Architecture Data Engineering Innovation Technology 
The Netflix TechBlog
JUNE 1, 2021
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?
Abhishek Tiwari
FEBRUARY 26, 2020
The evolution of your technology architecture should depend on the size, culture, and skill set of your engineering organization. There are no hard-and-fast rules to figure out interdependency between technology architecture and engineering organization but below is what I think can really work well for product startup.
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O'Reilly
FEBRUARY 18, 2020
It’s also the data source for our annual usage study, which examines the most-used topics and the top search terms. [1]. This combination of usage and search affords a contextual view that encompasses not only the tools, techniques, and technologies that members are actively using, but also the areas they’re gathering information about.
The Netflix TechBlog
JANUARY 11, 2022
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?
The Netflix TechBlog
MARCH 25, 2019
We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.
Adrian Cockcroft
FEBRUARY 13, 2023
STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager Data Engineering and Michael Floyd AWS Head of Sustainability Solutions. SUS302 Optimizing architectures for sustainability — Katja Philipp AWS SA and Szymon Kochanski AWS SA. SUS209 — there was no talk with this code.
All Things Distributed
NOVEMBER 15, 2016
The reality is that many traditional BI solutions are built on top of legacy desktop and on-premises architectures that are decades old. They require teams of data engineers to spend months building complex data models and synthesizing the data before they can generate their first report. Powered by Innovation.
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