Memorial Sloan Kettering Cancer Center Data Engineer Interview Questions + Guide in 2025

Memorial Sloan Kettering Cancer Center (MSK) is dedicated to the singular mission of ending cancer for life through innovative research and compassionate patient care.

As a Data Engineer at MSK, you will play a pivotal role in shaping and maintaining the organization's data infrastructure. You will be responsible for developing and managing data pipelines that ensure the seamless flow of data from various sources into reliable repository systems. This involves integrating, transforming, and preparing data for analysis, enabling data-driven decision-making to support MSK's mission in cancer research and patient care. The ideal candidate will possess strong proficiency in SQL and PySpark, experience with cloud-based data services (preferably AWS), and a solid understanding of data warehousing concepts. Your ability to work collaboratively in cross-functional teams and communicate technical concepts effectively to non-technical stakeholders will be crucial.

This guide will help you prepare for your interview by providing insights into the skills and experiences valued at MSK, as well as the types of questions you might encounter. By understanding the role's requirements and aligning them with your own experiences, you will position yourself as a strong candidate for this impactful role.

The interview process for a Data Engineer at Memorial Sloan Kettering Cancer Center is structured to assess both technical skills and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different aspects of your qualifications and alignment with the company's mission.

1. Initial Phone Screening

The process begins with a phone screening, usually conducted by a recruiter or HR representative. This initial conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to MSK. Expect questions about your resume, your interest in the role, and how your values align with the mission of the organization.

2. Technical Interview

Following the initial screening, candidates are invited to participate in a technical interview. This round may be conducted via video conferencing and typically lasts around 45 minutes. During this interview, you will be assessed on your technical skills, particularly in SQL, PySpark, and data engineering concepts. You may be asked to solve coding problems or discuss your experience with data pipelines, cloud services, and data warehousing solutions.

3. Panel Interview

The next step often involves a panel interview with team members and possibly a hiring manager. This round is more in-depth and may include a mix of technical and behavioral questions. You will be expected to demonstrate your problem-solving abilities, discuss past projects, and explain your approach to data management and engineering challenges. This is also an opportunity for you to ask questions about the team dynamics and ongoing projects.

4. Final Interview

In some cases, a final interview may be conducted, which could involve a more informal discussion with senior management or other stakeholders. This round focuses on your long-term career goals, how you envision contributing to the team, and your ability to collaborate across departments. It’s also a chance for you to showcase your communication skills and how you would fit into the organizational culture.

Throughout the interview process, candidates are encouraged to express their passion for data engineering and their commitment to supporting MSK's mission of ending cancer for life.

As you prepare for your interviews, be ready to tackle a variety of questions that will help the interviewers gauge your fit for the role and the organization.

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

Understand the Interview Structure

The interview process at Memorial Sloan Kettering typically consists of three rounds: an initial phone screening, followed by a managerial interview, and finally a team interview. Familiarize yourself with this structure and prepare accordingly. Each round may focus on different aspects, so be ready to discuss your technical skills, past experiences, and how they align with the mission of MSK.

Emphasize Your Technical Proficiency

As a Data Engineer, you will be expected to demonstrate strong skills in SQL, PySpark, and cloud technologies. Brush up on your knowledge of data processing, database design, and cloud services, particularly AWS. Be prepared to discuss specific projects where you utilized these skills, and consider practicing coding problems that reflect the technical challenges you might face in the role.

Showcase Your Problem-Solving Abilities

Expect to encounter situational and behavioral questions that assess your problem-solving skills. Prepare examples from your past experiences where you successfully navigated complex data issues or implemented efficient data solutions. Highlight your analytical thinking and how you approach troubleshooting in a collaborative environment.

Align with the Company’s Mission

Memorial Sloan Kettering is dedicated to ending cancer for life. During your interview, express your passion for this mission and how your work as a Data Engineer can contribute to it. Be ready to discuss how your values align with the organization’s goals and how you can support their innovative research and patient care initiatives.

Prepare for Behavioral Questions

Behavioral questions are a significant part of the interview process. Reflect on your past experiences and prepare to discuss how you’ve handled challenges, worked in teams, and communicated with non-technical stakeholders. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Be Ready for Technical Assessments

You may be asked to complete a technical assessment or coding challenge as part of the interview process. Practice coding problems on platforms like LeetCode or HackerRank, focusing on medium-level questions that test your understanding of algorithms and data structures. Familiarize yourself with the specific technologies mentioned in the job description, such as Terraform and Databricks.

Communicate Clearly and Confidently

Strong communication skills are essential for a Data Engineer, especially when collaborating with cross-functional teams. Practice articulating your thoughts clearly and confidently. Be prepared to explain complex technical concepts in a way that is accessible to non-technical team members.

