Humana Data Engineer Interview Questions + Guide in 2025

Humana Inc. is a leading health insurance company committed to putting health first for its members, employees, and the communities they serve.

The Data Engineer role at Humana is essential for designing, building, and maintaining robust data processing architectures that transform complex datasets into actionable insights. As part of the Integrated Health technology team, the Data Engineer will work on a variety of data-centric projects, utilizing cloud-native platforms to ingest and manage data effectively. This position requires expertise in developing data pipelines, data lakes, and data warehouses, leveraging tools such as Apache Airflow, Spark, and various cloud platforms including AWS, Azure, and GCP.

Candidates should possess a strong background in programming and data manipulation, with proficiency in ETL/ELT processes, big data technologies, and RESTful APIs. A Bachelor's degree in Computer Science or related fields is typically required, along with a minimum of five years of relevant experience. Ideal candidates will be innovative thinkers who thrive in a collaborative environment and are passionate about utilizing data to enhance healthcare services.

By following this guide, you will gain insights into the specific skills and experiences Humana values in their Data Engineers, enabling you to tailor your preparation for a successful interview.

The interview process for a Data Engineer position at Humana is structured to assess both technical and behavioral competencies, ensuring candidates align with the company's mission and values. The process typically consists of several rounds, each designed to evaluate different aspects of your skills and fit for the role.

1. Initial Screening

The first step in the interview process is an initial screening, usually conducted by a recruiter. This conversation lasts about 30 minutes and focuses on your background, experience, and motivations for applying to Humana. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, allowing you to gauge if it aligns with your career goals.

2. Technical Interview

Following the initial screening, candidates typically undergo a technical interview. This round may involve one or more sessions with a technical lead or a senior data engineer. During this interview, you will be assessed on your knowledge of data engineering concepts, including data pipelines, ETL processes, and cloud technologies. Expect to discuss your experience with tools such as Apache Airflow, Spark, and various cloud platforms like AWS or Azure. You may also be asked to solve technical problems or case studies relevant to the role.

3. Behavioral Interview

The next round often includes a behavioral interview, which may be conducted by the hiring manager or a product manager. This interview focuses on your past experiences and how they relate to the competencies required for the Data Engineer position. You will be asked to provide examples of how you have handled challenges, collaborated with teams, and contributed to projects in previous roles. This is an opportunity to demonstrate your problem-solving skills and your ability to work in a team-oriented environment.

4. Final Interview

The final stage of the interview process may involve a more in-depth discussion with senior leadership or cross-functional team members. This round is designed to assess your strategic thinking and how you can contribute to Humana's goals. You may be asked to discuss your vision for data engineering within the healthcare sector and how you can leverage data to improve patient outcomes. This is also a chance for you to ask questions about the company's direction and how the Data Engineer role fits into that vision.

As you prepare for your interviews, consider the specific skills and experiences that will be relevant to the questions you may encounter. Next, let's delve into the types of questions that candidates have faced during the interview process.

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

Understand the Interview Structure

Be prepared for a multi-round interview process, typically consisting of four rounds. The first round will likely be with the hiring manager, followed by three technical interviews. The second round may include both behavioral and technical questions, so be ready to discuss your past experiences and how they relate to the role. Familiarize yourself with the STAR method (Situation, Task, Action, Result) to effectively articulate your experiences.

Showcase Your Technical Expertise

As a Data Engineer, you will be expected to demonstrate a strong understanding of data processing architectures, ETL/ELT tools, and cloud platforms. Brush up on your knowledge of tools like Apache Airflow, Talend, and big data technologies such as Hadoop and Spark. Be prepared to discuss your experience with data pipelines, data lakes, and data warehouses, as well as your familiarity with RESTful APIs and microservices architecture.

Align with Humana's Mission

Humana emphasizes a commitment to putting health first. During your interview, express your passion for using data to improve healthcare outcomes. Share examples of how your work has positively impacted customer experiences or contributed to health initiatives. This alignment with the company's mission will resonate well with your interviewers.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and teamwork skills. Reflect on past projects where you faced challenges and how you overcame them. Highlight your ability to work independently and make decisions on complex issues, as this is a key aspect of the role.

Emphasize Continuous Learning

Humana values employees who are eager to learn and grow. Discuss any recent courses, certifications, or projects that demonstrate your commitment to staying current in the field of data engineering. If you have certifications in cloud technologies or experience with CI/CD pipelines, be sure to mention them.

Create a Comfortable Interview Environment

If your interview is conducted remotely, ensure you have a quiet, dedicated space free from interruptions. Test your internet connection beforehand to meet the required speed of at least 25 Mbps download and 10 Mbps upload. This will help you present yourself professionally and avoid technical issues during the interview.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers about the team dynamics, ongoing projects, and how the Data Engineering role contributes to Humana's overall strategy. This shows your genuine interest in the position and helps you assess if the company culture aligns with your values.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Engineer role at Humana. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Humana. The interview process will likely assess your technical skills, problem-solving abilities, and understanding of data engineering principles, particularly in the context of healthcare. Be prepared to discuss your experience with data pipelines, cloud platforms, and data processing architectures.

Technical Skills

1. Can you explain the difference between ETL and ELT?

Understanding the distinction between these two data processing methods is crucial for a Data Engineer.

How to Answer

Discuss the processes involved in both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), emphasizing when to use each based on data volume and processing needs.

Example

“ETL is a process where data is extracted from various sources, transformed into a suitable format, and then loaded into a data warehouse. In contrast, ELT loads raw data into the data warehouse first and then transforms it as needed. ELT is often more efficient for large datasets, especially in cloud environments where storage is cheaper.”

