Tempus Labs Data Engineer Interview Questions + Guide in 2025

Tempus Labs is a pioneering healthcare technology company that leverages artificial intelligence and data to enhance clinical care and precision medicine.

As a Data Engineer at Tempus Labs, you will play a crucial role in managing and integrating complex data systems to support the company’s mission of creating the largest integrated dataset of molecular and clinical data. You will work closely with engineers, product managers, scientists, and operations teams to build and maintain enterprise data models that incorporate diverse sources such as clinical records, genomic data, and lab results. Key responsibilities include authoring and maintaining data documentation, advocating for best practices in data modeling, and implementing solutions to monitor data quality.

The ideal candidate will possess a strong background in healthcare informatics, advanced SQL skills, and experience with various data modeling techniques, including 3NF and dimensional modeling. Familiarity with ETL processes, database technologies, and data modeling tools is essential. Additionally, experience with GCP architecture and clinical data would be beneficial.

This guide will help you prepare for your data engineering interview at Tempus Labs by equipping you with insights into the role's requirements, the company's values, and the types of questions you may encounter.

The interview process for a Data Engineer at Tempus Labs is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and experience.

1. Initial Recruiter Call

The process begins with a 30-minute phone call with a recruiter. This initial screening focuses on your background, interest in the role, and basic qualifications. The recruiter will also provide insights into Tempus Labs' culture and the specifics of the Data Engineer position. This is an opportunity for you to ask questions about the company and the role.

2. Technical Assessment

Following the recruiter call, candidates are usually required to complete a technical assessment. This may take the form of a take-home coding challenge or an online assessment that tests your SQL skills, algorithms, and possibly Python programming. The assessment is designed to evaluate your problem-solving abilities and coding proficiency, particularly in relation to data manipulation and analysis.

3. Video Interviews

Candidates who successfully pass the technical assessment will move on to a series of video interviews. These typically include: - A technical interview with team members focusing on system design, data modeling, and specific technical challenges relevant to the role. - A behavioral interview that assesses your soft skills, teamwork, and how you align with the company’s values. Expect scenario-based questions that explore your past experiences and how you handle challenges.

4. Onsite Interview (or Virtual Onsite)

The final stage often involves an onsite interview, which may be conducted virtually. This session usually consists of multiple rounds, including: - A deep dive into your take-home assignment or technical assessment, where interviewers will ask you to explain your thought process and decisions. - Additional technical interviews that may cover data architecture, ETL processes, and the application of data modeling techniques. - Behavioral interviews with various team members, including product managers and engineering leads, to assess your fit within the team and your approach to collaboration.

5. Final Review and Offer

After the onsite interviews, the hiring team will review your performance across all stages. Candidates can expect to receive feedback and a decision within a few days to a week. If selected, the recruiter will discuss the offer details, including compensation and benefits.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during this process.

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

Communicate Clearly and Professionally

Given the feedback from candidates about communication issues during the interview process, it’s crucial to maintain clear and professional communication throughout. Be proactive in following up after interviews, and don’t hesitate to ask for clarification on any points that may seem ambiguous. This not only shows your interest in the role but also your commitment to effective communication, which is vital in a collaborative environment like Tempus.

Prepare for a Multi-Stage Interview Process

The interview process at Tempus typically involves multiple stages, including a recruiter screen, a take-home coding challenge, and several rounds of interviews with team members. Familiarize yourself with each stage and prepare accordingly. For the take-home assignment, ensure you understand the requirements and take the time to produce quality work that reflects your skills. During the interviews, be ready to discuss your take-home project in detail, as it often serves as a focal point for technical discussions.

Brush Up on Technical Skills

As a Data Engineer, you will need to demonstrate strong SQL skills and a solid understanding of data modeling techniques. Review key concepts such as 3NF, dimensional modeling, and data vault techniques. Be prepared to discuss your experience with ETL/ELT processes and how you have applied these in previous roles. Additionally, practice coding challenges that focus on SQL and algorithms, as these are common in the interview process.

Showcase Your Domain Knowledge

Tempus values candidates with domain knowledge in healthcare and informatics. Be prepared to discuss your understanding of clinical data, genomics, and next-generation sequencing. If you have experience with specific healthcare data systems like HL7 or Epic, make sure to highlight this during your interviews. Demonstrating your familiarity with the industry will set you apart from other candidates.

Emphasize Collaboration and Teamwork

Tempus operates in small, autonomous teams, so showcasing your ability to work collaboratively is essential. Prepare examples from your past experiences where you successfully collaborated with cross-functional teams, such as engineers, product managers, and scientists. Highlight your role in these collaborations and the impact they had on project outcomes.

Be Ready for Behavioral Questions

Expect a mix of technical and behavioral questions during your interviews. Prepare for scenario-based questions that assess your problem-solving abilities and how you handle challenges. Reflect on your past experiences and be ready to discuss your strengths, weaknesses, and how you’ve overcome obstacles in your work.

