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Who are we?
Netflix is the world's leading streaming entertainment service with 195+ million paid memberships in over 190 countries enjoying TV series, documentaries and feature films across a wide variety of genres and languages. Netflix is reinventing entertainment from end to end. We are revolutionizing how shows and movies are produced, pushing technological boundaries to efficiently deliver streaming video at massive scale over the internet, and continuously improving and personalizing how content is presented around the globe.
The Data Science and Engineering (DSE) organization at Netflix exists to improve the use of data and analytic methodologies to improve key aspects of our global business. The Analytics Engineers within DSE are key partners to business leaders and decision makers who are making decisions to grow our member base and bring the most joy to our subscribers via compelling content, personalized product interactions, and a stellar customer experience.
Analytics Engineers partner in a wide range of ways with the business, including dataset preparation, analytic tool building, exploratory analytics, and authoring memos with analytic and experimentation findings. Our Analytics Engineers flourish through flexibility and nimbleness, and their work varies across business areas based on changing goals and needs. These roles are extremely impactful due to their high level of partnership and flexibility in approach, and provide opportunities to gain experience with our large-scale technologies as well as participate in the Netflix culture.
What will you learn?
- Collaborate with stunning colleagues on business facing projects, with business partners
- Collaborate with other roles in Data Science & Engineering to accomplish projects
- Perform exploratory data analytics and summarize findings and any recommendations in a memo
- Build interactive analytic tools to improve efficiency of access to business insights
- Build analytic datasets using cloud data technologies such as Presto and SparkSQL within our code-centric big data platform
- Collaborate on designing components of analytical data models
Who are you?
- Curious and motivated learner - completing a Master’s level degree in Business Analytics, Economics, Statistics, Computer Science, Engineering, or Mathematics
- Clear communicator - you are concise and articulate in speech and writing
- Able to manipulate and evaluate data with SQL to perform business analysis
- Experience with data analysis and analytics tools such as Tableau, PowerBI, Qlik, Looker, etc.
- Practical knowledge of Python for analytics or visualization (Numpy, Pandas, Plotly, Dash)
- Coursework or some experience with data structures and dimensional data modeling, data warehouse architectures, and data preparation (ETL)
A sample interview loop might include:
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Recruiter screening call or meeting. The recruiter screens for general role fit, and fact-finds to prepare the rest of the loop. (This could instead be done by a hiring manager, especially if the company doesn’t have recruiters.)
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Technical phone screen. A phone conversation tests for core skills, typically basic programming or technical concepts. (Some companies may conduct two phone screens to gather more signal, or so a second interviewer can offer perspective.)
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Take-home evaluation. This is a variant of the basic skills screen that may take the form of an online challenge or simpler take-home test. (Many companies skip this.)
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Onsite interviews. These typically extend half or most of a day and include three to six interviews in several formats, covering:
a. in-person coding questions
b. non-coding technical questions
c. behavioral questions
d. wrap-up conversation with the hiring manager that includes questions, concerns, or loose ends, and sets expectations on next steps
e. some kind of social event, like lunch with the team.
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Interviewer feedback. Each interviewer offers written feedback on the candidate, and/or discussion among the interviewing panel.
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Post-interview follow-ups. Calls, meetings, and possibly second onsite visits allow the hiring team to assess anything not yet covered or to gather more signal on something interviewers disagree about.
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Reference checks. The hiring manager or interviewers call past employers and colleagues to verify aspects of the candidate’s experience.
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Decision. The company gives either a rejection or an offer. An offer leads to an acceptance or rejection by the candidate.
