From the perspective of career development path, it can generally be divided into two routes, one is the professional technical route, also called the T sequence, and the other is the management route, also called the M sequence. Each sequence is divided into many levels. T-sequence general positions from low to high are engineer, senior engineer, architect/expert, senior architect/senior expert, senior architect/senior expert, chief architect/chief expert/chief scientist, etc. Of course, the name of each company The law may not be the same, but it is similar. T-sequence generally focuses on technology. Of course, higher levels will bring teams, but the number of teams in T-sequence belts is less than that of M-sequence belts of the same level. The M sequence is generally engineer, senior engineer, TeamLeader/supervisor, technical manager, senior technical manager, deputy director, director, senior director, general manager, vice president VP, and CTO from low to high. In addition, no matter whether you take the T sequence or the M sequence, you will eventually have the opportunity to develop into a CTO. There is a leap-forward promotion in career development. In this case, personal ability is generally longer in practice in the same position, and the ability has been greatly improved. If you encounter a good opportunity, you can make a leap. For example, the leap from senior engineer to director, from technical manager to technical VP, and from architect to CTO. Regardless of whether you are cross-level, you need to learn a lot of skills to improve yourself every time you are promoted. This skill is mainly the skills of the technology itself. Of course, the management M-sequence must be improved.
1. Hadoop platform operation and maintenance engineer
Many Hadoop platform operation and maintenance engineers have transferred from traditional operation and maintenance engineers and have not done actual programming development. If you go in the direction of big data, you must learn development, programming, and develop toward architects, big data platform managers, and directors.
2. Big data platform engineer
You can develop as a big data architect and take a professional route, or you can develop as a big data platform manager and director.
3. Big data ETL engineer
To the data analysis manager and director, you can also develop to the big data platform manager and director.
4. Streaming Computing Engineer
You can develop in the direction of big data platform managers and directors, or in the direction of big data architects.
5. Data Warehouse Engineer
You can develop in the direction of data analysis manager and director.
6.Spark engineer
You can develop in the direction of big data platform managers and directors, or in the direction of big data architects.
7. A search engineer
can develop into a search leader/Leader, it is best to learn the recommendation algorithm, and then develop to the director of the search recommendation department, or as a search architect.
8. Recommended algorithm engineer
You can develop as an algorithm manager, director, or search recommendation department director, or you can recommend a system architect.
9. User Portrait Engineer
You can develop in the direction of data analysis manager and director, or you can develop in the direction of algorithm manager and director.
10. Natural language processing NLP engineer
You can develop in the direction of NLP algorithm leader, algorithm manager, and director.
11.Machine learning engineer
You can develop in the direction of algorithm manager and director, or in the direction of algorithm architect.
12. Data mining engineer
You can develop in the direction of data analysis manager and director.
13.Deep learning engineer
You can develop in the direction of algorithm manager and director.
14. Data Analyst
Go up and develop into data analysis manager and data analysis director.
15. Web development engineer partial back-end interface
Go up and develop into engineering technical manager, technical director, or go to T sequence to develop into architect.
16.Front-end engineer
It is best to learn 15 skills and follow the 15 route. Of course, you can also develop into a front-end architect.
17. Big Data Product Manager
It’s best to move up from the big data department to a company-level product director and product VP.
18. Director of Big Data Platform
Developed as a big data VP.
19. Director of Algorithms
Developed as a big data VP.
20. Director of Data Analysis
Developed as a big data VP.
21. Big data architect, chief big data architect
Developed as a big data VP.
22. VP of Big Data Vice President
Improve yourself in other areas of skills, such as Web engineering, front-end, mobile development, website architecture, etc., and then develop into CTO.
The market average salary level of each position.
Position salary is related to working years, technical level, educational background, and company background. Therefore, for the same position, there is no fixed value, only a rough range. In addition, it is also related to market supply and demand. In recent years, there is a shortage of big data and artificial intelligence talents, and even more scarce is the talents of artificial intelligence. Therefore, from the perspective of the overall market, the salary of big data is higher than that of Web development and the ratio of artificial intelligence. Big data is higher. After a few years, despite changes in prices and market supply and demand, the average salary situation in the market will also change. Below is an approximate range of the average salary in the current job market. In addition, recruitment websites often give annual salary, because the annual salary is paid for 12 months, and some for 16 months, which is not uniform, and the annual salary structure of some companies is base cash. Part + the sum of the value of the discounted equity option, so the calculation based on the annual salary can not clearly feedback the actual salary status, so we are based on the base cash part of the monthly salary, and here refers to the pre-tax salary, and the region is represented by Beijing . The following are personal opinions, for reference only, not as authoritative data:
1. Hadoop platform operation and maintenance engineer
The monthly salary is about 1.5 to 2.5w. The w letter stands for Wan. This position is generally slightly lower than the salary of a big data platform engineer. The main reason is that the operation and maintenance personnel may not have the ability to develop project code. Of course, except for those with strong personal ability.
