Netflix Data Engineer Interview Guide
Updated by Netflix candidates
Written by Graham Carlson, Senior Technical ContributorThis guide was written with the help of data engineer interviewers at Netflix.
tl;dr
While most think of Netflix as a video streaming company, its footprint and impact are much larger than a single platform. Since its move to streaming in 2007, Netflix has significantly expanded its offerings into different aspects of the entertainment business, including movie and show production, games, and advertising. Its global reach belies its size—Netflix has a much smaller headcount than its FAANG peers, with a staff hiring and retention philosophy that prioritizes experience and domain expertise over volume.
Netflix’s status as the most popular streaming platform in the world is due in no small part to its investment in highly performant data tools and analysis. Netflix is a leader in data-driven decision-making, and has built a number of powerful data engineering processes to conduct market research, track operational uptime, measure performance, and other mission-critical processes. Their ability to efficiently collect, ingest, process, and analyze 300 million subscribers’-worth of data is a key driver of their market dominance.
Netflix has also invested significantly in defining its company culture, and has become adept at hiring and cultivating this culture across the organization. An organization that focuses on autonomy rather than process and rules, Netflix encourages team members to collaborate and iterate in inventive ways. If you’re a data engineer who thrives when given space to experiment and who values giving and receiving constructive criticism, Netflix might be a good place for you.
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What does a Netflix Data Engineer do?
Data engineers at Netflix play a crucial role in the company’s data supply chain, creating infrastructure to deliver insights to key stakeholders throughout the organization. Data engineer roles are assigned a particular product or element of Netflix’s business and operations, building systems to gather and shift data where it needs to go. Some example teams or projects you might work on include:
- Content performance
- Security
- Advertising
- Growth
- Games
- Finance
The role can change significantly depending on the team you work on, but in general, data engineers are responsible for building and monitoring data ingestion and storage tools that work with existing Netflix tooling. Many of your projects will be collaborative and require you to work across teams and disciplines with other stakeholders. For example, your work on content performance might involve collaboration with Netflix’s platform team, development studios, or marketing department.
No matter the project you’re working on, you’ll be expected to be enthusiastic about collaborating and explaining your work. Because Netflix already has a highly performant data warehouse with a significant set of open source and internal tools, you’ll also be focused on building repeatable workflows that can be used by other data engineers for other projects, rather than one-off solutions for discrete tasks. As you develop solutions, you might be asked to explain your work and process, and to be open to constructive criticism from your coworkers.
Before you apply
- Research and explore Netflix’s data engineering projects: Netflix's technical work is well documented in blog posts, videos, and other publicly available sources, including their data engineering projects. While you may not find content geared towards the team you’re applying to, you can still use these resources to understand the overall data engineering philosophy Netflix prefers, and get a feel for some of the internal tools they’ve built—such as go/boost—and about the tools they used to create their centralized data warehouse.
- Spend some time learning about Netflix’s culture: Their culture is very well-documented, as Netflix has been more than willing to carefully define and promote the kind of approach to work they feel functions best at their organization. In addition to helping you understand the sort of traits they value most in their employees, reviewing Netflix culture documents (older version, debatably more relevant than the newer version) can give you a significant boost during interviews, as they sometimes ask questions directly related to one or all of the cultural values.
- Practice your interviewing skills: Even if you’re a seasoned data engineer with an impressive background, Netflix’s interview process can be very challenging. Particularly their behavioral and system design rounds. Go through some mock interviews with an expert to identify key points for improvement and prepare to tackle the real thing with confidence.
Interview process
Netflix initially relied on a fairly loose set of organizational interview guidelines, which allowed their teams to come up with questions and lead the interview process. However, they’ve made some significant changes in the last few years, centralizing elements of the process as they’ve grown. Here’s the process as it stands currently:
- Recruiter screen
- General technical screen
Onsite, which contains:
- Coding test
- System design assessment
- Behavioral interviews
1. Recruiter Screening
The recruiter screening will take place over the phone, and the conversation will allow you to speak to your experience, goals, and understanding of Netflix’s culture. The interviewer will draw from a bank of questions, and while it’s important that you have an understanding of the team you’re applying to, these questions are likely to be more general and focused on your experience as a data engineer.
Some questions they might ask include:
Asked at 2. Technical screening round
During the technical screening round, the questions are very specific to the work the team you are applying to does. This round will focus on a team-specific problem, and will be both conversational and hands-on, allowing you to demonstrate your coding skills as well as your ability to think about the unique problems Netflix’s data org might take on.
The questions generally start with simple exercises—you’ll be given a dataset or problem and asked to handle some basic SQL queries to extract insights. The interview will ramp up after the early steps, focusing on big data management, building a data pipeline that can ingest large amounts of data, and other questions related to scale.
For example, if you’re applying to work in the studio metrics team, a question during the technical screening round might focus on how you build a data pipeline to ingest, validate, and process viewership data so the team can learn what content resonates best with a particular audience. You’d be expected to build a repeatable and efficient solution that works with Netflix's data warehouse tooling, particularly Apache Iceberg and Apache Spark.
Some questions you might get in this round include:
- Design a data pipeline to track viewership data.
