Description

Job Description

An AWS Glue and Amazon S3 job description typically focuses on the design, development, and maintenance of data pipelines and ETL (Extract, Transform, Load) processes within the AWS ecosystem, heavily leveraging AWS Glue for data transformation and Amazon S3 for data storage.

Key Responsibilities:

•            Data Pipeline Design and Development:

Designing, building, and maintaining scalable and efficient data pipelines using AWS Glue, often integrating with other AWS services like Lambda, EMR, Redshift, Kinesis, and Athena.

•            ETL Job Creation and Management:

Developing and optimizing AWS Glue ETL jobs (using Spark/Python/Scala) to clean, transform, and enrich raw data, preparing it for analytics or other downstream applications.

•            Data Cataloging and Discovery:

Utilizing AWS Glue Data Catalog and crawlers to automatically discover, catalog, and manage metadata for various data sources, including those stored in Amazon S3.

•            Amazon S3 Data Management:

Managing data storage in Amazon S3, including setting up appropriate storage classes, managing data lifecycle policies, and ensuring data security and accessibility.

•            Data Quality and Validation:

Implementing data quality checks and validation rules within the ETL process to ensure the accuracy and reliability of data.

•            Monitoring and Troubleshooting:

Monitoring the performance and health of Glue jobs and data pipelines, troubleshooting issues, and optimizing for efficiency and cost-effectiveness.

•            Collaboration and Communication:

Working with data engineers, data scientists, and other stakeholders to understand data requirements and deliver robust data solutions.

Required Skills and Qualifications:

•            AWS Services Expertise:

In-depth knowledge of AWS Glue, Amazon S3, and other relevant AWS big data and analytics services.

•            Programming Languages:

Proficiency in programming languages commonly used with AWS Glue, such as Python, Scala, and Spark.

•            Data Warehousing and Data Lakes:

Understanding of data warehousing concepts, data lake architectures, and best practices for data storage and retrieval.

•            SQL and Database Skills:

Strong SQL skills for data manipulation and analysis, and understanding of database design principles.

•            Analytical and Problem-Solving Skills:

Ability to analyze complex data problems, design effective solutions, and troubleshoot technical issues.

•            Communication Skills:

Effective communication skills to collaborate with team members and explain technical concepts clearly

Key Skills

Education

Any Graduate