Ancestry Data Engineer Interview Questions + Guide in 2025

Ancestry is a human-centered company dedicated to helping individuals discover their family histories and enrich their lives through personal stories.

As a Data Engineer at Ancestry, you will play a crucial role in building and maintaining the infrastructure necessary to support the company’s extensive data needs. Key responsibilities include developing and optimizing data pipelines, ensuring data quality, and implementing data storage solutions that align with Ancestry's mission of providing insights into family connections. Proficiency in SQL is essential, as it will be a primary tool in handling large datasets and performing data transformations. Familiarity with object-oriented programming, particularly in languages like Python or Java, is also important for writing efficient code that supports data operations.

A successful Data Engineer at Ancestry will possess strong analytical skills and a deep understanding of data architecture, as well as a passion for leveraging data to improve user experiences. The ideal candidate should be comfortable working with cloud environments, particularly AWS, and have experience with data engineering tools such as Spark and Airflow. You’ll thrive in this role if you embrace collaboration, are open to learning, and are committed to building solutions that empower Ancestry’s users to explore their heritage.

This guide will help you prepare for your interview by providing a clear understanding of the role's expectations and the skills that will be evaluated.

The interview process for a Data Engineer role at Ancestry is designed to assess both technical skills and cultural fit within the company. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Screening

The process begins with an initial screening, which is usually a 30-minute phone interview with a recruiter. During this conversation, the recruiter will discuss the role, the company culture, and your background. This is an opportunity for you to showcase your passion for data engineering and how your experiences align with Ancestry's mission of enriching people's lives through data.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview. This interview may be conducted via video call and will focus on your proficiency in key technical areas such as SQL, object-oriented programming, and possibly some algorithms. Expect to solve practical problems and demonstrate your understanding of data engineering concepts. The interviewers are known to be supportive, providing guidance as needed, which helps create a less stressful environment.

3. Onsite Interview

The final stage usually involves an onsite interview, which may consist of multiple rounds with various team members, including hiring managers and engineers. Each round will delve deeper into your technical skills, including your experience with data preparation, infrastructure as code (IAC), and familiarity with cloud services like AWS. Additionally, you may encounter behavioral questions aimed at assessing your teamwork and problem-solving abilities. This stage is crucial for determining how well you would fit into Ancestry's collaborative and inclusive work culture.

As you prepare for these interviews, it's essential to be ready for a mix of technical challenges and discussions about your past experiences and how they relate to the role. Now, let's explore the specific interview questions that candidates have encountered during this process.

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

Embrace the Company Culture

Ancestry values a human-centered approach, where every individual's story matters. Familiarize yourself with the company's mission and values, and be prepared to discuss how your personal values align with theirs. Show genuine enthusiasm for the work they do in helping people discover their family histories. This will not only demonstrate your interest in the role but also your fit within their inclusive and diverse culture.

Prepare for Technical Proficiency

As a Data Engineer, you will need to showcase your skills in SQL and object-oriented programming. Brush up on your SQL knowledge, focusing on complex queries, data manipulation, and optimization techniques. Additionally, be ready to discuss your experience with programming languages like Python or Java, as well as any familiarity with ML frameworks such as PyTorch or TensorFlow. Practice coding challenges that reflect real-world scenarios you might encounter in the role.

Be Ready for Collaborative Problem-Solving

During the interview, you may encounter questions that assess your ability to work collaboratively. Ancestry's interviewers are known for being supportive and helpful, so approach these questions with a mindset of teamwork. Share examples from your past experiences where you successfully collaborated with others to solve complex problems. Highlight your communication skills and your ability to adapt to different team dynamics.

Showcase Your Passion for Data Engineering

Express your enthusiasm for data engineering and how it can impact Ancestry's mission. Discuss any relevant projects or experiences that demonstrate your ability to develop, deploy, and support data infrastructure. If you have experience with tools like Spark or Airflow, be sure to mention it, as these are valuable in the data engineering space. Your passion and knowledge will set you apart from other candidates.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers that reflect your interest in the role and the company. Inquire about the challenges the ML Platform team is currently facing or how they envision the future of data engineering at Ancestry. This not only shows your engagement but also helps you assess if the company and role align with your career goals.

Follow Up with Gratitude

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Mention specific aspects of the conversation that resonated with you, reinforcing your interest in the role. This small gesture can leave a lasting impression and demonstrate your professionalism.

