies. Additionally, exceptional collaboration and communication skills are crucial as you will need to advocate for data initiatives across different business units.

This guide will help you prepare for your interview by focusing on the specific skills and experiences that Synovus values, positioning you as a strong candidate for the Data Engineer role.

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

1. Initial Screening

The first step is an initial screening, which usually takes about 30-45 minutes. This is typically conducted by a recruiter who will discuss your background, experience, and motivation for applying to Synovus. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role. This is an opportunity for you to ask questions about the company and clarify any concerns regarding sponsorship, as Synovus has specific policies regarding international candidates.

2. Technical Interview

Following the initial screening, candidates will participate in a technical interview that lasts approximately 1-1.5 hours. This interview may be conducted by one or two technical team members and will focus on your proficiency in data engineering concepts, including SQL, data modeling, and ETL processes. Expect to answer questions related to your experience with data visualization tools, statistical methods, and any relevant programming languages such as Python or R. You may also be asked to solve problems or discuss past projects that demonstrate your technical capabilities.

3. Behavioral Interview

The behavioral interview is designed to assess how well you align with Synovus's values and culture. This round typically involves questions about your teamwork, problem-solving abilities, and how you handle challenges in a collaborative environment. Interviewers will be interested in your past experiences and how they relate to the responsibilities of the Data Engineer role, particularly in fostering a culture of data-informed decision-making.

4. Final Interview

In some cases, there may be a final interview with senior leadership or cross-functional team members. This stage is less common but provides an opportunity for you to showcase your strategic thinking and how you can contribute to the broader goals of the organization. It may also involve discussions about your vision for data engineering and how you can drive innovation within the company.

As you prepare for your interviews, it's essential to be ready for a mix of technical and behavioral questions that will help the interviewers gauge your fit for the role and the company. Here are some of the questions that candidates have encountered during the interview process.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Synovus. The interview will likely cover a mix of technical skills, data management concepts, and collaboration abilities. Candidates should be prepared to demonstrate their expertise in data engineering, data visualization, and their ability to work with various stakeholders.

Technical Skills

1. What are the benefits of Object-Oriented Programming (OOP)?

Understanding the principles of OOP is crucial for data engineers, as it can impact how you design and implement data solutions.

How to Answer

Discuss the key benefits of OOP, such as code reusability, scalability, and maintainability. Provide examples of how these benefits can be applied in data engineering projects.

Example

“OOP allows for code reusability through inheritance, which can significantly reduce redundancy in data processing scripts. For instance, I created a base class for data transformation that could be extended for various data sources, making it easier to maintain and update the codebase.”

2. Can you explain what an interface is in C#?

This question tests your understanding of programming concepts that are often used in data engineering tasks.

How to Answer

Define an interface and explain its purpose in C#. Highlight how interfaces can facilitate better design and flexibility in your code.

Example

“An interface in C# defines a contract that classes can implement. It allows for a flexible design where different classes can be treated uniformly, which is particularly useful in data processing where various data sources may require different handling methods.”

3. Describe your experience with ETL processes.

ETL (Extract, Transform, Load) is a fundamental aspect of data engineering, and interviewers will want to know your hands-on experience.

How to Answer

Detail your experience with ETL tools and processes, emphasizing any specific technologies you have used and the impact of your work.

Example

“I have extensive experience with ETL processes using tools like Apache NiFi and Talend. In my previous role, I designed an ETL pipeline that reduced data processing time by 30%, allowing for more timely insights for business stakeholders.”

4. How do you ensure data quality in your projects?

Data quality is critical for effective data analysis and decision-making.

How to Answer

Discuss the methods and tools you use to monitor and ensure data quality throughout the data lifecycle.

Example

“I implement data validation checks at various stages of the ETL process, using tools like Great Expectations to automate quality checks. Additionally, I regularly conduct data audits to identify and rectify any anomalies.”

5. What is your experience with data visualization tools?

Data visualization is a key responsibility for a Data Engineer, and interviewers will want to assess your proficiency in this area.

How to Answer

Mention the specific tools you have used and how you have applied them to create meaningful visualizations for stakeholders.

Example

“I have worked extensively with Tableau and Power BI to create interactive dashboards that provide insights into key performance metrics. One project involved developing a dashboard that visualized customer engagement data, which helped the marketing team tailor their campaigns effectively.”

Data Management and Analysis

1. How do you approach data modeling?

Data modeling is essential for structuring data effectively for analysis.

How to Answer

Explain your process for creating data models, including any methodologies you follow and tools you use.

Example

“I follow a dimensional modeling approach to create star schemas that simplify reporting and analysis. I use tools like ERwin to design and visualize the data model, ensuring it aligns with business requirements.”

2. Can you explain the difference between structured and unstructured data?

Understanding data types is fundamental for a Data Engineer.

How to Answer

Define both types of data and provide examples of each, highlighting their implications for data processing.

Example

“Structured data is organized in a predefined format, such as relational databases, while unstructured data lacks a specific structure, like text documents or social media posts. Handling unstructured data often requires different processing techniques, such as natural language processing.”

3. What statistical techniques do you use for data analysis?

Statistical knowledge is important for interpreting data and making informed decisions.

How to Answer

Discuss the statistical methods you are familiar with and how you have applied them in your work.

Example

“I frequently use regression analysis and hypothesis testing to derive insights from data. For instance, I applied regression analysis to understand the factors affecting customer churn, which informed our retention strategies.”

4. How do you handle data integration from multiple sources?

Data integration is a common challenge in data engineering.

How to Answer

Describe your approach to integrating data from various sources, including any tools or frameworks you use.

Example

“I utilize Apache Kafka for real-time data integration from multiple sources, ensuring that data is consistently available for analysis. I also implement data transformation processes to standardize data formats across sources.”

5. What experience do you have with cloud data platforms?

Cloud platforms are increasingly used for data storage and processing.

How to Answer

Mention the cloud platforms you have worked with and how you have leveraged them in your projects.

Example

“I have experience with AWS and Azure, where I have utilized services like Amazon Redshift for data warehousing and Azure Data Factory for orchestrating data workflows. This has allowed for scalable and efficient data processing solutions.”