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# Data Engineer 3 4P/499

- **Company:** 4P Consulting Inc.
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Testing (Software), Artificial Intelligence, Business Analytics Applications, Data Analysis, Automation of Tests, Information Engineering, Data Integrity, Python (Programming Language), Machine Learning, Standard Sql, Software Engineering, Data Streaming, Management of Software Versions, Delivery Pipeline, Test Scripts, Data Lakes, Data Analytics, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** October 16, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-3-4p-499-atlanta-ga--9fdd5274-514d-4f93-b042-5bcf5aaf0c54

## About the Role

* 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

Technical Skills

 * 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

## Description

Data Engineer 3 - Quality Assurance (AI & Data Platforms)Location- Atlanta, GaClient- Southern Company GasContract- 10 MonthsPosition OverviewWe 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.Key ResponsibilitiesData Quality & TestingDesign, develop, and execute data quality tests for data moving through relational systems and data lakesMonitor, validate, and enforce data integrity, accuracy, and consistencyIdentify, document, and resolve data issues across ingestion, transformation, and analytics layersAI & Machine Learning QAImplement QA testing strategies for AI and machine learning modelsValidate model inputs, outputs, and performance against defined requirementsWork closely with data scientists and engineers to ensure reliable and trustworthy AI solutionsSupport testing of model retraining, versioning, and deployment pipelinesAutomation & EngineeringDevelop and maintain automated testing frameworks for data pipelines and AI modelsWrite and maintain test scripts using Python and SQLApply strong data modeling principles to support scalable and testable architecturesCollaboration & Continuous ImprovementPartner with data engineers, ML engineers, and product teams to improve data and model qualitySupport continuous improvement of QA processes and software development lifecycleDocument test plans, results, and quality metrics clearly for technical and business stakeholdersExperience5-10 years of experience in data engineering, data QA, or software testingHands-on experience testing data pipelines and analytics platformsExperience supporting AI/ML model validation and testingTechnical SkillsStrong proficiency in Python and SQLDeep understanding of data modeling and data lake architecturesExperience with automation testing tools and frameworksStrong knowledge of machine learning concepts, workflows, and frameworksExperience validating AI analytical models and data-driven systems
Data Engineer 3 - Quality Assurance (AI & Data Platforms)
Location- Atlanta, GaClient- Southern Company GasContract- 10 MonthsPosition OverviewWe 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.Key ResponsibilitiesData Quality & TestingDesign, develop, and execute data quality tests for data moving through relational systems and data lakesMonitor, validate, and enforce data integrity, accuracy, and consistencyIdentify, document, and resolve data issues across ingestion, transformation, and analytics layersAI & Machine Learning QAImplement QA testing strategies for AI and machine learning modelsValidate model inputs, outputs, and performance against defined requirementsWork closely with data scientists and engineers to ensure reliable and trustworthy AI solutionsSupport testing of model retraining, versioning, and deployment pipelinesAutomation & EngineeringDevelop and maintain automated testing frameworks for data pipelines and AI modelsWrite and maintain test scripts using Python and SQLApply strong data modeling principles to support scalable and testable architecturesCollaboration & Continuous ImprovementPartner with data engineers, ML engineers, and product teams to improve data and model qualitySupport continuous improvement of QA processes and software development lifecycleDocument test plans, results, and quality metrics clearly for technical and business stakeholdersExperience5-10 years of experience in data engineering, data QA, or software testingHands-on experience testing data pipelines and analytics platformsExperience supporting AI/ML model validation and testingTechnical SkillsStrong proficiency in Python and SQLDeep understanding of data modeling and data lake architecturesExperience with automation testing tools and frameworksStrong knowledge of machine learning concepts, workflows, and frameworksExperience validating AI analytical models and data-driven systems