By following these tips, you will be well-prepared to make a strong impression during your interview at Ancestry. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Ancestry. The interview will likely focus on your technical skills, particularly in SQL, object-oriented programming, and data engineering concepts. Be prepared to demonstrate your understanding of data pipelines, infrastructure as code, and your experience with relevant programming languages and frameworks.

Technical Skills

1. Can you explain the differences between SQL and NoSQL databases?

Understanding the strengths and weaknesses of different database types is crucial for a Data Engineer.

How to Answer

Discuss the characteristics of SQL databases, such as structured data and ACID compliance, versus NoSQL databases, which are more flexible and can handle unstructured data.

Example

“SQL databases are ideal for structured data and complex queries, ensuring data integrity through ACID properties. In contrast, NoSQL databases excel in handling large volumes of unstructured data and offer greater flexibility in data modeling, making them suitable for applications that require rapid scaling.”

2. Describe a data pipeline you have built. What were the key components?

This question assesses your practical experience in data engineering.

How to Answer

Outline the steps you took to design and implement the pipeline, including data sources, transformation processes, and storage solutions.

Example

“I built a data pipeline that ingested data from various APIs, transformed it using Apache Spark, and stored it in a PostgreSQL database. The pipeline included error handling and logging mechanisms to ensure data quality and reliability.”

3. How do you ensure data quality in your ETL processes?

Data quality is critical in data engineering, and interviewers want to know your approach.

How to Answer

Discuss the methods you use to validate and clean data during the extraction, transformation, and loading phases.

Example

“I implement data validation checks at each stage of the ETL process, such as schema validation and duplicate detection. Additionally, I use logging to track data anomalies and set up alerts for any discrepancies.”

4. What is Infrastructure as Code (IaC), and how have you used it?

Understanding IaC is essential for modern data engineering roles, especially in cloud environments.

How to Answer

Explain the concept of IaC and provide examples of tools you have used to implement it.

Example

“Infrastructure as Code allows us to manage and provision computing resources through code rather than manual processes. I have used Terraform to define and deploy AWS resources, ensuring consistency and repeatability in our infrastructure setup.”

5. Can you discuss your experience with cloud platforms, particularly AWS?

Cloud computing is a significant aspect of data engineering, and familiarity with AWS is often required.

How to Answer

Share your experience with AWS services relevant to data engineering, such as S3, EC2, and RDS.

Example

“I have extensive experience using AWS, particularly with S3 for data storage and EC2 for running data processing jobs. I also utilize AWS Lambda for serverless computing to trigger data processing workflows based on events.”

Programming and Frameworks

1. What are the key principles of object-oriented programming?

A solid understanding of OOP is essential for a Data Engineer.

How to Answer

Discuss the main principles of OOP, such as encapsulation, inheritance, and polymorphism.

Example

“The key principles of object-oriented programming include encapsulation, which restricts access to certain components; inheritance, allowing new classes to inherit properties from existing ones; and polymorphism, enabling methods to do different things based on the object it is acting upon.”

2. How do you handle version control in your projects?

Version control is vital for collaboration and maintaining code integrity.

How to Answer

Explain your experience with version control systems, particularly Git, and how you manage branches and merges.

Example

“I use Git for version control, creating branches for new features or bug fixes. I regularly commit changes with clear messages and use pull requests to facilitate code reviews before merging into the main branch.”

3. Can you explain how you would optimize a slow-running SQL query?

Performance optimization is a key skill for a Data Engineer.

How to Answer

Discuss techniques you would use to analyze and improve query performance.

Example

“To optimize a slow-running SQL query, I would first analyze the execution plan to identify bottlenecks. Then, I would consider adding indexes, rewriting the query for efficiency, or breaking it into smaller, more manageable parts.”

4. What experience do you have with data processing frameworks like Apache Spark?

Familiarity with data processing frameworks is often required for data engineering roles.

How to Answer

Share your experience with Spark, including any specific projects or use cases.

Example

“I have used Apache Spark for large-scale data processing tasks, such as aggregating and transforming data from multiple sources. Its ability to handle distributed data processing has significantly improved the performance of our data workflows.”

5. Describe a challenging technical problem you faced and how you solved it.

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

How to Answer

Provide a specific example of a technical challenge, the steps you took to resolve it, and the outcome.

Example

“I faced a challenge with data inconsistency in our ETL process, which was causing discrepancies in our reports. I conducted a thorough investigation, identified the root cause as a timing issue in data ingestion, and implemented a more robust scheduling mechanism to ensure data was processed in the correct order.”