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Data Engineers of Netflix?—?Interview with Samuel Setegne

The Netflix TechBlog

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

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Sustainability at AWS re:Invent 2022 All the talks and videos I could find…

Adrian Cockcroft

SUS209 — there was no talk with this code. STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager Data Engineering and Michael Floyd AWS Head of Sustainability Solutions. SUS304 to SUS311 No talks with these codes. Good example, well presented interesting new material.

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AI meets operations

O'Reilly

First, the behavior of an AI application depends on a model , which is built from source code and training data. A model isn’t source code, and it isn’t data; it’s an artifact built from the two. This means that, to have a history of how an application was developed, you have to look at more than the source code.

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Organise your engineering teams around the work by reteaming

Abhishek Tiwari

The engineering organisation described may not work for you because of a team of 8-10 people is still a very big overhead. In this model, software architecture and code ownership is a reflection of the organisational model. Is it possible to draw inspiration from outside of software engineering? Probably yes.

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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

Here we describe the role of Experimentation and A/B testing within the larger Data Science and Engineering organization at Netflix, including how our platform investments support running tests at scale while enabling innovation. Curious to learn about what it’s like to be a Data Engineer at Netflix?