Susquehanna International Group Data Engineer Interview Questions + Guide in 2025

Susquehanna International Group (SIG) is a global quantitative trading firm that leverages game theory and probabilistic thinking to optimize decision-making in financial markets.

As a Data Engineer at SIG, you will play a crucial role in designing, developing, and maintaining complex database systems that handle vast amounts of data. Your responsibilities will include supporting the development of database applications, writing Linux shell scripts and Python for batch processing, and implementing solutions using technologies like Hadoop, Hive, Druid, and Spark. You will also be responsible for maintaining and supporting Oracle database instances in a high-transaction environment, performing routine operational activities, and providing after-hours support when necessary.

To excel in this role, you should possess a minimum of 7 years of progressive experience in database development and administration, ideally with a strong foundation in Oracle systems. Proficiency in Python and Unix shell scripting, as well as experience with large datasets (terabytes), is essential. Additionally, you should be skilled in query performance tuning and be able to effectively communicate with end users to address their needs. The ideal candidate is a naturally curious problem solver who is motivated to innovate and grow within a collaborative team environment.

This guide will help you prepare by highlighting key areas of focus for the interview process, including technical skills, problem-solving abilities, and cultural fit within SIG.

The interview process for a Data Engineer role at Susquehanna International Group is structured and thorough, designed to assess both technical skills and cultural fit. Here’s a breakdown of the typical steps involved:

1. Initial Recruiter Call

The process begins with a phone screening conducted by a recruiter. This initial conversation typically lasts around 30 minutes and focuses on your background, experience, and motivation for applying to SIG. Expect questions about your previous roles, technical skills, and how you align with the company’s values and culture.

2. Online Assessment

Following the recruiter call, candidates are usually invited to complete an online coding assessment, often hosted on platforms like CodeSignal or Codility. This assessment typically includes a set of coding problems that test your knowledge of algorithms, data structures, and problem-solving abilities. The questions can range from easy to medium difficulty, and candidates are advised to practice common coding challenges to prepare effectively.

3. Technical Interview

If you perform well on the online assessment, the next step is a technical interview, which may be conducted over the phone or via video call. This interview usually lasts about an hour and involves in-depth discussions about your coding solutions from the assessment, as well as additional technical questions related to database management, SQL queries, and programming concepts. Interviewers may also explore your experience with specific technologies relevant to the role, such as Python, Unix shell scripting, and big data frameworks like Hadoop and Spark.

4. Onsite Interview

Candidates who successfully navigate the technical interview are typically invited for an onsite interview, which can last several hours and consist of multiple rounds. During this phase, you will engage in hands-on coding exercises, system design challenges, and discussions about your past projects. Expect to collaborate with multiple interviewers, including engineers and managers, who will assess your technical skills, problem-solving approach, and ability to communicate effectively.

5. Behavioral Interview

In addition to technical assessments, there is often a behavioral interview component. This part of the process focuses on your interpersonal skills, teamwork, and how you handle challenges in a work environment. Interviewers may ask about your experiences working in teams, resolving conflicts, and adapting to changing situations.

6. Final Discussions

The final step may involve discussions about team fit and potential projects you could work on at SIG. This is also an opportunity for you to ask questions about the company culture, growth opportunities, and any other concerns you may have.

As you prepare for your interview, it’s essential to be ready for a mix of technical and behavioral questions that reflect the skills and experiences outlined in the job description. Now, let’s delve into the specific interview questions that candidates have encountered during the process.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Susquehanna International Group. The interview process will likely focus on your technical skills, particularly in database management, data processing, and programming. Be prepared to demonstrate your problem-solving abilities and your understanding of data systems, as well as your experience with relevant technologies.

Technical Skills

1. Can you explain the differences between Hadoop, Hive, and Spark?

Understanding the distinctions between these technologies is crucial for a Data Engineer role, as they are commonly used in data processing and analytics.

How to Answer

Discuss the primary functions of each technology, emphasizing their use cases and how they complement each other in a data pipeline.

Example

"Hadoop is a framework for distributed storage and processing of large data sets using the MapReduce programming model. Hive is a data warehouse infrastructure built on top of Hadoop that provides data summarization and query capabilities using a SQL-like language. Spark, on the other hand, is a fast and general-purpose cluster computing system that can process data in memory, making it significantly faster than Hadoop for certain tasks."

