Job Summary:
The Data Engineer III independently designs, develops, implements, and supports complex enterprise data pipelines, data models, and analytical solutions that deliver reliable, actionable information to the business. This role contributes to solution architecture, strengthens data quality and governance, and applies hands-on artificial intelligence (AI) experience to build AI-ready data assets and integrate AI capabilities into data engineering workflows. The Data Engineer III owns technical delivery within assigned initiatives, resolves non-routine problems, and collaborates with business and technology partners within established architecture, security, and governance standards.
Essential Functions:
Independently design, develop, test, deploy, and support scalable ETL/ELT pipelines, data models, and analytical data assets; improve performance, reliability, and maintainability throughout the delivery lifecycle.
Serve as a key contributor to data stewardship workgroups and cross-functional teams supporting data management and governance.
Promote metadata management and data quality practices through knowledge-sharing sessions, technical guidance, and collaboration across the organization.
Translate stakeholder priorities, business definitions, and data quality requirements into appropriate data models, technical specifications, and engineering solutions.
Own the technical delivery of assigned data governance engineering initiatives, including metadata cataloging and data quality solutions, within established program standards.
Analyze data and business processes to develop and maintain glossary definitions, policies, standards, guidelines, and workflows that support data management and governance programs.
Lead the development and maintenance of metadata catalogs for assigned data domains, including technical metadata, business definitions, and data lineage.
Identify and quantify data-related issues, assess business impact, and collaborate with stakeholders to develop and implement remediation plans.
Partner with subject matter experts, end users, and data modelers to define business rules and implement automated controls that measure and improve data quality.
Lead the investigation and resolution of reported data quality issues; develop and report metrics that demonstrate remediation progress, data quality trends, and adherence to established standards.
Set objectives and manage technical delivery for assigned work, accounting for dependencies and impacts on related teams. Contribute to operational goals and work independently within established procedures, seeking direction for new domains or unique assignments.
Resolve ambiguous, non-routine technical problems through analysis, data trends, and sound engineering judgment. Improve work methods based on experience, engage appropriate resources, and escalate risks or decisions outside established boundaries.
Build and maintain governed, AI-ready datasets and pipelines for AI and machine learning use cases, including data preparation, validation, lineage, and access controls; collaborate with data science and application teams on integration requirements.
Implement and support approved AI capabilities within data engineering solutions, such as AI-assisted data quality checks, metadata enrichment, or unstructured data processing. Validate outputs against defined acceptance criteria and monitor accuracy, reliability, latency, and cost.
Use approved AI-assisted development tools to support coding, testing, troubleshooting, and documentation. Independently review and test generated outputs, protect sensitive data, and follow established security, privacy, and responsible AI requirements.
Perform any other job duties as requested
Education and Experience:
Bachelor’s degree in Computer Science or a related field, or equivalent years of relevant work experience, is required.
A minimum of five (5) years of experience designing and developing ETL/ELT and/or business…