Behavioral & Team Collaboration

The culture at Ancestry is highly collaborative, and the engineering teams pride themselves on being supportive and friendly. Your behavioral interviews are designed to assess how you handle team dynamics, prioritize work, and navigate challenges.

Be ready to go over:

• Conflict Resolution – Navigating technical disagreements with peers or stakeholders.
• Ownership – Taking responsibility for production incidents or project delays and driving the resolution.
• Adaptability – Shifting priorities when business needs change or when working with legacy systems.

Example scenarios:

• "Tell me about a time you had to work with a legacy pipeline that was poorly documented. How did you reverse-engineer it to make improvements?"
• "Describe a situation where you had to balance a tight product deadline with the need to build a highly optimized, clean data architecture."

As a Data Engineer at Ancestry, your primary responsibility is to design, develop, and maintain robust data pipelines that ingest and process massive, complex datasets. You will write clean, maintainable code in Python, Scala, or Java, leveraging distributed processing frameworks to transform raw historical records and genomic data into clean, structured datasets.

You will collaborate closely with cross-functional teams, including product managers, data scientists, and frontend engineers. Your work ensures that downstream users have reliable, high-performing access to the data they need to build consumer-facing features and run advanced analytics. You will also participate in architectural reviews, helping to define the future state of the data platform.

In addition to building new pipelines, you will be responsible for the operational health of the data infrastructure. This includes monitoring pipeline performance, optimizing resource utilization in the cloud to manage costs, and troubleshooting complex production issues. You will champion data quality, implementing automated testing and validation frameworks to ensure the integrity of the company's core data assets.

To be competitive for the Data Engineer position at Ancestry, you must demonstrate a strong blend of software engineering fundamentals and big data expertise.

• Must-have skills – Proficient in Python, Scala, or Java, with deep technical expertise in Apache Spark and SQL. Experience building production-grade ETL/ELT pipelines in a cloud environment (preferably AWS). Strong understanding of data warehousing, dimensional modeling, and distributed systems.
• Nice-to-have skills – Experience with orchestration tools like Apache Airflow, modern table formats like Delta Lake or Apache Iceberg, and containerization technologies like Docker and Kubernetes. Familiarity with managing genomics or DNA data is a significant plus.
• Experience level – Typically requires 3+ years of professional software or data engineering experience, with a proven track record of delivering scalable data solutions in production.
• Soft skills – Strong communication skills, a proactive attitude toward problem-solving, and a highly collaborative mindset that thrives in a team-oriented environment.

Q: What is the interview difficulty level for the Data Engineer role at Ancestry? A: Candidates generally describe the interview difficulty as average. While the technical standards are high—particularly around Spark and SQL—the interviewers are highly encouraging and do not expect you to know everything. They focus on your core engineering instincts and how you reason through problems.

Q: How much preparation time is typical for this interview process? A: Most successful candidates spend two to three weeks preparing. This allows enough time to review distributed systems concepts, practice SQL and coding problems, and structure behavioral stories using the STAR method.

Q: What is the hybrid or remote work policy for this role? A: Ancestry offers a hybrid work model for most engineering roles, with offices located in Lehi, UT, and San Francisco, CA. You should clarify the specific in-office expectations for your target team with your recruiter during your initial call.

Q: How long does the hiring process take from the initial screen to an offer? A: The timeline can vary, but many candidates report a relatively fast turnaround, sometimes receiving an offer within one to two weeks after completing the final round of interviews. However, keep in mind that coordination can occasionally introduce delays.

• Emphasize Practical Scale: When discussing your past projects, highlight the volume, velocity, and variety of the data you managed. Ancestry operates at a massive scale, so showing that you understand the operational realities of large-scale systems is crucial.
• Be Proactive with Communication: If you do not hear back within a reasonable window after an interview, proactively reach out to your recruiter. Keeping a polite, consistent line of communication ensures your application stays top-of-mind.

• Showcase Your Spark Optimizations: Since Spark is a core component of the technical screen, be ready to discuss specific instances where you successfully optimized a slow-running Spark job or resolved an out-of-memory issue in production.
• Align with the Mission: Ancestry is a mission-driven company focused on personal discovery. Expressing genuine interest in their product, scale, and the unique challenges of historical and genetic data can help you stand out.

Preparing for a Data Engineer interview at Ancestry is an exciting opportunity to showcase your technical expertise in distributed systems and cloud architecture. The company offers a unique environment where you can work on incredibly rich, scaled datasets that directly impact how millions of people connect with their past. By focusing your preparation on Apache Spark internals, SQL optimization, and collaborative problem-solving, you can position yourself as a strong candidate.

Remember that the interviewers at Ancestry are known for being exceptionally supportive and friendly. They want to see you succeed and are looking for engineers who can bring both technical rigor and a collaborative spirit to the team. Approach each round as a peer-to-peer technical discussion, and do not hesitate to ask clarifying questions to show your analytical depth.

To further refine your preparation, explore additional interview insights, community reviews, and real-world preparation resources on Dataford. Dedicating focused time to practicing these core domains will build the confidence you need to excel throughout the interview loop.

The salary range provided represents the base compensation for the Data Engineer position based in Lehi, UT. When evaluating an offer, keep in mind that total compensation at Ancestry typically includes base salary, performance bonuses, and a comprehensive benefits package. Your specific offer will depend on your experience level, technical depth, and performance throughout the interview process.