2. Big data platform engineer
Around 2 to 3w, big data platforms generally have both cluster operation and maintenance and project programming and development capabilities, and the salary is a bit higher. Generally, there are three years of relevant work experience, and a monthly salary of 2w or more is relatively easy. 3w is a demarcation point, and it is not easy to break through 3w.
3. Big data ETL engineer
Around 2 to 3w, the salary range is similar to that of big data platform engineers, but slightly lower, mainly because ETL engineers generally have relatively weak engineering capabilities. This is the overall point of view, people with strong ability can also be higher than big data platform engineers. If the ETL engineer reaches 2.5w or more, the increase will be slower. 3w is also a salary bottleneck, and it is not easy to break through 3w.
4. Streaming Computing Engineer
About 2 to 3w, similar to big data platform engineers.
5. Data Warehouse Engineer
Data warehouse engineers generally have weak engineering capabilities. If they can reach 2w, they are already very good. 2.5w is considered very high, and it is difficult to break through 3w.
6.Spark engineer
About 2 to 3w, similar to big data platform engineers.
7. Search for engineers
Around 2 to 4w, the salary of search engineers is slightly higher. Generally working for three years, it is easier to reach 2w. With 5 years of relevant experience, it is not difficult to break through 3w, and it is reasonable to reach 4w with more than 8 years of experience. The highest can exceed 5w.
8. Recommended algorithm engineer
Generally around 2 to 4w, the recommendation algorithm is deeper than search, and the salary is slightly higher than that of search engineers.
9. User Portrait Engineer
From 2 to 3w, the user portrait engineer can focus on data statistics or algorithm engineering. It is easier to reach 2w. It is possible to make a deep breakthrough in the algorithm.
10. Natural language processing NLP engineer
From 2 to 4w, this position is an emerging position in recent years, and there is a shortage of talents. The salary is similar to the recommended algorithm position.
11.Machine learning engineer
2 to 4w, the salary is similar to the recommended algorithm position.
12. Data mining engineer
From 2 to 3w, general data mining is partial to data analysis, and reaching 2.5w is not low. Of course, it is a little bit engineering, and it is reasonable to break through 3w.
13.Deep learning engineer
This is an emerging position in recent years and there is a shortage of talents. The salary is 2 to 4w. It is not difficult to break through 4w. Senior can reach more than 5w.
14. Data Analyst
1.5 to 2.5w, data analysis is partial data statistics, and overall salary is slightly lower than that of machine learning engineers. Generally, the number of girls in this area is higher than that in other engineering jobs, because on the whole, there are many more technical men than women. If girls who do data analysis can account for half, in fact this proportion is already very high. The role of data analysis is similar to the salary of machine learning, and it is not a problem to break through 3w.
15. Web development engineer partial back-end interface
1 to 2.5w, pure Web development within 20,000 is more common, senior can exceed 2.5w. If you are very good, you can be an architect, and it is easy to be more than 3w.
16.Front-end engineer
1 to 2w, generally a little lower than the web back-end salary, generally not more than 2w.
17. Big Data Product Manager
From 1.5 to 2.5w, big data product managers are emerging in recent years, and the talents are relatively scarce, so it is difficult to recruit. I think most of them are traditional products. Big data product managers are often transferred from traditional products and understand some data-driven and algorithm-driven knowledge, so the salary is higher than that of traditional product managers. 1.5w is relatively easy, and seniors can reach 2.5w.
18. Director of Big Data Platform
From 3 to 6w, the lowest starting price for directors is 3w, and 5w is relatively normal. 6w is a bottleneck, it is not easy to break through. Of course, directors are also divided into levels, including intermediate directors and senior directors. Senior director 6w or more is relatively easy.
19. Director of Algorithms
3 to 6w, compared with the director of big data platform, even slightly higher.
20. Director of Data Analysis
3 to 6w, compared with the director of big data platform, generally slightly lower.
21. Big data architect, chief big data architect
Architects and directors have similar salaries, but they are also divided into levels. Intermediate, senior, senior, chief. Generally, senior architects may be higher than directors. The chief architect is the highest and can reach the salary level of the VP of Big Data.
22. VP of Big Data Vice President
From 6 to 10w, the salary of chief architect and big data VP mentioned above is similar. These two positions are generally technically more technical than the big data VP, and the big data VP management skills are stronger. But the overall comprehensive strength is similar, both have a wide range of technical knowledge, and generally they also lead teams, but the big data VP leads more people. Generally, the position of big data VP starts at 6w, and 8w is more common. Breaking through 10w is not a problem.