Asked at Netflix • Asked at Netflix Onsite rounds:
3. Coding skills assessment
The coding skills assessment includes a more involved and challenging set of questions, and will be primarily used to determine your skills in extracting data using SQL. These questions will relate to something the team does regularly, or a current problem they are facing. Like most data assessments, you’ll be given a table or tables of data and asked to write queries to draw particular insights from the data.
Some queries you might see would be:
- Determine the top ten most popular shows by number of viewers.
- Write queries to help the content team determine which genres are most popular in a particular region.
- Determine how many new users signed up over a given period of time.
- How would you determine which shows get watched together most frequently?
While some interviewers may allow you to use another language or framework to solve these problems, many will expect you to be able to extract and process data using SQL.
4. System design round
Netflix has a highly performant data gathering and analysis system, one designed to handle the enormous scale of data that comes with being the world’s most popular streaming platform. As a result, they put a major emphasis not only on your ability to think through system design, but to build solutions that work within their process and extant data services architecture, and can operate at scale.
Review and strengthen your system design skills using Exponent’s System Design Interview Rubric. This can help you create a plan, consider the advantages and drawbacks of different approaches, and come up with a solution that stands out.
Because you likely won’t already be familiar with their architecture, the system design round won’t necessarily require you to build a solution that works with their existing data warehouse or other tools. However, you will be tested on your ability to build data pipelines and storage solutions that can handle large amounts of data, and that utilize cloud-native and distributed tools. As with other rounds, this will focus on a current or common problem your team deals with.
If you’re applying to work on the platform team, you might be asked to create an ETL pipeline to process new user signup data, designing an ingestion and data storage schema that ensures data freshness while offering efficiency at scale. For more advanced data engineer roles, you might be asked follow-up questions about your approach—for example, how you would handle expanding this system to another region or managing data governance requirements based on user location.
Another key component of Netflix’s approach is using microservices to handle scale. The sheer size and amount of data at Netflix means they favor a decentralized and modular set of tools over a monolithic solution. The interviewers will want to see you apply this to your solution, designing something with a great deal of structural flexibility, optimized to use only necessary computational resources.
Because system design is such a major point of emphasis, interviewer(s) will assess you based not just on your design, but your ability to share your thinking and process, to ask pertinent questions, and to defend your work if need be. Even the ideal solution might not be enough if you’re reluctant or unable to explain your thinking.
Some system design questions you might encounter are:
Asked at Amazon, DoorDash, Salesforce • 5. Behavioral interview(s)
Other than system design, another element of the interview process that Netflix emphasizes is the behavioral round. Netflix prizes a particular approach to work and collaboration, more than many other companies of their size and importance do. Their approach is autonomous and demanding, and their extensive cultural documentation describes their organization as a “dream team” that prioritizes results. Because they focus on hiring experienced and talented team members, each one is expected to produce at a high level in their position.
Netflix has a list of values (you can see in their culture decks) they consider important, but for pretty much every role, they expect you to be highly communicative and collaborative, offer constructive criticism, and boldly defend your approach—when you believe it’s the best one. From the beginning, Netflix has avoided creating an overly process-driven culture, instead betting on the expertise and drive of their teams to find the best way to solve a problem or build a new feature. Although this is changing a little now that they’ve reached a certain size, their behavioral interviews still focus on these characteristics.
You’ll be asked to go through various parts of your career, talking about times when you did things that exemplify Netflix’s culture. You’ll also be asked about their culture documents (old version & new version), which makes it essential that you review them ahead of time and identify some key ways that you resemble the kind of team member they prize. Discussing examples of times when you benefited from autonomy or when you successfully defended and advocated for a certain approach are good ways to show this. They also want to hear about how you handle making difficult decisions—the process you go through and how you weigh your options and ultimately reach a decision, whether or not it was the right one at the time.
Some questions you might hear in this round:
Asked at Netflix Asked at Netflix Additional resources
- Netflix Data Engineering Blog
- The Netflix Data Engineering Stack
- Get a Job at Netflix: Interview Process and Top Questions
- Data Engineering Interview Course
- Top Data Engineering Interview Questions
- Top Netflix Interview Questions
FAQs about the Netflix Data Engineer interview
How should I prepare for a Netflix Data Engineer interview?
You can start by brushing up on your data engineering skills, particularly around SQL, but also Scala and Python, which are used at Netflix. You should also go through the Netflix Data Engineering blog and their video content, where they explain the schema and architecture of data flow at Netflix. Ensuring that you have a good understanding of Netflix’s cultural values is also critical, as you will be asked about it. At least review their infamous culture memo from 2009.
How much do Netflix Data Engineers make?
According to Levels, data engineers at Netflix make:
- L4: $363,000
- L5: $567,000
- L6: $787,000
How long is the Netflix Data Engineer interview process?
The exact time will depend on team availability and other factors, but the interview process typically takes between 3–4 weeks.
Can you reapply to Netflix after rejection?
Netflix allows applicants to reapply 6–12 months after a rejection. In the time between applications, consider ways to level up your skills and make yourself a desirable candidate for your new prospective role.
Does Netflix offer internships?
Netflix offers an internship program that typically lasts 12 weeks, timed with the summer vacation period.
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