Oshi Health Data Engineer Interview Questions + Guide in 2025

Oshi Health is at the forefront of revolutionizing gastrointestinal care through a digital clinic model that offers convenient access to a multidisciplinary care team focused on holistic patient management.

As a Data Engineer at Oshi Health, you will play a pivotal role in managing and evolving the data operations of an increasingly complex healthcare platform. The position involves collaborating closely with product, clinical, and marketing teams to develop and refine data models and pipelines that drive insights and improve patient care. Key responsibilities include developing reusable queries, establishing data quality tests, and implementing robust data governance structures. A successful candidate will possess strong SQL skills, a solid understanding of data warehousing standards, and the ability to adapt to the fast-paced, innovative environment at Oshi Health. You will be expected to work hands-on, designing solutions while ensuring the integrity and protection of sensitive client data in compliance with healthcare regulations.

This guide will help you prepare for your interview by providing insight into the specific skills and knowledge areas that Oshi Health values, enabling you to present your qualifications confidently and effectively.

The interview process for a Data Engineer at Oshi Health is designed to assess both technical skills and cultural fit within the organization. It typically unfolds over several rounds, allowing candidates to demonstrate their expertise and alignment with Oshi's mission-driven approach to healthcare.

1. Initial Screening

The process begins with an initial screening, which usually involves a conversation with a recruiter or a member of the People Operations team. This discussion focuses on your background, motivations for applying, and an overview of the role. The recruiter will also provide insights into Oshi Health's culture and values, ensuring that candidates understand the organization's commitment to inclusivity and innovation in healthcare.

2. Technical Assessment

Following the initial screening, candidates are often given a technical assessment. This may include a take-home assignment that evaluates your ability to document data architecture and engineering solutions. The assessment is designed to gauge your understanding of data operations and your ability to communicate complex technical concepts clearly.

3. Technical Interviews

Candidates typically participate in multiple technical interviews, which may involve 3 to 4 rounds with various team members, including data engineers and the Head of Engineering. These interviews focus on your technical skills, particularly in SQL, data modeling, and engineering best practices. Expect to discuss the pros and cons of different data architecture decisions and demonstrate your problem-solving abilities through real-world scenarios.

4. Team Collaboration Interview

In this round, you will meet with members of the engineering team and possibly cross-functional teams such as analysts and marketing. This interview assesses your collaborative skills and how well you can work within a team to develop data pipelines and support various business functions. You may be asked to provide examples of past projects where you successfully collaborated with others to achieve a common goal.

5. Final Interview with HR

The final step in the interview process typically involves a conversation with an HR representative. This interview focuses on cultural fit, your alignment with Oshi's core values, and any remaining questions you may have about the role or the company. It’s an opportunity for you to express your enthusiasm for the position and to discuss how you can contribute to Oshi Health's mission.

As you prepare for your interviews, consider the specific skills and experiences that will be relevant to the questions you may encounter.

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

Understand the Interview Structure

Be prepared for a multi-round interview process, which may include meetings with various stakeholders such as People Ops, the Head of Engineering, and team members. Familiarize yourself with the typical flow of interviews at Oshi Health, as this will help you manage your time and energy throughout the process. Expect a take-home assessment focused on technical documentation for data architecture and engineering, so allocate sufficient time to complete it thoughtfully.

Showcase Your Technical Expertise

Given the emphasis on SQL and algorithms in the role, ensure you are well-versed in these areas. Brush up on advanced SQL techniques, including complex queries, data modeling, and performance optimization. Be ready to discuss your experience with data architecture decisions and engineering best practices, as these topics are likely to come up during your interviews. Prepare to articulate your thought process behind various technical choices and how they align with Oshi's mission.

Emphasize Collaboration and Communication

Oshi Health values teamwork and open communication, so be prepared to discuss your experiences working collaboratively with cross-functional teams. Highlight instances where you successfully partnered with product, clinical, or marketing teams to develop data solutions. Demonstrating your ability to communicate complex technical concepts to non-technical stakeholders will be crucial, as it reflects the company's commitment to inclusivity and teamwork.

Align with Company Values

Familiarize yourself with Oshi Health's core values: Own the Outcome, Do the Right Thing, Be Direct & Open, Learn & Improve, Team, and Thrive on Diversity. Prepare examples from your past experiences that illustrate how you embody these values. This alignment will not only help you stand out as a candidate but also show that you are a good cultural fit for the organization.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving skills, adaptability, and ability to thrive in a fast-paced environment. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples that demonstrate your capabilities. Reflect on your experiences in startup environments, as this will resonate well with Oshi's dynamic culture.

Be Ready to Discuss Data Governance and Security

Given the sensitive nature of healthcare data, be prepared to discuss your understanding of data governance, security, and compliance, particularly in relation to HIPAA standards. Highlight any relevant experience you have in managing sensitive data and ensuring its integrity. This knowledge will be crucial in demonstrating your readiness to handle the responsibilities of the Data Engineer role.

