The Netflix TechBlog
JULY 15, 2021
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?
O'Reilly
FEBRUARY 18, 2020
It’s the single most popular programming language on O’Reilly, and it accounts for 10% of all usage. This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. In programming, Python is preeminent. Figure 3 (above).
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The Netflix TechBlog
JANUARY 11, 2022
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. Curious to learn about what it’s like to be a Data Engineer at Netflix?
Dynatrace
JULY 6, 2022
While automating IT practices can save administrators a lot of time, without AIOps, the system is only as intelligent as the humans who program it. This requires significant data engineering efforts, as well as work to build machine-learning models. Monitoring automation is ongoing.
The Netflix TechBlog
JUNE 6, 2022
This will be our “Appsec Reviews and Assessments” function and we are hiring for passionate, early career Appsec engineers to join this group. We will continue to learn as we go through this next phase of evolution of our program. Our focus has been on improving overall security assurance as opposed to just vulnerability prevention.
The Netflix TechBlog
MARCH 2, 2021
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.
The Agile Manager
JULY 31, 2022
There are shadow IT teams of developers or data engineers that spring up in areas like operations or marketing because the captive IT function is slow, if not outright incapable, of responding to internal customer demand. There are also shadow activities of large software delivery programs. But scale posed a challenge.
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