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Data Engineers of Netflix?—?Interview with Kevin Wylie

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “Data Engineers of Netflix” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Kevin, what drew you to data engineering?

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A Recap of the Data Engineering Open Forum at Netflix

The Netflix TechBlog

A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale.

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A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix

The Netflix TechBlog

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

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Spice up your Analytics: Amazon QuickSight Now Generally Available in N. Virginia, Oregon, and Ireland.

All Things Distributed

They require teams of data engineers 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.

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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

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.

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Part 3: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

This article is the last in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Its easier to develop and maintain, and tends to be more familiar for analytics engineers, data scientists, and data engineers.

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