Design and develop automated metadata harvesting architecture
Build Python/SQL-based data pipelines for metadata extraction from diverse systems
Integrate AI tools for intelligent metadata classification and tagging
Implement Human-in-the-Loop (HITL) validation workflows for AI-assisted harvesting
Deploy harvesting automation across enterprise systems for scalable metadata capture
Develop drift detection and alerting mechanisms for metadata change management
Create and maintain technical documentation for all automation components
Support empirical testing in sandbox environments for technology evaluations
Optimize harvesting processes to achieve efficiency gains
Coordinate with QA teams on automation quality standards
Troubleshoot and resolve pipeline issues across production environments

Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or related field
Minimum 5 years of experience in Python development and SQL
Experience with AI/ML integration or automation frameworks
Proficiency with data pipeline tools (Apache Airflow, Prefect, or similar)
Experience with cloud platforms (AWS, Azure) and their AI/ML services
Strong understanding of ETL/ELT processes and data integration patterns
Experience with version control (Git) and CI/CD practices

Automation Engineer

Key skills