Simplebet Software Engineer Interview Questions + Guide in 2025

Simplebet is a B2B sports technology company dedicated to transforming the fan engagement experience through machine learning and real-time solutions in micro-betting.

The Software Engineer role at Simplebet involves developing and optimizing robust data engineering pipelines and back-end services that power the company's innovative sports betting products. Key responsibilities include building reliable ETL processes utilizing Python and SQL, enhancing the Django REST API for data accessibility, and managing real-time data streams to ensure seamless user experiences. The ideal candidate should possess a strong foundation in software development, particularly in data-centric environments, alongside a collaborative spirit to work effectively within a tight-knit team. Familiarity with tools such as Databricks, Kafka, and cloud-based architectures is advantageous, as is a passion for sports and understanding of data modeling best practices.

This guide aims to equip candidates with the necessary insights and knowledge to effectively prepare for the Simplebet Software Engineer interview, enhancing their chances of success in showcasing their technical skills and cultural fit within the company.

The interview process for a Software Engineer at Simplebet is structured to assess both technical skills and cultural fit within the company. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and compatibility with the team.

1. Initial Assessment

The process begins with an initial assessment, which usually takes about 45 minutes. This assessment often includes a math test focused on probability questions, allowing candidates to demonstrate their analytical skills. The questions may vary in complexity, but they generally cover fundamental concepts that are essential for the role.

2. Technical Screen

Following the initial assessment, candidates typically undergo a technical phone screen. This interview is conducted by a member of the data science or engineering team and focuses on basic machine learning concepts, coding skills, and problem-solving abilities. Candidates should be prepared to discuss their experience with Python and SQL, as well as their understanding of data modeling and algorithms.

3. Technical Assessment

Candidates who successfully pass the technical screen are then given a more comprehensive technical assessment. This may involve a take-home coding assignment that tests their proficiency in Python and SQL, as well as their ability to analyze data. The assessment often includes probability questions and may require candidates to complete the task within a week. Clear communication and the ability to ask questions during this phase are encouraged.

4. Call with Leadership

After the technical assessment, candidates typically have a call with a senior leader or the head of the engineering team. This conversation often revolves around the candidate's previous work, the technical assessment results, and their approach to problem-solving. It’s an opportunity for candidates to showcase their thought process and how they align with the company's goals.

5. Onsite Interview

The final stage of the interview process is an onsite interview, which usually lasts half a day. During this phase, candidates meet with various team members, including senior leadership and potential colleagues. The atmosphere is generally relaxed, but candidates should expect a mix of technical and behavioral questions. This is also a chance for candidates to assess the company culture and determine if it aligns with their values.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during each stage of the process.

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

Understand the Company Culture

Simplebet places a strong emphasis on culture fit, so it's crucial to familiarize yourself with their values and work environment. Be prepared to discuss how your personal values align with the company's mission of enhancing fan engagement through innovative technology. Reflect on your past experiences and think about how they demonstrate your ability to contribute positively to the team dynamic.

Prepare for Technical Assessments

Expect a variety of technical assessments, particularly focused on algorithms and coding challenges. Brush up on your Python skills, as this is a key requirement for the role. Practice coding problems that involve data structures and algorithms, and be ready to explain your thought process clearly. Familiarize yourself with SQL as well, since it may come up in the context of data manipulation and querying.

Master Probability and Statistics

Given the emphasis on probability in the interview process, ensure you have a solid understanding of key concepts such as Bayesian statistics, p-values, and conditional probability. Be prepared to tackle probability questions that may seem straightforward but can be tricky. Practice explaining these concepts in simple terms, as you may need to communicate your understanding to non-technical stakeholders.

Be Ready for Behavioral Questions

The interview process may include personality questions that assess your fit within the team. Prepare for questions that explore your strengths and weaknesses, as well as how you handle criticism and conflict. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing clear examples from your past experiences that highlight your problem-solving skills and adaptability.

Communicate Clearly and Confidently

Throughout the interview, maintain clear and confident communication. When discussing your technical skills or past projects, be specific about your contributions and the impact they had. If you encounter a question you’re unsure about, it’s okay to take a moment to think or ask for clarification. Demonstrating a thoughtful approach can leave a positive impression.

