Strive Health is dedicated to revolutionizing kidney care through innovative solutions that enhance patient outcomes and streamline healthcare processes.

As a Data Engineer at Strive Health, you will play a crucial role in constructing and maintaining data pipelines that integrate diverse healthcare data sources into a cohesive data platform. This position requires a strong understanding of both technical and business aspects of data management, particularly in the context of healthcare systems. Key responsibilities include analyzing source system data to ensure data quality, developing cloud-based architecture components, and applying your knowledge of healthcare data types and standards to inform pipeline development. To excel in this role, you should have experience with SQL and Python, familiarity with ETL processes, and a keen analytical mindset. Your contributions will directly support Strive Health's mission to provide high-quality, coordinated care to patients with kidney disease.

This guide aims to equip you with the insights and knowledge needed to navigate the interview process effectively, setting you up for success in securing the Data Engineer position at Strive Health.

The interview process for a Data Engineer role at Strive Health is designed to assess both technical skills and cultural fit within the organization. Here’s what you can expect:

1. Initial Screening

The process begins with an initial screening, typically conducted by a recruiter over the phone. This conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Strive Health. The recruiter will also provide insights into the company culture and the specific expectations for the Data Engineer role.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment. This may take place via a video call and will involve a data engineering professional from the team. During this session, you can expect to tackle questions related to SQL, data pipeline construction, and ETL/ELT processes. You may also be asked to solve a coding challenge, which could involve writing Python scripts or analyzing data sets to demonstrate your problem-solving abilities.

3. Behavioral Interview

After successfully completing the technical assessment, candidates will participate in a behavioral interview. This round typically involves multiple interviewers and focuses on your past experiences, teamwork, and how you align with Strive Health's values. Expect questions that explore your analytical skills, adaptability to new technologies, and your approach to overcoming challenges in a collaborative environment.

4. Final Interview

The final interview is often a more in-depth discussion with senior members of the data engineering team or management. This round may include a mix of technical and behavioral questions, as well as discussions about your long-term career goals and how they align with Strive Health's mission. You may also be asked to present a past project or experience that showcases your skills and contributions to data engineering.

As you prepare for your interview, it’s essential to familiarize yourself with the types of questions that may arise during each stage of the process.

Strive Health Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Strive Health. The interview will focus on your technical skills, understanding of data engineering principles, and your ability to apply these in a healthcare context. Be prepared to discuss your experience with data pipelines, SQL, and cloud-based architectures, as well as your familiarity with healthcare data standards.

Technical Skills

1. Can you explain the ETL process and how you have implemented it in your previous projects?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, especially in healthcare where data integrity is paramount.

How to Answer

Discuss your experience with ETL processes, emphasizing the tools you used and the challenges you faced. Highlight how you ensured data quality and integrity throughout the process.

Example

“In my previous role, I implemented an ETL process using Apache NiFi to extract data from various EHR systems. I transformed the data to ensure it met our quality standards and loaded it into our data warehouse. I faced challenges with data inconsistencies, which I addressed by implementing validation checks during the transformation phase.”

2. Describe your experience with SQL and how you have used it to manipulate data.

SQL is a fundamental skill for data engineers, and your ability to write complex queries will be assessed.

How to Answer

Provide specific examples of SQL queries you have written, including any advanced functions or optimizations you implemented. Mention how these queries contributed to your projects.

Example

“I have extensive experience with SQL, including writing complex queries with joins and subqueries to analyze patient data. For instance, I created a query that aggregated patient outcomes based on treatment types, which helped our clinical team identify effective interventions.”

3. What cloud technologies have you worked with, and how did you apply them in your data engineering tasks?

Familiarity with cloud-based architectures is essential for this role, especially in a healthcare setting.

How to Answer

Discuss the cloud platforms you have experience with, such as AWS, Azure, or Google Cloud, and how you utilized their services for data storage, processing, or analytics.

Example

“I have worked extensively with AWS, utilizing services like S3 for data storage and Redshift for data warehousing. I built a data pipeline that ingested clinical data into S3, transformed it using AWS Glue, and loaded it into Redshift for analysis, which significantly improved our reporting capabilities.”

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

Data quality is critical in healthcare, and interviewers will want to know your strategies for maintaining it.

How to Answer

Explain the methods you use to validate and clean data, as well as any monitoring tools you implement to catch issues early.

Example

“I ensure data quality by implementing validation rules at each stage of the pipeline. I use tools like Great Expectations to automate data validation and set up alerts for any anomalies. This proactive approach has helped us maintain high data integrity and trustworthiness.”

5. Can you discuss your experience with both structured and unstructured data?

Understanding how to handle different data types is important for a Data Engineer, especially in a healthcare context.

How to Answer

Share your experience with various data formats and how you processed them, including any specific tools or frameworks you used.

Example

“I have worked with both structured data from relational databases and unstructured data from sources like clinical notes. For unstructured data, I used Apache Spark with NLP libraries to extract meaningful insights, which were then integrated into our structured datasets for comprehensive analysis.”

Healthcare Domain Knowledge

1. What healthcare data standards are you familiar with, and how have you applied them?

Knowledge of healthcare data standards is essential for ensuring compliance and interoperability.

