Design and implement data processing pipelines using tools like Hadoop, Spark, and Kafka to handle large datasets efficiently.

Develop and maintain ETL (Extract, Transform, Load) processes to ingest data from various sources into data warehouses or data lakes.

Optimize data processing algorithms and queries to improve performance and reduce processing time, ensuring scalability.

Monitor data pipelines for errors and performance bottlenecks, implementing proactive measures to ensure data quality and reliability.

Collaborate with data scientists and analysts to understand data requirements and provide processed data for analysis and modeling.

Implement data quality checks and validation rules to ensure data accuracy and consistency throughout the processing pipeline.

Automate data processing tasks using scripting languages like Python or shell scripting to improve efficiency and reduce manual intervention.

Document data processing workflows and procedures to ensure knowledge sharing and maintainability of the data processing infrastructure.

Troubleshoot data processing issues and implement solutions to resolve data quality or performance problems in a timely manner.

Stay updated with the latest data processing technologies and trends, evaluating and implementing new tools and techniques to improve data processing capabilities.