AWS Data Engineer Glue, Lambda, Redshift, Event Bridge

Overview

AWS Data Engineer, you will be responsible for designing, implementing, and maintaining scalable data pipelines and solutions on the AWS enterprise platform. You will collaborate closely with cross-functional teams to gather requirements, architect data solutions, and ensure data integrity, availability, and performance.

Primary AWS Tools Utilized
:
Glue, Lambda, Redshift, Event Bridge

• Start Date:
  
  Immediate
• Location:
  
  Onsite - East Memphis (Germantown area) / Open to St. Louis for right candidate
• Employment Status:
  No form of employment accommodation or sponsorship is being considered

• Design and Develop Data Pipelines:
  Design, develop, and implement scalable and efficient data pipelines using AWS services such as AWS Glue, AWS Data Pipeline, AWS Lambda, and other relevant technologies.
• Data Modeling and Architecture:
  Architect data models and data flows to support the organization's analytical and reporting needs. Implement data structures optimized for performance and scalability.
• Data Integration:
  Integrate data from various sources including databases, APIs, and streaming data sources. Ensure seamless data flow and consistency across different systems.
• Data Transformation and ETL:
  Develop ETL processes to transform raw data into usable formats for analytics and reporting. Optimize data transformation processes for performance and efficiency.
• Data Quality and Governance:
  Implement data quality checks and governance processes to ensure data accuracy, consistency, and compliance with regulatory requirements.
• Monitoring and Optimization:
  Monitor data pipelines and systems for performance and reliability. Implement optimizations and improvements to enhance efficiency and scalability.
• Collaboration and Communication:
  Collaborate closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions that meet business needs. Communicate effectively with technical and non-technical stakeholders.
• Documentation and Best Practices:
  Document data pipelines, data models, and technical specifications. Promote best practices for data engineering and ensure adherence to standards and guidelines.

• Mid-Senior level

• Full-time

• Information Technology

• Investment Banking

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