Data engineers have the following responsibilities:

Data engineers are professionals who are responsible for designing, building, and maintaining data processing systems. They are primarily concerned with the flow, transformation, and storage of data to ensure its reliability, security, and efficiency.

1. Data collection and extraction: obtain data from different data sources (database, files, APIs, etc.) and extract, clean and transform it for further processing and analysis.

2. Data storage and management: choose an appropriate database or data warehouse to store and organize data, and ensure the completeness and consistency of the data.

3, data conversion and processing: processing, conversion and organization of raw data to meet specific business needs and analysis purposes.

4, data pipeline and workflow: design and construction of data pipeline, data transfer from the source to the target system, and automated data processing workflow.

5. Data Quality and Monitoring: Ensure the accuracy, completeness, and reliability of data, and set up a monitoring mechanism to detect Sun drafts and solve data quality problems in a timely manner.

6, data security and privacy: take measures to protect data security and privacy, and comply with relevant laws and regulations and data management policies.

7. Technology Selection and Architecture Design: Evaluate and select appropriate technology tools and frameworks, and design a scalable, efficient and maintainable data processing system architecture.

Data Engineer Application Requirements

1, Education Requirements: Usually requires a Bachelor's Degree or above, with related majors such as Computer Science, Software Engineering, Data Science, and Information Management.

2. Technical background: solid programming skills and familiarity with common programming languages and tools for data processing and analysis, such as Python, SQL, Hadoop, Spark, etc., are required.

3, data processing experience: some data processing experience is required, including actual project experience in data collection, cleaning, transformation and storage.

4, database knowledge: a certain understanding of relational databases and non-relational databases and practical experience, familiar with data modeling, query language, etc..

5, big data technology: understanding of the basic principles and applications of big data technology, such as distributed computing, data lake, data stream processing.