Follow Up Professionally

After your interview, consider sending a follow-up email thanking your interviewers for their time and reiterating your interest in the position. This not only shows professionalism but also keeps you on their radar. If you don’t hear back within a reasonable timeframe, don’t hesitate to reach out for an update on your application status.

By preparing thoroughly and approaching the interview with confidence, you can position yourself as a strong candidate for the Software Engineer role at Simplebet. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Software Engineer interview at Simplebet. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to demonstrate your knowledge in algorithms, Python, SQL, and probability, as well as your ability to work collaboratively in a team environment.

Technical Skills

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the distinction between these two types of machine learning is crucial for a software engineer working with data.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight scenarios where one might be preferred over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

2. Describe a time you optimized a piece of code. What was the outcome?

This question assesses your coding skills and your ability to improve performance.

How to Answer

Provide a specific example of a code optimization you performed, detailing the problem, the solution, and the impact it had on performance.

Example

“I worked on a data processing script that was taking too long to execute. I identified that using a more efficient algorithm reduced the time complexity from O(n^2) to O(n log n), which improved the execution time from several minutes to under 30 seconds.”

3. What is your experience with ETL processes?

As a software engineer, familiarity with ETL (Extract, Transform, Load) processes is essential.

How to Answer

Discuss your experience with ETL tools and frameworks, emphasizing any specific projects where you implemented ETL processes.

Example

“I have built ETL pipelines using Python and SQL, where I extracted data from various sources, transformed it to fit our data model, and loaded it into a PostgreSQL database. This process improved data accessibility for our analytics team.”

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

Data quality is critical in software engineering, especially in data-driven environments.

How to Answer

Explain the methods you use to validate and maintain data quality throughout the data lifecycle.

Example

“I implement data validation checks at each stage of the ETL process, ensuring that data meets predefined quality standards. Additionally, I conduct regular audits and use automated testing to catch any discrepancies early.”

5. Can you explain what a REST API is and how you have used it?

Understanding REST APIs is vital for a software engineer, especially in a data-centric role.

How to Answer

Define REST APIs and describe how you have implemented or interacted with them in your previous work.

Example

“A REST API is an architectural style for designing networked applications, allowing different systems to communicate over HTTP. I have developed RESTful services using Django to serve data to our front-end applications, ensuring efficient data retrieval and manipulation.”

Algorithms and Data Structures

1. What is the time complexity of a binary search?

This question tests your understanding of algorithms and their efficiencies.

How to Answer

Explain the concept of binary search and its time complexity.

Example

“Binary search operates on sorted arrays and has a time complexity of O(log n) because it repeatedly divides the search interval in half, making it much more efficient than linear search.”

2. Describe a data structure you have implemented and its use case.

This question assesses your practical knowledge of data structures.

How to Answer

Provide an example of a data structure you have implemented, explaining its purpose and advantages.

Example

“I implemented a hash table to store user sessions in a web application. This allowed for O(1) average time complexity for lookups, significantly improving the performance of session management.”

3. How would you approach solving a problem with multiple solutions?

This question evaluates your problem-solving skills and ability to analyze different approaches.

How to Answer

Discuss your thought process for evaluating multiple solutions, including criteria for selecting the best one.

Example

“I would first outline all possible solutions and their pros and cons, considering factors like time complexity, resource usage, and maintainability. Then, I would prototype the most promising solutions to compare their performance in practice.”

4. Can you explain the concept of recursion and provide an example?

Recursion is a fundamental concept in algorithms, and understanding it is crucial for a software engineer.

How to Answer

Define recursion and provide a simple example to illustrate your point.

Example

“Recursion is a method where a function calls itself to solve smaller instances of the same problem. For example, calculating the factorial of a number can be done recursively by multiplying the number by the factorial of the number minus one until reaching one.”

5. What is a graph, and how would you traverse it?

This question tests your understanding of graph theory and traversal algorithms.

How to Answer

Define a graph and describe common traversal methods, such as depth-first search (DFS) and breadth-first search (BFS).

Example

“A graph is a collection of nodes connected by edges. To traverse a graph, I can use DFS, which explores as far as possible along each branch before backtracking, or BFS, which explores all neighbors at the present depth prior to moving on to nodes at the next depth level.”