2. Describe your experience with SQL performance tuning.

Performance tuning is essential for maintaining efficient database operations, especially in high-transaction environments.

How to Answer

Provide specific examples of techniques you have used to optimize SQL queries, such as indexing, query rewriting, or analyzing execution plans.

Example

"I have extensive experience in SQL performance tuning, particularly in optimizing complex queries. For instance, I identified slow-running queries by analyzing execution plans and implemented indexing strategies that reduced query execution time by over 50%. Additionally, I regularly review and refactor queries to ensure they are efficient and scalable."

3. How do you handle data migration between RDBMS and Big Data systems?

Data migration is a common task for Data Engineers, and understanding the challenges involved is key.

How to Answer

Discuss the tools and methodologies you use for data migration, as well as any challenges you have faced and how you overcame them.

Example

"I typically use tools like Apache Sqoop for transferring data between RDBMS and Hadoop ecosystems. During a recent migration project, I faced challenges with data integrity and consistency. I implemented a two-phase commit protocol to ensure that data was accurately transferred and validated before finalizing the migration."

4. What is your experience with Python and shell scripting for data processing?

Python and shell scripting are essential for automating data workflows and batch processing.

How to Answer

Share specific projects where you utilized Python and shell scripts, highlighting the tasks you automated and the impact on efficiency.

Example

"In my previous role, I developed Python scripts to automate data extraction and transformation processes, which reduced manual effort by 70%. Additionally, I wrote shell scripts to schedule and monitor batch jobs, ensuring that data pipelines ran smoothly and on time."

5. Can you explain how you would design a database schema for a high-transaction environment?

Designing a robust database schema is critical for performance and scalability.

How to Answer

Discuss the principles of database normalization, indexing strategies, and how you would ensure data integrity and performance.

Example

"When designing a database schema for a high-transaction environment, I prioritize normalization to reduce data redundancy while ensuring that the schema supports efficient querying. I also implement indexing on frequently queried columns and consider partitioning large tables to improve performance. Additionally, I use foreign keys to maintain data integrity across related tables."

Problem Solving

1. Describe a challenging data problem you faced and how you resolved it.

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

How to Answer

Provide a specific example, detailing the problem, your approach to solving it, and the outcome.

Example

"At one point, we encountered significant latency issues in our data processing pipeline due to a bottleneck in data ingestion. I conducted a thorough analysis and discovered that our data source was overwhelmed. I proposed a solution to implement a queuing system that allowed for asynchronous data ingestion, which improved our processing speed by 40%."

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

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

How to Answer

Discuss the methods you use to validate and clean data, as well as any tools or frameworks you employ.

Example

"I ensure data quality by implementing validation checks at various stages of the data pipeline. I use tools like Apache NiFi for data flow management, which allows me to set up data validation rules. Additionally, I regularly conduct data audits and use automated testing frameworks to catch anomalies early in the process."

3. What strategies do you use for debugging data processing issues?

Debugging is a critical skill for Data Engineers, and interviewers want to know your approach.

How to Answer

Explain your systematic approach to identifying and resolving data processing issues.

Example

"When debugging data processing issues, I start by isolating the problem area, whether it's in the data ingestion, transformation, or storage phase. I use logging and monitoring tools to trace data flow and identify where the failure occurs. Once I pinpoint the issue, I analyze the logs and data to understand the root cause and implement a fix, followed by thorough testing to ensure the issue is resolved."

4. How would you approach designing a data pipeline for a new application?

This question assesses your ability to design scalable and efficient data architectures.

How to Answer

Outline the steps you would take, from requirements gathering to implementation and monitoring.

Example

"I would start by gathering requirements from stakeholders to understand the data needs of the application. Next, I would design the data pipeline architecture, selecting appropriate technologies for data ingestion, processing, and storage. After implementing the pipeline, I would set up monitoring and alerting to ensure its performance and reliability, making adjustments as necessary based on usage patterns."

5. Can you discuss a time when you had to work with a difficult stakeholder?

Collaboration is key in data engineering, and this question evaluates your interpersonal skills.

How to Answer

Share a specific example, focusing on how you navigated the situation and maintained a positive working relationship.

Example

"I once worked with a stakeholder who had very specific data requirements that were challenging to meet. I scheduled regular check-ins to ensure I understood their needs and kept them updated on progress. By actively listening and incorporating their feedback, I was able to deliver a solution that met their expectations while also educating them on the technical constraints we faced."