AWS Data Engineer with Glue, Lambda, Redshift, Event Bridge

Saxon Global

Memphis (TN)

On-site

USD 100,000 - 130,000

Full time

14 days+

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Job summary

A leading technology firm is seeking an experienced AWS Data Engineer in Memphis, TN. The role involves designing and maintaining data pipelines using AWS services like Glue and Lambda. You will work closely with various teams to ensure data integrity and efficiency. This full-time position is ideal for candidates with a mid-senior level experience in information technology and investment banking.

Qualifications

Experience in designing and implementing data pipelines on AWS.
Strong knowledge of data governance processes and quality checks.
Familiarity with performance optimization techniques.

Responsibilities

Design and implement scalable data pipelines using AWS services.
Architect data models for analytical and reporting needs.
Integrate data from various sources into consistent formats.

Skills

AWS Glue

AWS Lambda

Data Modeling

ETL Processes

Data Integration

Data Quality

Tools

AWS Data Pipeline

Redshift

Event Bridge

Job description

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

Key Responsibilities

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.

Seniority level