- Design and Develop Data Pipelines: Create and maintain efficient, reliable, and scalable data pipelines that extract, transform, and load (ETL) data from diverse sources into AWS data storage systems.
- Data Modeling and Architecture: Design and implement data models for data warehousing and data lakes on AWS, ensuring data integrity, performance, and scalability.
- AWS Cloud Infrastructure: Utilize various AWS services such as Amazon S3, Amazon Redshift, AWS Glue, Amazon EMR, Amazon RDS, and others to build data solutions.
- Data Transformation and Processing: Develop data transformation processes, including data cleansing, enrichment, and aggregation, to ensure data accuracy and consistency.
- Performance Optimization: Identify and implement performance optimization techniques to enhance data processing speed and reduce latency in data pipelines.
- Data Security and Compliance: Ensure that data handling practices comply with relevant data security and privacy regulations. Implement security measures to protect sensitive data.
- Monitoring and Troubleshooting: Monitor data pipelines, data jobs, and data storage systems for issues and troubleshoot any data-related problems to ensure smooth data flow.
- Documentation: Create and maintain technical documentation for data engineering processes, data models, and data pipelines.
- Collaboration and Communication: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions that meet business needs.
- Continuous Improvement: Stay updated with the latest AWS services and data engineering best practices to propose and implement improvements to the existing data infrastructure.