Stay Informed About Company Culture

Tempus has received mixed reviews regarding its organizational culture. It’s important to convey your alignment with the company’s mission and values during the interview. Research the company’s goals and recent developments, and be prepared to discuss how your personal values align with their mission to advance precision medicine.

Follow Up After Interviews

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview and reiterate your interest in the role. This not only demonstrates professionalism but also keeps you on the interviewers' radar as they make their decisions.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Engineer role at Tempus Labs. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Tempus Labs. The interview process will likely focus on your technical skills, particularly in SQL, data modeling, and system design, as well as your ability to work collaboratively in a team environment. Be prepared to discuss your experience with data integration, data quality, and the healthcare domain.

Technical Skills

1. Can you explain the differences between 3NF, star schema, and data vault modeling techniques?

Understanding data modeling techniques is crucial for this role, as it directly impacts how data is structured and accessed.

How to Answer

Discuss the characteristics of each modeling technique, including their use cases and advantages. Highlight your experience with these models in previous projects.

Example

"3NF is focused on reducing redundancy and ensuring data integrity, making it ideal for transactional systems. Star schema simplifies data retrieval for analytical queries, which is beneficial for reporting. Data vault modeling provides flexibility and scalability, allowing for easy integration of new data sources. In my previous role, I utilized star schema for our data warehouse to enhance query performance."

2. Describe your experience with SQL in an enterprise data warehouse environment.

SQL proficiency is essential for managing and querying large datasets.

How to Answer

Provide specific examples of complex SQL queries you have written, including joins, subqueries, and performance optimization techniques.

Example

"I have extensive experience writing complex SQL queries for data extraction and transformation in an enterprise data warehouse. For instance, I optimized a query that involved multiple joins and aggregations, reducing execution time by 40% by indexing key columns and restructuring the query."

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

Data quality is critical in healthcare applications, and interviewers will want to know your approach.

How to Answer

Discuss the methods you use to monitor data quality, such as profiling, validation checks, and automated testing.

Example

"I implement data quality checks at various stages of the ETL process, including profiling source data for completeness and consistency. I also set up automated alerts for any anomalies detected in the data, ensuring that any issues are addressed promptly."

4. Can you walk us through your experience with ETL/ELT processes?

Understanding ETL/ELT processes is vital for integrating data from various sources.

How to Answer

Explain the ETL/ELT processes you have worked on, the tools you used, and the challenges you faced.

Example

"I have designed and implemented ETL processes using tools like Apache NiFi and Talend. One challenge I faced was integrating data from disparate sources with different formats. I developed a flexible transformation layer that standardized the data before loading it into our data warehouse."

5. What strategies do you use for data modeling in a microservices architecture?

Given Tempus' emphasis on microservices, understanding how to model data in this context is important.

How to Answer

Discuss how you approach data modeling in a microservices environment, including considerations for data ownership and service boundaries.

Example

"In a microservices architecture, I advocate for decentralized data ownership, where each service manages its own data. I use API contracts to define how services interact with each other, ensuring that data models are aligned with business capabilities while maintaining data integrity across services."

Behavioral Questions

1. Describe a time when you had to collaborate with cross-functional teams.

Collaboration is key in a role that involves working with engineers, product managers, and scientists.

How to Answer

Share a specific example that highlights your teamwork skills and how you navigated challenges.

Example

"During a project to integrate genomic data into our platform, I collaborated with data scientists and product managers. We held regular meetings to align on requirements and address any roadblocks. This collaboration led to a successful integration that improved our data offerings significantly."

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

Time management is crucial in a fast-paced environment.

How to Answer

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

Example

"I use a combination of Agile methodologies and project management tools like Jira to prioritize tasks. I assess the impact and urgency of each task, ensuring that critical projects align with business goals while maintaining flexibility to adapt to changing priorities."

3. Tell us about a challenging data problem you faced and how you resolved it.

Problem-solving skills are essential for a Data Engineer.

How to Answer

Provide a specific example of a data-related challenge, the steps you took to resolve it, and the outcome.

Example

"I encountered a significant data quality issue where incoming clinical data had inconsistent formats. I led a team to develop a data cleansing process that standardized the formats before ingestion. This not only resolved the issue but also improved our data processing time by 30%."

4. Why do you want to work at Tempus Labs?

Understanding the company's mission and values is important for cultural fit.

How to Answer

Express your passion for precision medicine and how your skills align with Tempus' goals.

Example

"I am passionate about leveraging data to improve patient outcomes, and Tempus' mission to advance precision medicine resonates with me. I believe my experience in data engineering and my commitment to quality will contribute to the impactful work being done here."

5. How do you handle feedback and criticism?

Being open to feedback is crucial for personal and professional growth.

How to Answer

Share your perspective on feedback and provide an example of how you have used it to improve.

Example

"I view feedback as an opportunity for growth. In a previous role, I received constructive criticism on my data modeling approach. I took the time to understand the feedback, sought additional resources, and applied the lessons learned to future projects, resulting in improved data structures and team collaboration."