Press ganey Data Engineer interview questions & guide 2026

The questions below reflect patterns observed in our interview process. While these are representative, remember that your specific interviewers may focus on different aspects of your background based on the team’s current priorities. Use these to identify your strengths and areas where you may need to sharpen your narrative.

Technical and Domain Expertise

These questions assess your hands-on experience with the Azure stack and your ability to manage data at scale.

• How have you optimized Spark jobs for performance in a large-scale Databricks environment?
• Can you describe your experience implementing Delta Lake for versioning and data reliability?

Preparation at Press Ganey requires a blend of deep technical recall and the ability to articulate your architectural philosophy. Do not simply list your tools; explain the why behind your technical choices.

Role-Related Knowledge – You must demonstrate mastery of Azure, Databricks, and Spark. Interviewers look for evidence that you understand the nuances of these technologies, not just how to use them, and can apply them to solve enterprise-scale problems.

Problem-Solving Ability – We look for candidates who can take an ambiguous requirement and break it down into a scalable technical solution. Be prepared to explain your thought process, including the alternatives you considered and why you chose your specific path.

Leadership and Collaboration – As a Staff or Senior Data Engineer, you are expected to influence team direction. Whether it is through mentoring junior staff or driving best practices, demonstrate how you elevate the performance of those around you.

The interview process at Press Ganey is designed to evaluate both your technical depth and your alignment with our collaborative culture. It typically begins with a recruiter screen to establish your baseline experience and interest, followed by a series of technical deep dives and stakeholder interviews. You should expect a rigorous pace, where each stage builds on the previous one to provide a comprehensive view of your engineering capabilities.

This timeline illustrates the progression from initial screening to the final panel. Candidates should use this as a roadmap to pace their preparation, ensuring they are ready for both high-level system design conversations and granular coding assessments. Because the process is structured, treat each stage as a distinct opportunity to showcase a different facet of your professional toolkit.

Technical Proficiency

We evaluate your ability to write clean, maintainable, and performant code.

Be ready to go over:

• Spark Optimization: Techniques like partition tuning, broadcast joins, and caching.
• Pipeline Architecture: Designing for idempotency, error handling, and restartability.

As a Data Engineer, you will spend your time designing, building, and optimizing data pipelines that serve as the backbone for Press Ganey's analytics products. You will be responsible for the entire lifecycle of data—from ingestion and transformation to storage and delivery. This involves creating robust ELT/ETL frameworks that can handle the volume and variety of healthcare data while ensuring that data quality remains high.

Collaboration is a daily requirement. You will work closely with data scientists to provide them with the curated datasets they need for machine learning models and partner with business units to deliver reports via Power BI. You will also be expected to contribute to the engineering culture by writing clear documentation, conducting code reviews, and enforcing best practices for reproducibility and maintainability.

A successful candidate will possess a strong foundation in cloud-based data engineering and a desire to work on complex, high-impact systems.

• Must-have skills: 5+ years of experience with Azure (Data Lake, ADF, SQL), Databricks, Spark, and Delta Lake. Proficiency in Python and SQL is non-negotiable.
• Experience level: 7+ years of total experience in data engineering, preferably in cloud-native environments.
• Soft skills: Excellent communication skills, a proactive approach to problem-solving, and a proven track record of leading projects.
• Nice-to-have skills: Familiarity with Scala, exposure to machine learning integration, and experience with GitLab or Azure DevOps for CI/CD.

Q: How difficult are the technical assessments? A: The technical rounds are rigorous but focus on practical applications of your skills rather than abstract riddles. Expect to demonstrate your ability to solve real-world data engineering problems using the tools listed in the job description.

Q: What is the company culture like for engineers? A: Press Ganey values innovation, collaboration, and a deep commitment to the healthcare mission. We look for engineers who are not only technically proficient but also curious and invested in the outcomes their data supports.

Q: What is the typical interview timeline? A: The process generally moves at a steady pace, usually spanning a few weeks from the initial recruiter screen to the final decision. We prioritize transparency and aim to keep candidates informed at every stage.

• Articulate your impact: When discussing past projects, focus on the "why" and the business outcome. Did your pipeline reduce latency? Did it improve data quality for a specific product?
• Prepare for behavioral questions: Use the STAR method (Situation, Task, Action, Result) to provide structured, clear answers to behavioral queries.
• Know your resume: Be prepared to discuss any project listed on your resume in deep technical detail; interviewers will dive into the "how" of your past work.
• Stay current: Be ready to discuss the latest trends in Azure and Databricks, as we value engineers who keep their skills current.

The Data Engineer role at Press Ganey offers a unique opportunity to apply sophisticated engineering practices to challenges that directly improve healthcare. By mastering the core technologies of our stack and preparing to discuss your architectural decisions with clarity, you will be well-positioned to succeed in our interview process.

Focus your preparation on your hands-on experience with Azure and Databricks, and ensure you can articulate your approach to complex system design. We encourage you to explore your own experiences through the lens of our evaluation criteria. You are capable of navigating this process, and we look forward to seeing the unique perspective you would bring to our team.

The salary data provided reflects the range for this position, which is influenced by factors such as your experience, technical expertise, and regional market conditions. Candidates should view this range as a baseline and be prepared to discuss their expectations based on their specific value proposition and qualifications.