1. Responsible for the design, development, and optimization of big data ETL processes, ensuring the accuracy, completeness, and timeliness of data. Understand business requirements, participate in data warehouse architecture design, develop reasonable ETL solutions, and meet the data processing requirements of different business scenarios. 2. Spark application development uses Spark for large-scale data processing and analysis, develops Spark applications, and implements operations such as data cleaning, transformation, and loading. Optimize Spark job performance, optimize Spark tasks, improve data processing efficiency, and reduce resource consumption. 3. Python programming and script development utilize Python to write data processing scripts and tools for tasks such as data collection, preprocessing, and monitoring. Collaborate with other teams to integrate Python code with Spark applications to achieve more complex data processing workflows. 4. PySpark integration and development are carried out in the PySpark environment, fully leveraging the advantages of Python and Spark to achieve efficient data processing and analysis. Resolve technical issues encountered during PySpark development, such as data type conversion, performance optimization, memory management, etc. 5. Develop and implement data quality monitoring strategies for data quality assurance, conduct quality checks and validations on data during the ETL process, and promptly identify and resolve data quality issues. Establish a data quality reporting mechanism, regularly report data quality status to relevant teams, and provide support for data decision-making. 6. Team collaboration and technical support: Work closely with team members such as data analysts, data scientists, and data warehouse engineers to complete project tasks and provide technical support and solutions. Participate in team technical exchanges and sharing, continuously improve the overall technical level and development efficiency of the team.