Data Engineer

Data Engineer 3 – Quality Assurance (AI & Data Platforms)

Location - Atlanta, Ga

Client - Southern Company Gas

Contract - 10 Months

We are seeking an experienced Data Engineer – Quality Assurance to support data quality, testing, and validation across our AI-driven analytics platform. This role is focused on testing data and AI analytical models as data flows through a data lake architecture
, ensuring accuracy, reliability, and performance.

As a Quality Assurance Data Engineer within the EDGE group
, you will play a critical role in enforcing data quality standards, validating machine learning models, and supporting continuous improvement of our data and AI systems through both manual and automated testing
.

• Design, develop, and execute data quality tests for data moving through relational systems and data lakes
• Monitor, validate, and enforce data integrity, accuracy, and consistency
• Identify, document, and resolve data issues across ingestion, transformation, and analytics layers

• Implement QA testing strategies for AI and machine learning models
• Validate model inputs, outputs, and performance against defined requirements
• Work closely with data scientists and engineers to ensure reliable and trustworthy AI solutions
• Support testing of model retraining, versioning, and deployment pipelines

• Develop and maintain automated testing frameworks for data pipelines and AI models
• Write and maintain test scripts using Python and SQL
• Apply strong data modeling principles to support scalable and testable architectures

• Partner with data engineers, ML engineers, and product teams to improve data and model quality
• Support continuous improvement of QA processes and software development lifecycle
• Document test plans, results, and quality metrics clearly for technical and business stakeholders

• 5–10 years of experience in data engineering, data QA, or software testing
• Hands-on experience testing data pipelines and analytics platforms
• Experience supporting AI/ML model validation and testing

• Strong proficiency in Python and SQL
• Deep understanding of data modeling and data lake architectures
• Experience with automation testing tools and frameworks
• Strong knowledge of machine learning concepts, workflows, and frameworks
• Experience validating AI analytical models and data-driven systems