Amazon S3 Engineer

Must Have Technical/Functional Skills

Primary Skil:
Amazon Data Engineer

Secondary: AWS Data Engineer, Amazon S3, Shell Scripting, Autosys

Experience:

Minimum 10 years

• Design, develop, and execute Data Pipelines and test cases to ensure data integrity and quality.
• Develop, implement, and optimize data pipelines that integrate Amazon S3 for scalable data storage, retrieval, and processing within ETL workflows.
• Leverage Amazon S3 for data storage, retrieval, and management within ETL workflows, including the ability to write scripts for data transfer between S3 and other systems.
• Utilize Amazon S3's advanced features such as versioning, lifecycle policies, access controls, and server-side encryption to ensure secure and efficient data management.
• Write, maintain, and troubleshoot scripts or code (using PySpark, Shell, or similar languages) to automate data movement between Amazon S3 and other platforms, ensuring high performance and reliability.
• Collaborate with cross-functional teams to troubleshoot and resolve data-related issues, utilizing Amazon S3 features such as versioning, lifecycle policies, and access management.
• Document ETL processes, maintain technical documentation, and ensure best practices are followed for data stored in Amazon S3 environments.
• Familiarity with Hadoop or Spark is often preferred.
• Validate HiveQL, HDFS file structures, and data processing within the Hadoop cluster.
• Strong analytical and troubleshooting skills.
• Excellent communication for collaborating with developers and stakeholders.
• Knowledge in Metadata dependent ETL process and batch/job framework

• Amazon S3:
  Data storage, retrieval, and management; scripting for ETL data transfer; advanced features including versioning, lifecycle policies, access controls, and server-side encryption; automation of data movement using Python, Shell, or similar languages; troubleshooting and collaboration for data-related issues; documentation and best practices for ETL processes.
• Py Spark
• SQL
• Oracle
• Domain:
  Banking knowledge, Payment’s knowledge preferred.
• Environment:
  Cloudera Platform
• Concept:
  Cloud Storage, Amazon S3, AWS, Data Warehousing, Data Transformation, ETL/ELT, Data Quality.