As a Data Engineer at Health Catalyst, you will play a pivotal role in transforming healthcare data into actionable insights that drive better patient outcomes and operational efficiencies. This position is vital for the development and maintenance of data pipelines, ensuring that accurate data is available for analytics and decision-making processes across various healthcare applications. Your work will directly impact the effectiveness of products used by healthcare providers, allowing them to leverage data in innovative ways.

The complexity and scale of the data you will handle are significant, involving large datasets from diverse sources. You will collaborate with cross-functional teams, including data scientists, analysts, and software engineers, to build robust data architectures that support the company's mission of improving healthcare through data-driven solutions. This role not only offers technical challenges but also provides an opportunity to contribute to meaningful change in the healthcare industry, making it both critical and rewarding.

Expect your interviews to cover a range of topics, showcasing your technical expertise as well as your analytical thinking. The following questions are indicative of what you might encounter, drawn from online interview communities. Remember, these questions aim to illustrate patterns rather than serve as a memorization list.

Technical / Domain Questions

This category assesses your expertise in data engineering principles and technologies.

• Explain the differences between a data warehouse and a data lake.
• What is normalization? Why is it important?

Preparation is key to success in interviews at Health Catalyst. Focus on demonstrating your technical skills and your fit within the company's culture.

Role-related knowledge – This criterion evaluates your technical expertise in data engineering, including familiarity with SQL, ETL processes, and data architecture. Prepare to discuss your hands-on experience with relevant tools and technologies.

Problem-solving ability – Interviewers will assess how you approach complex problems, structure your thought process, and devise effective solutions. Be ready to articulate your problem-solving methodology clearly.

Culture fit / values – It’s essential to align with Health Catalyst's mission and values. Showcase your commitment to improving healthcare outcomes and how your work contributes to this goal.

The interview process for the Data Engineer position at Health Catalyst typically begins with an initial conversation with a recruiter, followed by a technical assessment focused on SQL skills. If successful, you will progress to an on-site interview, which involves technical case studies and behavioral interviews with various team members.

Expect a rigorous and thorough evaluation, as the company values collaboration, data-driven decision making, and a strong commitment to its mission. The process is designed to not only assess your technical abilities but also how well you can work within a team-oriented environment.

This visual timeline illustrates the stages of the interview process at Health Catalyst. Use it to guide your preparation and manage your energy effectively throughout each stage. Be aware that the pace and structure may vary slightly depending on the specific team and role.

Understanding how you will be evaluated can significantly enhance your preparation. Here are key areas of focus for the Data Engineer role:

Technical Expertise

Demonstrating a strong grasp of technical skills is crucial. Interviewers will assess your knowledge of data engineering principles, databases, and programming languages.

• SQL Proficiency – Be prepared to write complex queries and optimize them for performance.
• ETL Processes – Understand the end-to-end process of extracting, transforming, and loading data.
• Data Modeling – Be able to discuss various data modeling techniques and their applications.

Problem-Solving Skills

Your ability to tackle complex problems will be a significant area of evaluation.

• Analytical Thinking – Discuss how you approach data-related challenges with logic and clarity.
• Real-World Scenarios – Be ready to apply your skills to hypothetical situations that may arise in the role.

Collaboration and Communication

Given the collaborative nature of the work, effective communication is essential.

• Team Dynamics – Share experiences where you've successfully worked within a team to achieve common goals.
• Stakeholder Engagement – Describe how you would communicate technical concepts to non-technical stakeholders.