How to Answer

Discuss specific standards like HL7 or FHIR and provide examples of how you have implemented them in your projects.

Example

“I am familiar with FHIR and HL7 standards, which I applied while integrating data from various EHR systems. I ensured that our data models adhered to these standards, facilitating seamless data exchange and improving interoperability between systems.”

2. How do you stay updated on trends and changes in healthcare data management?

The healthcare industry is constantly evolving, and staying informed is crucial for a Data Engineer.

How to Answer

Mention any resources, communities, or events you follow to keep your knowledge current.

Example

“I regularly attend webinars and conferences focused on healthcare data management. I also follow industry publications and participate in online forums to discuss best practices and emerging technologies with peers.”

3. Can you describe a project where you had to analyze claims data? What challenges did you face?

Analyzing claims data is a common task in healthcare data engineering, and interviewers will want to know your experience.

How to Answer

Share a specific project, the challenges you encountered, and how you overcame them.

Example

“In a recent project, I analyzed claims data to identify patterns in patient admissions. One challenge was dealing with missing data, which I addressed by implementing imputation techniques and collaborating with clinical teams to fill in gaps, ultimately leading to actionable insights for reducing readmission rates.”

4. How do you approach data privacy and security in your engineering practices?

Data privacy is critical in healthcare, and your understanding of best practices will be evaluated.

How to Answer

Discuss the measures you take to ensure data security and compliance with regulations like HIPAA.

Example

“I prioritize data privacy by implementing encryption for data at rest and in transit. I also ensure that our data pipelines comply with HIPAA regulations by conducting regular audits and training team members on best practices for handling sensitive information.”

5. Describe a time when you had to collaborate with non-technical stakeholders. How did you ensure effective communication?

Collaboration is key in a healthcare setting, and your ability to communicate technical concepts to non-technical stakeholders will be assessed.

How to Answer

Provide an example of a project where you worked with non-technical teams and how you facilitated understanding.

Example

“I worked on a project where I had to present data insights to the clinical team. I created visualizations that simplified complex data and used analogies to explain technical concepts. This approach helped bridge the gap between our teams and led to a successful implementation of data-driven decisions.”

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• How would you design a machine learning model to classify major health issues based on health features? You work as a machine learning engineer for a health insurance company. Design a model that classifies if an individual will undergo major health issues based on a set of health features.

• What metrics and statistical methods would you use to identify dishonest users in a sports tracking app? You work for a company with a sports app that tracks running, jogging, and cycling data. Formulate a method to identify users who might be cheating, such as driving a car while claiming to be on a bike ride. Specify the metrics and statistical methods you would analyze to detect athletic anomalies.

• Develop a function str_map to determine if a one-to-one correspondence exists between characters of two strings at the same positions. Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

• Build a logistic regression model from scratch using gradient descent without an intercept term. Create a logistic regression model from scratch, using basic gradient descent (with Newton's method) as the optimization method and the log-likelihood as the loss function. Do not include an intercept term or a penalty term. You may use numpy and pandas but not scikit-learn. Return the parameters of the regression.

• Why are job applications decreasing despite stable job postings? You observe that the number of job postings per day has remained stable, but the number of applicants has been decreasing. What could be the reasons for this trend?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook informs you that friend requests have decreased by 10%. How would you approach this issue?

• How would you assess the validity of a .04 p-value in an AB test? Your company is running an AB test to increase conversion rates on a landing page, and the PM finds a p-value of .04. How would you evaluate the validity of this result?

• How would you analyze the performance of a new LinkedIn feature without an AB test? LinkedIn has launched a feature allowing candidates to message hiring managers directly during the interview process, but due to engineering constraints, it can't be AB tested. How would you analyze the feature's performance?

• Customer success manager vs. free trial for Square's new product? The CEO of Square's small business division wants to hire a customer success manager for a new software product, while another executive suggests a free trial. What would be your recommendation for getting new or existing customers to use the new product?

• How would you build a fraud detection model using a dataset of 600,000 credit card transactions? Imagine you work at a major credit card company and are given a dataset of 600,000 credit card transactions. Describe your approach to building a fraud detection model.

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• How would you tackle multicollinearity in multiple linear regression? Describe the methods you would use to address multicollinearity in a multiple linear regression model.

• How would you design a facial recognition system for employee clock-in and secure access? You work as an ML engineer for a large company that wants to implement a facial recognition system for employee clock-in, clock-out, and access to secure systems, including temporary contract consultants. How would you design this system?

• How would you handle data preparation for building a machine learning model using imbalanced data? Explain the steps you would take to prepare data for building a machine learning model when dealing with imbalanced data.

Embarking on your journey to become a Data Engineer at Strive Health is an exciting opportunity to shape the future of healthcare through innovative data solutions. To gain deeper insights into what the role entails, explore our detailed Strive Health Interview Guide where you'll find a plethora of interview questions and strategies that could be asked. We've also crafted specialized interview guides for roles like software engineer and data analyst, offering a comprehensive view of Strive Health's interview process across various positions.

At Interview Query, we equip you with the indispensable insights and preparation techniques required to master every challenge of your Strive Health Data Engineer interview. Delve into our vast array of company interview guides for an edge in your preparation. Should you have any questions, we're here to help you every step of the way.

Best of luck with your interview!