Follow Up Professionally

After your interviews, 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 organization. A thoughtful follow-up can leave a positive impression and keep you top of mind as they make their decision.

By preparing thoroughly and aligning your skills and experiences with the needs of Memorial Sloan Kettering, you can 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 Memorial Sloan Kettering Cancer Center. The interview process will likely assess your technical skills, problem-solving abilities, and alignment with the organization's mission. Be prepared to discuss your experience with data infrastructure, cloud technologies, and your approach to collaboration and communication.

Technical Skills

1. Can you explain the differences between SQL and NoSQL databases?

Understanding the distinctions between these database types is crucial for a Data Engineer, especially in a cloud environment.

How to Answer

Discuss the fundamental differences in structure, scalability, and use cases for each type of database. Highlight scenarios where one might be preferred over the other.

Example

"SQL databases are structured and use a predefined schema, making them ideal for complex queries and transactions. In contrast, NoSQL databases are more flexible, allowing for unstructured data and horizontal scaling, which is beneficial for handling large volumes of data in real-time applications."

2. Describe your experience with cloud data services, particularly AWS.

This question assesses your familiarity with cloud platforms, which is essential for the role.

How to Answer

Detail your experience with specific AWS services, such as S3, EC2, or Glue, and how you have utilized them in past projects.

Example

"I have extensive experience with AWS, particularly with S3 for data storage and EC2 for running data processing tasks. In my previous role, I designed a data pipeline using AWS Glue to automate ETL processes, which significantly reduced data processing time."

3. How do you ensure data quality and integrity in your pipelines?

Data quality is critical in healthcare settings, and interviewers will want to know your strategies.

How to Answer

Discuss specific techniques you use to validate and clean data, as well as monitoring practices to maintain data integrity.

Example

"I implement data validation checks at various stages of the pipeline, such as schema validation and anomaly detection. Additionally, I use logging and monitoring tools to track data quality metrics and quickly address any issues that arise."

4. Can you explain what Infrastructure as Code (IaC) is and how you have applied it?

IaC is a key concept in modern data engineering, especially in cloud environments.

How to Answer

Define IaC and provide examples of tools you have used, such as Terraform or CloudFormation, to manage infrastructure.

Example

"Infrastructure as Code allows us to manage and provision computing resources through code rather than manual processes. I have used Terraform to automate the deployment of our data infrastructure, ensuring consistency and reducing the risk of human error."

5. What is your experience with data transformation tools like PySpark?

This question evaluates your technical skills in data processing.

How to Answer

Share your experience with PySpark, including specific projects where you utilized it for data transformation.

Example

"I have used PySpark extensively for processing large datasets in a distributed environment. For instance, I developed a PySpark job that transformed raw clinical data into a structured format for analysis, which improved our reporting capabilities."

Behavioral Questions

1. Describe a time when you had to troubleshoot a data pipeline issue.

This question assesses your problem-solving skills and resilience.

How to Answer

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

Example

"Once, I encountered a significant delay in our data pipeline due to a misconfigured job in Airflow. I quickly analyzed the logs, identified the issue, and reconfigured the job. After implementing a monitoring solution, we reduced similar issues by 30% in the following months."

2. How do you prioritize tasks when managing multiple data projects?

This question evaluates your organizational skills and ability to manage time effectively.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use.

Example

"I prioritize tasks based on their impact on the business and deadlines. I use project management tools like Jira to track progress and ensure that high-impact projects receive the attention they need while maintaining communication with stakeholders."

3. Tell me about a time you collaborated with a cross-functional team.

Collaboration is key in a data engineering role, especially in a healthcare setting.

How to Answer

Share an example that highlights your communication skills and ability to work with diverse teams.

Example

"I worked closely with data scientists and clinical staff to develop a data model for patient outcomes. By facilitating regular meetings and ensuring everyone’s input was valued, we created a model that significantly improved our predictive analytics capabilities."

4. How do you handle feedback and criticism regarding your work?

This question assesses your ability to accept feedback and grow from it.

How to Answer

Discuss your perspective on feedback and provide an example of how you have used it constructively.

Example

"I view feedback as an opportunity for growth. For instance, after receiving constructive criticism on my data visualization skills, I took an online course to improve. This not only enhanced my skills but also led to better insights for our team."

5. Why do you want to work at Memorial Sloan Kettering Cancer Center?

This question gauges your motivation and alignment with the organization's mission.

How to Answer

Express your passion for the mission of MSK and how your skills can contribute to their goals.

Example

"I am deeply passionate about using data to improve patient outcomes, and MSK's commitment to ending cancer aligns perfectly with my values. I believe my experience in building robust data pipelines can help support the innovative research and clinical care that MSK provides."