Senior Data Quality Engineer - AI-Driven Data Quality & Pipelines

Coca-Cola Bottling Co. Consolidated; Charlotte (NC); On-site; USD 90,000 - 120,000; Full time

Benefits offered by this job

Career growth

Competitive benefits

Purpose-driven

Professional development

Job summary

Coca-Cola Bottling Co. Consolidated is seeking a Data Quality Engineer III to design and implement advanced data quality frameworks and automated validation across enterprise datasets.

The role blends data engineering with data quality, ensuring reliable data for analytics and AI use cases. You will develop scalable pipelines, perform profiling and anomaly detection, and apply AI-driven techniques to improve monitoring and rule generation.

Qualifications

• Bachelor's degree (4 years) required.
• 5 to 7 years of experience in data quality roles.
• Bachelor's in Computer Science or Business Administration preferred.

Responsibilities

• Design, build, and maintain scalable data quality pipelines and rules.
• Develop data pipelines and orchestration to support ingestion and quality enforcement.
• Perform advanced data profiling and root cause remediation.
• Implement automated monitoring and anomaly detection for data quality.
• Collaborate with data governance and business teams on standards.
• Create reusable data quality patterns and best practices.
• Explore AI/ML-driven data quality solutions, including intelligent rule generation.
• Perform other duties as assigned.

Skills

SQL and data manipulation

Python programming

Data quality rules design

ETL/ELT pipelines

Data profiling and anomaly detection

Data governance tools

Reusable data quality patterns

Structured and semi-structured data

Troubleshooting / root cause analysis

Education

Bachelor's degree

CS or Business Administration degree preferred

Tools

Alation

Palantir

Informatica

Job description

Coca-Cola Bottling Co. Consolidated is seeking a Data Quality Engineer III to design and implement advanced data quality frameworks and automated validation across enterprise datasets.

The role blends data engineering with data quality, ensuring reliable data for analytics and AI use cases. You will develop scalable pipelines, perform profiling and anomaly detection, and apply AI-driven techniques to improve monitoring and rule generation.