Свою "карьеру" я начинал с Питона (первые шаги в программировании), но за время работы в Амазон (мой первый промышленный опыт) я писал много на чем, но не так то много на Питоне. Был опыт с Java, Kotlin, Typescript, Javascript, и немного Go c Perl. В итоге, я полюбил типизацию, понял прелести полиморфизма, и жить не могу без использования интерфейсов. В Питоне нету интерфейсов, но есть Абстрактные классы и Протоколы. На первый взгляд, это одно и тоже, но в по факту работают по разному. В этом хорошее видео отлично объясняют в чем же разница.

#areRightALot #leadership #principles #amazon

Leaders are right a lot. They have strong judgment and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.

We make decisions every day - smaller or bigger. One very good LP at Amazon that I naturally tend to follow is "Are Right A Lot". Amazon expects engineers to avoid unnecessary mistakes. Everyone is supposed either to be experienced enough to make decisions using past experience or to use other techniques like deriving decisions from data and design documents (SDE-I, II engineers are terrible writers, though). Ofc, it is also fine to simply discuss something over slack for minor points. This is a significantly useful skill that many of us train each day. If you prepare for an interview, ensure you have a story that exemplifies you approach dealing with ambiguity the right way.

If you study algorithms and data structures, this website has pretty cool visualization of algorithms
https://visualgo.net

#interview

Interview preparation materials. Nice!

https://techinterviewhandbook.org/

Неплохой гайд по подготовке к интервью по SQL с примерами от «Тамби Масаева»: https://www.youtube.com/watch?v=y6CWIBKEw_g

А лучшие новости для меня такие ☝️:

Привет) Хотел сказать спасибо за твой канал и за твою помощь, я в итоге получил офер)

Не знаю как вам, а мне так нравится идея ФП. Жду когда будет возможность сравнить его с ООП

Очень интересное мнение на тему почему ФП не стал нормой для программистов. Посмотрел как остросюжетное кино: https://www.youtube.com/watch?v=QyJZzq0v7Z4

5 минутная мотивашка на тему как поасть в FANG: https://www.youtube.com/watch?v=sv7uiIIVX5A

Про выгорание https://youtu.be/aRs1Kfw8cqs, вдруг кому-то поможет

Software Engineers Tenure in San Francisco (2017?)

Source: https://hackerlife.co/blog/san-francisco-large-corporation-employee-tenure

#interview #mock
🥳🥳🥳 Еще очень крутые новости с утра!

Хотел вас поблагодарить. Получил оффер.Вы провели со мной два алго мока, и давали хороший фидбек. Очень ценная помощь.Спасибо еще раз!

#coding #algorithm #dynamic #programming #interview

Замечательное объяснение подхода решения задач через Динамическое Программирование!

Если знаете еще хорошие ресурсы (видео, статьи), то поделитесь в комментах, для меня и других

https://youtu.be/aPQY__2H3tE

#interviews #coding #algorithms

Top 10 Dynamic Programming Problems

https://www.techiedelight.com/top-10-dynamic-programming-problems/

#interview #behavioral #levels

Хороший текст про уровни в Google, похожее в Амазон.
Примерный маппинг уровней в Гугл с уровнями в Амазон
- L3 at Google is ~L4 at Amazon
- L4 at Google is ~L5 at Amazon
- L5 at Google is ~L6 at Amazon
- L6 at Google is ~L6 at Amazon - да, все верно, в Амазон L6 - это много.

Возьмите на заметку те, кто проходят поведенческие собеседования, чтобы подобрать соответсвующую историю.

I'm a staff engineer at Google. My take on it is a little different than the others here.

The normal levels are 3/4/5, with 5 being Senior Engineer. It is normal for you to progress up level 5, Senior. What that level means is this: Google has something that needs to be done, and knows that you can take care of it on your own with no problems. In other words, Google knows what the problems is, knows what the answer is, and trusts you to get it done and take care of the details.

The next level is 6, Staff Engineer. What this means is: Google knows what the problem is, but does not know what the answer is, but trusts that you will figure out how to solve the problem. Levels above 6 are more concerned with identifying problems/goals, and figuring out which ones to tackle (in addition to the things in all the other levels).

So, in my opinion, the best way to become a staff engineer is to have a track record of solving problems that other people don't immediately know the answer to. If there are issues out there, where people are scratching their heads and thinking, "I'm not really sure how we should go about that.", and you are consistently solving those problems, then that is where you want to be for getting to level 6, Staff. People will be saying, "I'm glad that person X did it, because I didn't know how to solve the problem."

Additionally, at level 6, you should have a broad view of how things work at Google and what teams are working on what projects in your department.

First you have to get to level 5, Senior. That's where people are looking at an issue and thinking, "I generally know how to tackle that, but it is complex and involves a lot of work and a lot of experience." Tackling those problems is how you get to level 5. People will be saying, "I'm glad X did it, because it looked complex, like it would take a lot of hard work."

To get to level 4, people should be thinking about you: "I'm glad X is working on this for me, because they didn't need much help or guidance and figured out a lot of it on their own."

To get to level 3, people should be thinking about you: "I'm glad X is here, because they understand how computers really work, they learn fast, they are passionate about technology, they can take charge, they communicate well, they are friendly and fun to be around, and they can see the underlying abstract patterns that are obscured by many surface details."

Source: https://www.quora.com/How-does-one-become-a-Staff-Software-Engineer-at-Google-What-might-a-new-grad-entering-the-company-do-to-grow-their-career-to-reach-that-level

Курс про построение дата-пайплайнов на основе Apache Kafka от Confluent: https://developer.confluent.io/learn-kafka/data-pipelines/intro/