SS&C Technologies Data Engineer Interview Questions + Guide in 2025

SS&C Technologies is a global leader in investment and financial services software, dedicated to providing cutting-edge solutions to financial and healthcare organizations worldwide.

As a Data Engineer at SS&C Technologies, you will play a pivotal role in transforming and optimizing data infrastructure for our cloud-native financial solutions. Your key responsibilities will include designing and implementing data models in a production environment, managing data migrations, and creating efficient ETL pipelines to ensure the seamless extraction, transformation, and loading of data from diverse sources. A successful Data Engineer will also collaborate closely with product teams and data warehouse engineers to develop analytics tools, dashboards, and machine learning functions that drive actionable insights and support business performance metrics.

To excel in this role, you should possess robust coding skills and a fundamental grasp of algorithms and data structures, along with proficiency in tools such as Hadoop, Spark, and Kafka. Strong problem-solving capabilities, excellent communication skills, and a self-motivated attitude are essential traits that align with SS&C's commitment to innovation and excellence. A Bachelor’s degree in a relevant STEM field and at least five years of experience in data engineering or a related field are required.

This guide will help you prepare for your interview by providing insights into the expectations for the Data Engineer role at SS&C Technologies, enabling you to articulate your experience and skills effectively.

The interview process for a Data Engineer position at SS&C Technologies is structured and typically consists of multiple stages designed to assess both technical and interpersonal skills.

1. Initial Phone Screen

The first step in the interview process is a phone screen with a recruiter or HR representative. This conversation usually lasts around 30-40 minutes and focuses on your resume, background, and motivations for applying to SS&C. The recruiter will also provide insights into the company culture and the specifics of the role, ensuring that you have a clear understanding of what to expect.

2. Technical Interview

Following the initial screen, candidates typically undergo a technical interview. This may be conducted via video call and lasts about an hour. During this session, you will be asked to solve coding problems and answer questions related to data structures, algorithms, and relevant technologies such as Hadoop, Spark, or ETL processes. Expect to demonstrate your problem-solving skills and coding proficiency, often through live coding exercises.

3. Behavioral Interview

After the technical assessment, candidates may participate in a behavioral interview. This round usually involves discussions with the hiring manager and possibly other team members. The focus here is on your past experiences, how you handle challenges, and your ability to work within a team. Questions may revolve around your approach to project management, collaboration, and how you align with the company’s values and culture.

4. Final Interview

The final stage typically consists of a series of interviews with senior team members or stakeholders. This may include discussions about your technical expertise, your understanding of the financial services industry, and how you would contribute to the team’s goals. This round can last several hours and may involve multiple interviewers, assessing both your technical and soft skills in a more in-depth manner.

Throughout the process, candidates are encouraged to ask questions about the team dynamics, ongoing projects, and the company’s future direction, as this demonstrates genuine interest and engagement.

As you prepare for your interview, it’s essential to be ready for a variety of questions that will test your technical knowledge and your fit within the company culture. Here are some of the types of questions you might encounter during the interview process.

Here are some tips to help you excel in your interview for the Data Engineer role at SS&C Technologies.

Understand the Company’s Cloud Transformation Journey

SS&C is heavily focused on transitioning its financial solutions to the cloud, particularly with its award-winning Genesis application. Familiarize yourself with the company's cloud-native technologies and how they are being utilized in the fintech industry. This knowledge will not only demonstrate your interest in the company but also allow you to discuss how your skills can contribute to this transformation.

Prepare for a Multi-Stage Interview Process

The interview process at SS&C typically involves multiple stages, including phone screens, behavioral interviews, and technical assessments. Be ready to discuss your resume in detail, as interviewers will likely ask about your past projects and experiences. Practice articulating your contributions to these projects, focusing on the technologies you used and the impact of your work.

Brush Up on Technical Skills

Given the technical nature of the role, ensure you are well-versed in relevant tools and technologies such as Hadoop, Spark, and ETL processes. You may encounter coding challenges or algorithmic questions, so practice coding problems on platforms like LeetCode or HackerRank. Be prepared to explain your thought process clearly, as communication is key in conveying complex technical concepts to both technical and non-technical stakeholders.

Emphasize Problem-Solving Abilities

SS&C values strong problem-solving skills. Be prepared to discuss specific challenges you have faced in previous roles and how you approached them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, highlighting your analytical thinking and ability to overcome obstacles.

Showcase Your Communication Skills

As a Data Engineer, you will need to explain complex data pipelines and analytics tools to various stakeholders. Practice articulating your thoughts clearly and concisely. Consider preparing a few examples of how you have successfully communicated technical information in the past, whether through documentation, presentations, or team discussions.

Align with Company Culture

SS&C promotes a culture of diversity and inclusion, as well as a commitment to employee wellbeing. During your interview, express your alignment with these values. Share experiences that demonstrate your ability to work collaboratively in diverse teams and your commitment to fostering an inclusive environment.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. This is also a chance to reiterate your enthusiasm for the role and the company. A well-crafted follow-up can leave a positive impression and keep you top of mind as they make their decision.

By preparing thoroughly and demonstrating your technical expertise, problem-solving skills, and alignment with SS&C's values, you will position yourself as a strong candidate for the Data Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at SS&C Technologies. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data infrastructure and cloud technologies. Be prepared to discuss your past projects, the tools you've used, and how you approach data challenges.

