Five elements of recruiting in large factories

3dbdbc47a5e7e4a54d8f7a5a08688f6a.png3 million words! The most complete big data learning interview community on the whole network is waiting for you!

Many students want to enter a large factory or a large Party A company, especially in the direction of big data development, and the positions are also concentrated in this group of companies at the head. In the past two years, the threshold for entering a large factory has become higher and higher, so what are the conditions for entering these companies?

Take the time today to take stock of several core elements of entering a big factory, students who want to enter a big factory, listen carefully!

1. Education background

985/211, QS top 100 students are especially popular with large companies. Because a better academic background represents your intelligence, learning ability, ability to accept new things, and ability to deal with new problems. Especially for fresh graduates, large factories generally have target colleges, and the proportion of academic qualifications in school recruitment is generally around 30-50%. However, the education factor will attenuate with the extension of working hours. Generally, the work is more than 5 years. Education is only the threshold for us to pass the resume screening, and it will account for 1-2% of the factor at most. More depends on the skills themselves. So for social recruitment, the skills we have mastered and the complexity of past projects will account for more than 80% of the factors. This is what I have repeatedly emphasized to everyone before. Work history and project complexity.

2. Past company history

When recruiting, big factories will value the company's popularity in the candidate's past work resume and the company's industry ranking. For example, in your industry, your company ranks in the top 3 or 5 in the industry. In addition, it reflects to our data development students that they will value the integrity of your past company's technology stack, data scale, and business complexity. This is also because many students have been working in small companies, and it will be very difficult to directly enter a large company. In essence, in addition to the technical stack and project complexity, it cannot be compared with big companies, and there is also a big difference in vision and professionalism. You can find a medium-sized company with a higher ranking in the industry to make the transition.

3. Matching degree of the project

This is easy to understand, that is, whether the projects you have done in the past are vacancies in a large factory. For example, if the business is similar or the same, it is originally a competitive relationship. Whether the technology stacks used in the project match each other. The experience of the former company can be copied directly. In addition to technology stack and business matching, the more important point is that the interviewer will also pay attention to the results or performance you have achieved in these projects, and the interviewer will infer your way of doing things and methods through your project description and achievement statement. So you see, the information that the interviewer hopes to get from your resume is definitely not just as simple as a certain technical point, but will examine deeper and higher-level technical understanding, business understanding, and methodology of doing things, etc.

4. Professionalism

The word "professionalism" is what I especially emphasized in writing articles and videos this year. The so-called professionalism is the degree of understanding of the position you are engaged in, and you must have a global perspective. For example, what are the mainstream technology stacks in the direction of data development, what problems are they solving, the mainstream technical solutions in the industry and their pros and cons, and the future development direction and laws of data development. In addition to the technology itself, we can consider business more In itself, for example, understand the industrial chain of the industry, the relationship between upstream and downstream, the development of competition, etc., and have a certain understanding of products, operations, and technologies. All considerations, this is also the only way for us to gradually advance from the most basic level of developers to technical experts and architects, and form our own methodology and technical methodology. There will be a qualitative leap in sensitivity and foresight.

5. People themselves

When considering a candidate, the interviewer will use the candidate's interview process and past project experience to judge the candidate's attitude, sense of responsibility, self-reflection, self-motivation and other personal qualities. During the interview, the team leader will also consider the candidate's plasticity, whether it is worth training, and whether he can play a greater role in the team through training.

In addition, there are some soft qualities, such as communication skills, coordination skills and so on. In addition, a qualified interviewer will also look at a person's appearance, whether he is full of energy, full of confidence, etc. I believe everyone is fine with this. After all, who is not Yushu Linfeng?

Of course, the last point is "luck". There is a clear difference between the current IT industry and 3 years ago. Job opportunities are decreasing, and interview opportunities are also decreasing. There are more metaphysical factors in interviews. Replay, more summary.

If this article is helpful to you, don't forget to  "Like",  "Like",  and "Favorite"  three times!

c849fed2272a6a5423a262a0d5263eca.png

c155871031f2723c4f6df551639f4e4e.jpeg

It will be released on the whole network in 2022 | Big data expert-level skill model and learning guide (Shengtian Banzi)

The Internet's worst era may indeed be here

I am studying in university at Bilibili, majoring in big data

What are we learning when we are learning Flink?

193 articles beat Flink violently, you need to pay attention to this collection

Flink production environment TOP problems and optimization, Alibaba Tibetan Scripture Pavilion YYDS

Flink CDC I'm sure Jesus can't keep him! | Flink CDC online problem inventory

What are we learning when we are learning Spark?

Among all Spark modules, I would like to call SparkSQL the strongest!

Hard Gang Hive | 40,000-word Basic Tuning Interview Summary

A Small Encyclopedia of Data Governance Methodologies and Practices

A small guide to user portrait construction under the label system

40,000-word long text | ClickHouse basics & practice & tuning full perspective analysis

[Interview & Personal Growth] More than half of 2021, the experience of social recruitment and school recruitment

Another decade begins in the direction of big data | The first edition of "Hard Gang Series" ends

Articles I have written about growth/interview/career advancement

What are we learning when we are learning Hive? "Hard Hive Sequel"

Guess you like

Origin blog.csdn.net/u013411339/article/details/132550729