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DataEngineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “DataEngineers of Netflix” series, where our very own dataengineers talk about their journeys to DataEngineering @ Netflix. Kevin, what drew you to dataengineering?
A summary of sessions at the first DataEngineering Open Forum at Netflix on April 18th, 2024 The DataEngineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our dataengineering teams play a crucial role in this mission by enabling data-driven decision-making at scale.
At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with dataanalytics and dataengineering, we comprise the larger, centralized Data Science and Engineering group.
They require teams of dataengineers to spend months building complex data models and synthesizing the data before they can generate their first report. Data is automatically replicated across multiple Availability Zones for redundancy and also backed up to S3 for durability. How you can get started.
Curious to learn more about other Data Science and Engineering functions at Netflix? To learn about Analytics and Viz Engineering, have a look at Analytics at Netflix: Who We Are and What We Do by Molly Jackman & Meghana Reddy and How Our Paths Brought Us to Data and Netflix by Julie Beckley & Chris Pham.
This article is the last in a multi-part series sharing a breadth of AnalyticsEngineering work at Netflix, recently presented as part of our annual internal AnalyticsEngineering conference. Its easier to develop and maintain, and tends to be more familiar for analyticsengineers, data scientists, and dataengineers.
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