Urgent Requirement || AWS Data Engineer with AWS Certified top 22 || Hartford, CT (Hybrid) Local

As an AWS Data Engineer, your primary responsibility is to design, implement, and maintain data architecture and infrastructure on the Amazon Web Services (AWS) platform. This AWS Data Engineer  role involves working with various data sources, processing frameworks, and storage solutions to ensure efficient and reliable data processing and analysis. Here are some key aspects of the role:

1. Data Architecture Design:
   • Design and implement scalable and efficient data architectures on AWS.
   • Choose appropriate data storage solutions based on the requirements, such as Amazon S3, Amazon Redshift, Amazon DynamoDB, etc.
   • Define data ingestion, transformation, and AWS Data Engineer storage processes.
2. Data Processing:
   • Implement data processing pipelines using services like AWS Glue, Apache Spark on Amazon EMR, or other relevant tools.
   • Optimize and tune data processing workflows for AWS Data Engineer performance and cost-efficiency.
   • Handle real-time and batch processing as per AWS Data Engineer the business needs.
3. Data Integration:
   • Integrate data from various sources, including databases, APIs, and streaming data.
   • Ensure data consistency and quality across different sources.
4. Data Security and Compliance:
   • Implement security measures to protect sensitive data.
   • Ensure compliance with data privacy regulations and industry standards.
5. Monitoring and Optimization:
   • Set up monitoring and alerting for data pipelines to detect and address issues proactively.
   • Continuously optimize data processing workflows AWS Data Engineer for better performance and cost-effectiveness.
6. Collaboration:
   • Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver appropriate solutions.
   • Collaborate with other teams to integrate data engineering AWS Data Engineer solutions into broader systems.
7. Automation and Infrastructure as Code:
   • Implement automation using AWS services or tools like AWS CloudFormation for infrastructure provisioning and management.
   • Leverage Infrastructure as Code (IaC) principles to ensure consistency and reproducibility.
8. Documentation:
   • Document data engineering processes, workflows, and configurations.
   • Create documentation for troubleshooting and maintenance procedures.
9. Continuous Learning:
   • Stay updated on the latest AWS services and features related to data engineering.
   • Explore and implement new technologies to improve data processing capabilities.
10. Troubleshooting and Support:
    • Provide support for data-related issues and troubleshoot problems in data pipelines.
    • Collaborate with AWS support when necessary.

Having a solid understanding of AWS services, database systems, ETL processes, and programming languages (e.g., Python, Scala) is essential for success in us staffing this role. Additionally, familiarity with data warehousing concepts, data modeling, and data governance practices will contribute to effective data engineering on AWS.