In this chapter, we have a deeper understanding of the organizational structure of the big data department and the situation of each position. In the following chapters, we have a deep understanding of the system architecture of common big data algorithms in order to better understand the business and products.
to sum up
In addition to career planning and development paths for various positions , other deep learning frameworks also have good open source implementations, such as MXNet. Please pay attention to the charging app, courses, WeChat groups later. For more content, please see the new book "Distributed Machine Learning Practice" (Artificial Intelligence Science and Technology Series)"
Other deep learning frameworks also have good open source implementations, such as MXNet. Please pay attention to the charging app, courses, WeChat groups. For more content, please see the new book "Distributed Machine Learning Practice (Artificial Intelligence Science and Technology Series)"
[New book introduction]
"Distributed machine learning in practice" (artificial intelligence science and technology series) [edited by Chen Jinglei] [Tsinghua University Press]
Features of the new book: Explain the framework of distributed machine learning and its application supporting personalized recommendation algorithm system step by step , Face recognition, dialogue robots and other practical projects
[New book introduction video]
Distributed machine learning practice (artificial intelligence science and technology series) new book [Chen Jinglei]
Video features: focus on the introduction of new books, analysis of the latest cutting-edge technology hotspots, and technical career planning suggestions! After listening to this lesson, you will have a brand new technological vision in the field of artificial intelligence! Career development will also have a clearer understanding!
[Excellent Course]
"Distributed Machine Learning Practical Combat" Big Data Artificial Intelligence AI Expert-level Excellent Course
[Free experience video]:
Video features: This series of expert-level high-quality courses has a corresponding supporting book "Distributed Machine Learning Practical Combat". The high-quality courses and books can complement each other and complement each other, which greatly improves the learning efficiency. This series of courses and books take distributed machine learning as the main line, and give a detailed introduction to the big data technology it depends on. After that, it will focus on the current mainstream distributed machine learning frameworks and algorithms. This series of courses and books focus on actual combat. , Finally, I will talk about a few industrial-level system combat projects for everyone. The core content of the course includes Internet company big data and artificial intelligence, big data algorithm system architecture, big data foundation, Python programming, Java programming, Scala programming, Docker container, Mahout distributed machine learning platform, Spark distributed machine learning platform, Distributed deep learning framework and neural network algorithm, natural language processing algorithm, industrial-grade complete system combat (recommended algorithm system combat, face recognition combat, dialogue robot combat), employment/interview skills/career planning/promotion guidance, etc. .
[Is it charged? Company introduction]
Rechargeable App is an online education platform focusing on vocational training and learning for office workers.
Focus on the improvement and learning of work vocational skills, improve work efficiency, and bring economic benefits! Are you charging today?
Is it charging?
http://www.chongdianleme.com/
Is it charging? App official website download address
https://a.app.qq.com/o/simple.jsp?pkgname=com.charged.app
Features are as follows:
[Jobs in the whole industry]-Focus on improving the vocational skills of office workers in the workplace
Covering all industries and positions, whether you are an office worker, executive or entrepreneur, there are videos and articles you want to learn. Among them, big data intelligent AI, blockchain, and deep learning are the practical experience of the Internet's first-line industrial level.
In addition to professional skills learning, there are general workplace skills, such as corporate management, equity incentives and design, career planning, social etiquette, communication skills, presentation skills, meeting skills, emailing skills, how to relax work pressure, personal connections, etc. Improve your professional level and overall quality in all aspects.
【Niuren Classroom】-Learn the work experience of Niuren
1. Intelligent personalization engine:
Massive video courses, covering all industries and all positions, through the skill word preference mining analysis of different industries and positions, intelligently matching the skill learning courses that are most interested in your current position.
2. Search the whole network
Enter keywords to search for massive video courses, there are everything, there is always a course suitable for you.
3. Details of listening to the class
Video playback details, in addition to playing the current video, there are also related video courses and article reading, strengthening a certain skill knowledge point, allowing you to easily become a senior expert in a certain field.
【Excellent Reading】-Interested reading of skill articles
1. Personalized reading engine:
Tens of millions of articles to read, covering all industries and all positions, through the skill word preference mining analysis of positions in different industries, intelligently matching the skills learning articles you are most interested in in your current position.
2. Read the whole network search
Enter keywords to search for a large number of articles to read, everything is available, there are always skills learning articles you are interested in.
[Robot Teacher]-Personally enhance fun learning
Based on the search engine and intelligent deep learning training, we will create a robot teacher who understands you better, chat and learn with the robot teacher in natural language, entertaining and learning, efficient learning, and happy life.
【Short Course】-Learn knowledge efficiently
Massive short courses to satisfy your time fragmented learning and quickly improve a certain skill knowledge point.