A Data Engineer at Garner health plays a critical role in transforming how consumers navigate the healthcare ecosystem. The core mission of the company relies heavily on data-driven insights to identify high-quality, affordable medical providers. As a member of the engineering team, you will design, build, and scale the data pipelines and infrastructure that ingest and process massive volumes of healthcare claims, clinical records, and provider directories. Your work directly impacts the accuracy and reliability of the recommendation engines used by thousands of members to make critical health decisions.

The complexity of this role stems from the highly unstructured and fragmented nature of healthcare data. You will face the unique challenge of designing robust schema architectures, implementing data quality checks, and building scalable ETL/ELT pipelines. This position requires a strong balance of software engineering practices, statistical understanding, and data modeling expertise. By delivering clean, reliable, and performant data products, you enable data scientists, analysts, and product teams to drive strategic insights and build impactful user-facing features.

Working at Garner health means collaborating with cross-functional teams in a fast-paced environment where data integrity is paramount. You will have a direct hand in shaping the architecture of a growing data platform. For engineers who enjoy solving ambiguous problems at the intersection of technology and healthcare, this role offers a high level of ownership, technical autonomy, and the opportunity to drive meaningful real-world impact.

The following questions are representative of what you can expect during the Data Engineer interview process. These questions are drawn from real interview experiences and are grouped by category to help you identify patterns and structure your preparation.

Python & SQL Coding

These questions evaluate your core programming skills, data manipulation capabilities, and your ability to write clean, efficient, and optimized code.

• Write a Python script to parse a custom log file, extract specific fields, and aggregate the results by a timestamp.
• Given a database schema with user actions and transaction histories, write a SQL query to calculate the rolling 7-day average of user spending.

To succeed in the Garner health selection process, you must demonstrate a balanced combination of technical mastery, analytical thinking, and cultural alignment. Preparation should focus not just on writing code, but on explaining the "why" behind your technical decisions.

Technical Execution – You must demonstrate strong proficiency in Python and SQL. Interviewers look for clean, readable code, efficient algorithmic choices, and a deep understanding of database indexing, query optimization, and data structures.

Analytical Foundations – Beyond writing pipelines, you need to understand the data itself. Be prepared to discuss statistics and probability concepts, as you will need to validate data distributions and ensure the integrity of the data flowing through your systems.

System Design & Architecture – You will be asked to design complex data systems on a whiteboard or digital canvas. You should be comfortable discussing data modeling, storage technologies, scalability bottlenecks, and trade-offs between batch and stream processing.

Collaboration & Ambiguity – Healthcare data is notoriously messy and requirements can change quickly. You must show that you can work effectively with cross-functional partners, ask clarifying questions, and make pragmatic trade-offs to deliver value.

The interview process for a Data Engineer at Garner health is designed to evaluate both your technical depth and your alignment with the company's collaborative culture. Candidates can expect a multi-stage journey that tests coding, statistical reasoning, system design, and behavioral competencies. The process generally spans several weeks, and maintaining proactive communication is key.

The journey begins with a recruiter screen to discuss your background and interest in the company. This is followed by a technical screen focusing on live coding in Python and SQL, along with statistical problem-solving. If you pass the initial screen, you will move to the final rounds, which include a detailed hiring manager interview, a systems architecture design session, a technical case study or presentation, and a cultural fit interview.

The timeline above outlines the typical progression of stages from your initial application to the final decision. Candidates should use this visual roadmap to pace their preparation, ensuring they allocate sufficient time to practice coding and statistics before the initial screens, while saving deep architectural review for the later stages. Note that the exact scheduling and sequence of rounds may occasionally vary depending on team availability and candidate location.

To excel in the Garner health interview process, you must understand the specific competencies evaluated in each core round. The following sections break down the focus areas, expectations, and key concepts you should master.

Python and SQL Coding

This area evaluates your hands-on software engineering capabilities. You are expected to write production-grade code that is not only correct but also optimized for performance and readability.

Be ready to go over:

• Python data structures – Deep understanding of lists, dictionaries, sets, and generators, and when to use them.
• SQL performance tuning – Utilizing indexes, understanding execution plans, and optimizing complex joins.
• Data manipulation libraries – Comfort with libraries like Pandas or PySpark for processing and transforming structured datasets.
• Advanced SQL concepts – Mastery of window functions, common table expressions (CTEs), and recursive queries.

Example questions or scenarios:

• "Write a Python function to find the first non-repeating character in a stream of healthcare provider IDs."
• "Given a table of patient visits, write a SQL query to find the longest consecutive streak of daily visits for each patient."

Statistics and Data Case Studies

This evaluation area tests your ability to apply mathematical and statistical concepts to real-world data problems. Garner health relies on rigorous data validation, making this round highly critical.

Be ready to go over:

• Probability theory – Basic probability rules, conditional probability, and Bayes' theorem.
• Data quality metrics – How to define, measure, and monitor data drift and schema drift in production pipelines.
• Descriptive statistics – Understanding mean, median, variance, standard deviation, and percentiles in skewed distributions.
• Advanced concepts (less common) – Hypothesis testing, p-values, and confidence intervals applied to pipeline data validation.

Example questions or scenarios:

• "How would you design an automated system to detect if an upstream data provider has suddenly started sending corrupted or incomplete claims data?"
• "Explain how you would calculate the probability that a specific provider recommendation is an anomaly based on historical search patterns."

Systems Architecture & Diagramming

In this round, you will be asked to design a scalable data system. You will need to draw a diagram, explain your architectural choices, and defend your decisions against potential failure modes.

Be ready to go over:

• Data warehousing & modeling – Star schema, Snowflake schema, and dimensional modeling concepts.
• Storage technologies – Choosing between relational databases, NoSQL databases, data lakes, and document stores.
• Data integration patterns – Designing robust ETL/ELT pipelines, handling late-arriving data, and implementing retry mechanisms.
• Advanced concepts (less common) – Distributed systems architectures, partitioning, sharding, and message queuing systems like Kafka.

Example questions or scenarios:

• "Design an architecture to ingest, process, and store millions of daily medical claims from various formats (JSON, CSV, XML) into a centralized data warehouse."
• "How would you modify your design if the business suddenly required real-time dashboards with sub-second query latency?"

Cultural Fit & Situational Judgment

This round focuses on your soft skills, work ethic, and alignment with the company's operating principles. Interviewers want to understand how you handle adversity, collaborate with teammates, and navigate ambiguity.

Be ready to go over:

• Conflict resolution – How you handle disagreements with colleagues or stakeholders in a constructive manner.
• Prioritization – How you manage competing deadlines and decide which tasks to prioritize when resources are limited.
• Ownership and growth – Examples of taking initiative, learning from failures, and mentoring others.

Example questions or scenarios:

• "Tell me about a time when you realized a production data pipeline had a critical bug that impacted downstream business metrics. What did you do?"
• "Describe a situation where you had to work with a non-technical stakeholder to gather requirements for a complex data engineering project."