Technical Skills

1. What experience do you have with ETL processes, and can you describe a specific project where you implemented one?

This question assesses your practical knowledge of ETL processes, which are crucial for data engineering roles.

How to Answer

Discuss a specific project where you designed or implemented an ETL pipeline, detailing the tools used and the challenges faced.

Example

“In my previous role, I developed an ETL pipeline using Apache Airflow to automate data extraction from various sources, including APIs and databases. This pipeline transformed the data into a usable format and loaded it into our data warehouse, significantly reducing manual processing time by 40%.”

2. Can you explain the differences between Hadoop and Spark, and when you would use one over the other?

This question tests your understanding of big data technologies, which are essential for the role.

How to Answer

Highlight the strengths and weaknesses of both technologies, and provide scenarios for their use.

Example

“Hadoop is great for batch processing large datasets, while Spark excels in real-time data processing due to its in-memory computing capabilities. I would use Hadoop for processing historical data in batch jobs, whereas Spark would be my choice for applications requiring real-time analytics.”

3. Describe your experience with data modeling and how you approach designing a data schema.

This question evaluates your ability to structure data effectively.

How to Answer

Explain your methodology for data modeling, including any tools or frameworks you use.

Example

“I typically start with understanding the business requirements and then create an Entity-Relationship Diagram (ERD) to visualize the data relationships. I use tools like ER/Studio for modeling and ensure normalization to reduce redundancy while maintaining performance.”

4. How do you handle data quality issues in your projects?

This question assesses your problem-solving skills and attention to detail.

How to Answer

Discuss specific strategies you employ to ensure data quality.

Example

“I implement data validation checks at various stages of the ETL process. For instance, I use automated scripts to check for duplicates and missing values before loading data into the warehouse. Additionally, I conduct regular audits to ensure ongoing data integrity.”

5. What programming languages are you proficient in, and how have you used them in your data engineering tasks?

This question gauges your technical proficiency and practical application of programming skills.

How to Answer

List the languages you are comfortable with and provide examples of how you’ve used them.

Example

“I am proficient in Python and Java. In my last project, I used Python for data manipulation and transformation using Pandas, while Java was used for building a data ingestion service that processed real-time data streams.”

Machine Learning

1. Have you implemented any machine learning models in your data engineering work? If so, can you describe the process?

This question explores your experience with machine learning, which is increasingly relevant in data engineering.

How to Answer

Detail a specific instance where you integrated machine learning into your data processes.

Example

“I collaborated with data scientists to implement a predictive model using scikit-learn. I was responsible for preparing the data, ensuring it was clean and structured, and then deploying the model into a production environment using Docker containers.”

2. What tools or frameworks do you prefer for building data pipelines that include machine learning components?

This question assesses your familiarity with tools that facilitate machine learning integration.

How to Answer

Mention specific tools and explain why you prefer them.

Example

“I prefer using Apache Airflow for orchestrating data pipelines because of its flexibility and ease of integration with various data sources. For machine learning, I often use TensorFlow or PyTorch, depending on the complexity of the model.”

Cloud Technologies

1. What experience do you have with cloud platforms, and how have you utilized them in your data engineering projects?

This question evaluates your knowledge of cloud technologies, which are essential for modern data engineering.

How to Answer

Discuss specific cloud platforms you’ve worked with and the projects you’ve completed.

Example

“I have extensive experience with AWS, particularly with services like S3 for data storage and Glue for ETL processes. In a recent project, I migrated our on-premise data warehouse to AWS Redshift, which improved our query performance by 50%.”

2. Can you explain how you would set up a data pipeline in a cloud environment?

This question tests your practical knowledge of cloud-based data engineering.

How to Answer

Outline the steps you would take to set up a data pipeline in the cloud.

Example

“I would start by defining the data sources and then use AWS Glue to create the ETL jobs. I would store the raw data in S3, transform it using Glue, and finally load it into Redshift for analysis. I would also set up monitoring using CloudWatch to track the pipeline’s performance.”

3. How do you ensure data security and compliance in your data engineering practices?

This question assesses your understanding of data governance and security.

How to Answer

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

Example

“I implement encryption for data at rest and in transit, and I ensure that access controls are in place to restrict data access to authorized users only. Additionally, I stay updated on compliance requirements like GDPR and ensure our data practices align with those regulations.”

4. Describe a challenging data migration project you worked on. What were the key challenges, and how did you overcome them?

This question evaluates your problem-solving skills in real-world scenarios.

How to Answer

Detail a specific migration project, the challenges faced, and the solutions you implemented.

Example

“I led a project to migrate our data from an on-premise SQL server to AWS Redshift. The key challenge was ensuring data integrity during the transfer. I developed a phased migration plan, using data validation scripts to compare source and target data, which helped us identify and resolve discrepancies before going live.”

5. What strategies do you use to optimize data processing performance?

This question assesses your ability to enhance efficiency in data workflows.

How to Answer

Discuss specific techniques or tools you use to improve performance.

Example

“I utilize partitioning and indexing in databases to speed up query performance. Additionally, I regularly analyze query execution plans to identify bottlenecks and optimize them. In one instance, I reduced query times by 30% by rewriting inefficient SQL queries and implementing proper indexing strategies.”