Follow Up Thoughtfully

After your interviews, send a thoughtful follow-up email to express your gratitude for the opportunity and reiterate your enthusiasm for the role. Use this as a chance to reflect on specific discussions you had during the interviews, reinforcing your interest in contributing to Oshi Health's mission.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Data Engineer role at Oshi Health. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Oshi Health. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of data architecture and engineering best practices. Be prepared to discuss your experience with data pipelines, SQL, and data governance, as well as your approach to designing scalable solutions.

Technical Skills

1. Can you explain the differences between a star schema and a snowflake schema in data warehousing?

Understanding data modeling is crucial for a Data Engineer, and this question assesses your knowledge of data warehouse design.

How to Answer

Discuss the structural differences between the two schemas, including their advantages and disadvantages in terms of query performance and data integrity.

Example

“A star schema has a central fact table connected to multiple dimension tables, which simplifies queries and improves performance. In contrast, a snowflake schema normalizes dimension tables into multiple related tables, which can reduce data redundancy but may complicate queries. I typically choose a star schema for faster query performance in reporting scenarios.”

2. Describe your experience with SQL and how you have used it in your previous projects.

SQL is a fundamental skill for data engineers, and this question allows you to showcase your proficiency.

How to Answer

Highlight specific SQL functions you are familiar with, such as joins, window functions, and aggregate functions, and provide examples of how you’ve applied them in real-world scenarios.

Example

“I have extensive experience with SQL, particularly in creating complex queries to extract insights from large datasets. For instance, I used window functions to calculate running totals for sales data, which helped the marketing team identify trends over time.”

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

Data quality is critical in healthcare, and this question assesses your approach to maintaining high standards.

How to Answer

Discuss the methods you use to validate data, such as data profiling, automated testing, and monitoring processes.

Example

“I implement data quality checks at various stages of the data pipeline, including validation rules to catch anomalies during data ingestion. Additionally, I use automated tests to ensure that transformations produce the expected results, and I monitor data quality metrics to identify issues proactively.”

4. Can you walk us through a data pipeline you designed and implemented?

This question allows you to demonstrate your hands-on experience and problem-solving skills.

How to Answer

Provide a detailed overview of the pipeline, including the technologies used, the challenges faced, and the outcomes achieved.

Example

“I designed a data pipeline using Apache Airflow to automate the extraction of data from multiple sources, including APIs and databases. I implemented data transformations using Python and stored the results in a cloud-based data warehouse. This pipeline reduced the data processing time by 50% and improved the accuracy of our reporting.”

5. What strategies do you use for optimizing query performance in a data warehouse?

Optimizing performance is essential for efficient data retrieval, and this question assesses your technical expertise.

How to Answer

Discuss techniques such as indexing, partitioning, and query rewriting that you have used to enhance performance.

Example

“I focus on indexing frequently queried columns to speed up data retrieval. Additionally, I use partitioning to divide large tables into smaller, more manageable pieces, which significantly improves query performance. I also analyze query execution plans to identify bottlenecks and optimize them accordingly.”

Data Governance and Security

1. How do you handle sensitive data in compliance with regulations like HIPAA?

Given the healthcare context, this question evaluates your understanding of data security and compliance.

How to Answer

Explain your approach to data governance, including encryption, access controls, and auditing practices.

Example

“I ensure that sensitive data is encrypted both at rest and in transit. I implement strict access controls to limit data access to authorized personnel only, and I regularly audit data access logs to detect any unauthorized attempts. Additionally, I stay updated on HIPAA regulations to ensure compliance.”

2. What is your experience with data governance frameworks?

This question assesses your familiarity with best practices in data management.

How to Answer

Discuss any frameworks you have worked with and how they have influenced your data management practices.

Example

“I have experience implementing the DAMA-DMBOK framework, which emphasizes data governance principles such as data quality, data architecture, and data security. This framework has guided my approach to establishing data stewardship roles and developing policies for data management.”

3. Can you describe a time when you identified a data quality issue and how you resolved it?

This question allows you to demonstrate your problem-solving skills in a real-world context.

How to Answer

Provide a specific example of a data quality issue, the steps you took to identify and resolve it, and the impact of your actions.

Example

“I once discovered that a significant portion of our customer data had missing values due to an error in the data ingestion process. I traced the issue back to a faulty API integration and worked with the development team to fix it. I then implemented additional validation checks to prevent similar issues in the future, which improved our data accuracy by 30%.”

4. How do you approach documentation for data architecture and engineering processes?

Documentation is vital for maintaining clarity and consistency in data operations.

How to Answer

Discuss your methods for documenting processes, including the tools you use and the level of detail you provide.

Example

“I prioritize clear and comprehensive documentation for all data architecture processes. I use tools like Confluence to create detailed diagrams and descriptions of data flows, transformations, and governance policies. This documentation not only helps onboard new team members but also serves as a reference for ongoing maintenance and improvements.”

5. What role does collaboration play in your work as a Data Engineer?

Collaboration is essential in a multidisciplinary environment, and this question assesses your teamwork skills.

How to Answer

Highlight your experience working with cross-functional teams and how you ensure effective communication.

Example

“I believe collaboration is key to successful data engineering. I regularly work with product, clinical, and marketing teams to understand their data needs and ensure that our data solutions align with their goals. I facilitate regular meetings to discuss progress and gather feedback, which fosters a collaborative environment and leads to better outcomes.”