Natera Data Engineer Interview Questions + Guide in 2025

Natera is a global leader in cell-free DNA testing, specializing in oncology, women's health, and organ health, dedicated to empowering personalized healthcare decisions through innovative technology.

As a Data Engineer at Natera, you will be integral in building and maintaining the infrastructure that supports data-driven healthcare solutions. Your key responsibilities will include designing and implementing data pipelines, ensuring data quality and integrity, and collaborating with cross-functional teams to translate business requirements into technical solutions. You will leverage cloud-native tools and services, particularly from AWS and GCP, to create efficient ETL processes and data warehousing solutions that facilitate real-time analytics. A strong foundation in SQL, as well as familiarity with machine learning frameworks, is essential for success in this role. You will also be expected to implement best practices in AIOps and MLOps, optimizing data operations and model deployments.

Candidates who thrive in this position typically possess strong analytical and problem-solving skills, demonstrate excellent communication abilities, and have a passion for improving healthcare through technology. Understanding Natera's commitment to innovation and collaboration will be crucial in aligning your approach to the company’s mission.

This guide will equip you with the knowledge and insights needed to excel in your interview, helping you demonstrate your fit for the Data Engineer role at Natera.

The interview process for a Data Engineer role at Natera is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different aspects of your qualifications and alignment with Natera's mission.

1. Initial HR Screening

The first step in the interview process is an initial screening conducted by an HR representative. This 30-minute conversation focuses on understanding your interest in Natera, your career aspirations, and how your background aligns with the company's values and culture. Be prepared to discuss your motivations for applying and what you know about Natera's work in cell-free DNA testing.

2. Technical Interview with Hiring Manager

Following the HR screening, candidates will have a technical interview with the hiring manager. This round is more focused on your technical knowledge and experience. Expect high-level questions regarding your familiarity with various technologies relevant to data engineering, such as cloud services, data warehousing, and ETL processes. While explicit coding challenges may not be presented, you should be ready to discuss your past projects and how you approached technical challenges.

3. Technical Assessment Rounds

The final stage of the interview process consists of multiple technical assessment rounds, typically involving two or three interviews. These sessions will delve deeper into your technical skills, particularly in SQL and data manipulation. You may be asked to solve practical problems, such as writing SQL queries to perform specific operations or calculating metrics based on given datasets. Additionally, expect discussions around data architecture, data quality, and integration processes, as well as your experience with cloud platforms and data visualization tools.

As you prepare for these interviews, it's essential to familiarize yourself with the specific technologies and methodologies mentioned in the job description, as well as to reflect on your past experiences that demonstrate your problem-solving abilities and technical acumen.

Next, let's explore the types of questions you might encounter during the interview process.

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

Understand Natera's Mission and Values

Natera is dedicated to transforming healthcare through innovative genetic testing. Familiarize yourself with their mission to empower personalized healthcare decisions and how they leverage technology in oncology, women's health, and organ health. This understanding will not only help you align your answers with the company’s goals but also demonstrate your genuine interest in contributing to their mission.

Prepare for Technical Proficiency

As a Data Engineer, you will be expected to showcase your technical skills, particularly in SQL and cloud services like AWS and GCP. Review common SQL operations and be prepared to discuss your experience with data warehousing, ETL processes, and data quality assurance. Practice articulating your thought process when solving technical problems, as this will be crucial during the technical interviews.

Emphasize Collaboration and Communication

Natera values teamwork and cross-functional collaboration. Be ready to discuss your experience working with diverse teams, including data scientists and business stakeholders. Highlight specific examples where your communication skills facilitated project success or resolved conflicts. This will demonstrate your ability to thrive in Natera's collaborative environment.

Showcase Your Problem-Solving Skills

During the interview, you may encounter scenario-based questions that assess your analytical and problem-solving abilities. Prepare to discuss past challenges you faced in data architecture or engineering and how you approached them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your solutions.

Familiarize Yourself with AIOps and MLOps

Given Natera's focus on AIOps and MLOps, it’s essential to understand these concepts and their relevance to data engineering. Be prepared to discuss how you have implemented best practices in these areas, including automation and orchestration of AI/ML operations. This knowledge will set you apart as a candidate who is not only technically proficient but also aligned with Natera's innovative approach.

Prepare for Behavioral Questions

Expect behavioral questions that explore your adaptability, resilience, and cultural fit within Natera. Reflect on your past experiences and how they align with Natera's commitment to diversity and inclusion. Be ready to share stories that illustrate your values and how they resonate with the company’s culture.

