End-to-End Data Pipeline Design and Implementation
Building distributed data pipelines on Azure Databricks platform, implementing complete ETL/ELT processes.
Leveraging pySpark for enhanced large-scale parallel processing capabilities.
Generative AI-Driven Data Enhancement
Integrating LLM/Generative AI into data flows for automated label generation, anomaly detection suggestions, text summarization, and data quality assessment.
Using AI models for preprocessing and tendency analysis to enhance downstream analytical model accuracy and efficiency.
Code Repository and Development Process Management
Managing all project code using GitHub private repositories, combined with Gitflow or trunk-based workflows to ensure version control and team collaboration standards.
Implementing high-standard CI/CD processes with automated testing and deployment to ensure code quality and deployment efficiency.
Architecture Design and Technical Governance
Adopting modular, reusable data architecture design to support horizontal scaling and future functionality expansion.
Implementing end-to-end monitoring and logging mechanisms, combined with Terraform or Azure DevOps Pipelines for IaC, environment configuration management, and security protection.