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Demystifying Interviewing for Backend Engineers @ Netflix

The Netflix TechBlog

For many roles, you will be given a choice between a take-home coding exercise or a one-hour discussion with one of the engineers from the team. The interview panel consists of two or three engineers, a hiring manager and a recruiter. The problems you are asked to solve are related to the work of the team.

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A Day in the Life of… a Software Training Specialist

Tasktop

I Also like to spend a little bit of time stretching and doing light exercises, reading or playing with my daughter before I dive into some work. I comb through our amazing User Guide (thanks to the Rebbeca Dobbin from the Product Development team!) My morning is not complete without my daily walk/jog for about half an hour.

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What Am I Working On (RDORR): SQL Server On Linux

SQL Server According to Bob

Before joining Microsoft in 1994 I worked at State Farm Insurance and spent several years in a testing group and later joined a team developing applications against SQL Server for OS/2. We developed a friendly competition to break one another’s code. For example, we were designing how to capture a crash dump.

Servers 40
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Automating chaos experiments in production

The Morning Paper

This is a fascinating paper from members of Netflix’s Resilience Engineering team describing their chaos engineering initiatives: automated controlled experiments designed to verify hypotheses about how the system should behave under gray failure conditions, and to probe for and flush out any weaknesses. Safeguards.

Latency 77
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Teaching rigorous distributed systems with efficient model checking

The Morning Paper

It describes the labs environment, DSLabs , developed at the University of Washington to accompany a course in distributed systems. Hence the DSLabs framework integrates model checking into a holistic distributed systems development environment (based on Java as the implementation language – it would be neat to see a Rust version!).

Systems 43
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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

mainly because of mundane reasons related to software engineering. The infrastructure should allow them to exercise their freedom as data scientists but it should provide enough guardrails and scaffolding, so they don’t have to worry about software architecture too much. It leverages elasticity of the cloud by design?—?both

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MLOps and DevOps: Why Data Makes It Different

O'Reilly

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. In effect, the engineer designs and builds the world wherein the software operates. This approach is not novel.

DevOps 138