Ask Insightful Questions

At the end of your interview, you will likely have the opportunity to ask questions. Use this time to inquire about Natera's future projects, team dynamics, or how they measure success in the Data Engineering team. Thoughtful questions will demonstrate your enthusiasm for the role and your desire to contribute meaningfully to the organization.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Natera. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data architecture and integration. Be prepared to discuss your familiarity with cloud services, data warehousing, ETL processes, and your approach to ensuring data quality and security.

Technical Skills

1. Can you explain the ETL process and its importance in data engineering?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is fundamental to data integration and management.

How to Answer

Discuss the steps involved in ETL, emphasizing how each step contributes to data quality and accessibility for analysis.

Example

“The ETL process involves extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse. This process is vital as it ensures that data is clean, consistent, and readily available for analysis, which is essential for making informed business decisions.”

2. Describe your experience with cloud services, particularly AWS or GCP.

Cloud services are integral to modern data engineering, and familiarity with these platforms is often a requirement.

How to Answer

Highlight specific services you have used, such as S3 for storage or BigQuery for data analysis, and how they contributed to your projects.

Example

“I have extensive experience with AWS, particularly using S3 for data storage and Redshift for data warehousing. In my last project, I utilized these services to create a scalable data pipeline that improved data retrieval times by 30%.”

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

Data quality is paramount in data engineering, and interviewers will want to know your strategies for maintaining it.

How to Answer

Discuss specific techniques or tools you use to validate and clean data, as well as any monitoring processes you have in place.

Example

“I implement data validation checks at various stages of the ETL process, using tools like Apache Airflow to monitor data quality. Additionally, I regularly conduct audits to ensure that the data remains accurate and reliable over time.”

4. Can you explain the concept of data modeling and its significance?

Data modeling is a key aspect of data architecture, and understanding it is essential for a Data Engineer.

How to Answer

Define data modeling and discuss its role in structuring data for efficient storage and retrieval.

Example

“Data modeling involves creating a visual representation of data structures and relationships. It is significant because it helps in designing databases that optimize performance and ensure that data can be easily accessed and analyzed.”

5. What is your experience with containerization technologies like Docker?

Containerization is increasingly important in data engineering for deploying applications and managing environments.

How to Answer

Share your experience with Docker or similar technologies, focusing on how they have improved your workflow.

Example

“I have used Docker to containerize applications, which has streamlined the deployment process and ensured consistency across different environments. This approach has reduced deployment times by 40% in my previous projects.”

SQL and Data Manipulation

1. How would you write a SQL query to calculate monthly churn from user registration data?

SQL proficiency is essential for data manipulation and analysis, and this question tests your practical skills.

How to Answer

Outline the logic of your query, focusing on how you would aggregate and filter the data to derive the churn rate.

Example

“To calculate monthly churn, I would first identify the number of users who registered in a given month and then count how many of those users were no longer active in the following month. The SQL query would involve using COUNT and GROUP BY functions to aggregate the data accordingly.”

2. Can you describe a complex SQL query you have written and its purpose?

This question assesses your ability to handle complex data scenarios using SQL.

How to Answer

Provide a specific example of a complex query, explaining the context and the outcome it achieved.

Example

“I once wrote a complex SQL query to join multiple tables and calculate the average order value per customer segment. This involved using subqueries and window functions to ensure accurate calculations, which ultimately helped the marketing team tailor their strategies effectively.”

3. What strategies do you use to optimize SQL queries for performance?

Performance optimization is critical in data engineering, and interviewers will want to know your approach.

How to Answer

Discuss techniques such as indexing, query restructuring, or using appropriate data types to enhance performance.

Example

“I optimize SQL queries by analyzing execution plans to identify bottlenecks. I often use indexing on frequently queried columns and rewrite queries to minimize the number of joins, which has significantly improved query performance in my previous roles.”

4. How do you handle missing or inconsistent data in your datasets?

Data quality issues are common, and your approach to handling them is crucial.

How to Answer

Explain your methods for identifying and addressing missing or inconsistent data, including any tools or techniques you use.

Example

“I handle missing data by first assessing the extent of the issue. I may use imputation techniques for small gaps or remove records if the missing data is significant. For inconsistent data, I implement validation rules during the ETL process to catch errors early.”

5. Can you explain the difference between a data lake and a data warehouse?

Understanding the distinction between these two concepts is important for data architecture.

How to Answer

Define both terms and discuss their use cases in data management.

Example

“A data lake is a storage repository that holds vast amounts of raw data in its native format, while a data warehouse is a structured storage solution optimized for analysis. Data lakes are ideal for big data and unstructured data, whereas data warehouses are better suited for structured data and complex queries.”