2. Describe your experience with cloud platforms like AWS, Azure, or GCP.

Your familiarity with cloud services is essential for this role.

How to Answer

Highlight specific projects where you utilized cloud platforms, focusing on the services you used and the outcomes achieved.

Example

“I have extensive experience with AWS, particularly with services like S3 for storage and Redshift for data warehousing. In my last project, I designed a data pipeline that ingested data from various sources into S3, processed it using AWS Lambda, and then loaded it into Redshift for analytics.”

3. What tools have you used for data orchestration?

This question assesses your knowledge of tools that manage data workflows.

How to Answer

Mention specific tools you have used, explaining their purpose and how they fit into your data engineering processes.

Example

“I have used Apache Airflow for orchestrating complex data workflows. It allows me to schedule and monitor data pipelines effectively, ensuring that tasks are executed in the correct order and that dependencies are managed properly.”

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

Data quality is critical in healthcare; your answer should reflect your approach to maintaining it.

How to Answer

Discuss methods you use to validate and cleanse data, as well as any tools that assist in this process.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Great Expectations to ensure data quality. Additionally, I perform regular audits and monitor data lineage to identify and rectify any issues promptly.”

5. Can you explain a challenging data engineering problem you faced and how you solved it?

This question evaluates your problem-solving skills and technical expertise.

How to Answer

Describe a specific challenge, the steps you took to address it, and the outcome.

Example

“In a previous role, I faced a challenge with slow data processing times due to inefficient queries. I analyzed the query performance and optimized the SQL statements, which reduced processing time by 50%. This improvement significantly enhanced our reporting capabilities.”

Data Processing and Architecture

1. What is a data lake, and how does it differ from a data warehouse?

Understanding the differences between these two storage solutions is vital for a Data Engineer.

How to Answer

Explain the characteristics of both data lakes and data warehouses, including their use cases.

Example

“A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. In contrast, a data warehouse is designed for structured data and is optimized for query performance. Data lakes are ideal for big data analytics, while data warehouses are better suited for business intelligence.”

2. Describe your experience with big data technologies like Hadoop or Spark.

Your familiarity with big data frameworks is essential for handling large datasets.

How to Answer

Discuss specific projects where you utilized these technologies, focusing on the challenges and results.

Example

“I have worked with Apache Spark for processing large datasets in real-time. In one project, I used Spark Streaming to process data from Kafka, which allowed us to analyze user behavior in real-time and make data-driven decisions quickly.”

3. How do you approach data modeling?

Data modeling is a key aspect of data engineering; your answer should reflect your methodology.

How to Answer

Explain your process for designing data models, including any tools or methodologies you use.

Example

“I start by gathering requirements from stakeholders to understand their data needs. Then, I create conceptual, logical, and physical data models using tools like ERwin. I ensure that the models are normalized to reduce redundancy while also considering performance for querying.”

4. What is your experience with RESTful APIs?

APIs are crucial for data integration; your familiarity with them is important.

How to Answer

Discuss your experience in designing or consuming APIs, focusing on their role in data engineering.

Example

“I have designed RESTful APIs to facilitate data exchange between our applications and external systems. I ensure that the APIs are well-documented and follow best practices for security and performance.”

5. How do you handle schema changes in your data pipelines?

Schema changes can disrupt data processing; your approach to managing them is critical.

How to Answer

Explain your strategy for handling schema evolution, including any tools or practices you employ.

Example

“I use schema evolution techniques, such as maintaining backward compatibility and using tools like Apache Avro for schema management. This allows me to adapt to changes without breaking existing data pipelines.”

Behavioral Questions

1. Describe a time when you had to work collaboratively with a team to achieve a goal.

Collaboration is key in data engineering; your answer should reflect your teamwork skills.

How to Answer

Share a specific example that highlights your role in the team and the outcome.

Example

“In my last project, I collaborated with data scientists and product managers to develop a new analytics feature. I facilitated regular meetings to ensure alignment and shared progress updates, which helped us deliver the feature ahead of schedule.”

2. How do you prioritize your tasks when working on multiple projects?

Time management is crucial in a fast-paced environment.

How to Answer

Discuss your approach to prioritization and any tools you use to manage your workload.

Example

“I prioritize tasks based on their impact and deadlines. I use project management tools like Jira to track progress and ensure that I’m focusing on high-priority items that align with business goals.”

3. Can you give an example of how you handled a conflict in the workplace?

Conflict resolution skills are important for maintaining a positive work environment.

How to Answer

Describe a specific situation, your approach to resolving it, and the outcome.

Example

“When a disagreement arose between team members about the best approach to a data pipeline, I facilitated a meeting where everyone could voice their opinions. By encouraging open communication, we reached a consensus on a solution that combined the best aspects of both proposals.”

4. What motivates you to work in data engineering, particularly in healthcare?

Your passion for the field can set you apart from other candidates.

How to Answer

Share your motivations and how they align with Humana’s mission.

Example

“I am passionate about using data to improve healthcare outcomes. Working at Humana allows me to contribute to meaningful projects that directly impact people’s lives, which is incredibly motivating for me.”

5. Why are you looking to change jobs?

This question assesses your career goals and fit for the company.

How to Answer

Be honest about your reasons while focusing on the positive aspects of the new opportunity.

Example

“I am looking for new challenges that align with my career goals. Humana’s commitment to leveraging data for better health outcomes resonates with me, and I believe my skills can contribute to that mission.”