Data & AI Leader

I lead architecture and strategy discussions for building, scaling, and optimizing big data platforms that support analytics, business intelligence, AI, and machine learning use cases. I define platform observability concepts, including monitoring, alerting, and data-quality signals, to improve reliability, incident response, and stakeholder trust.

I established secure software development lifecycle practices, release-management processes, coding and deployment standards, and testing strategies. I collaborate on Microsoft Purview-based data governance, supervise data migration and backfilling, lead AI enablement with semantic layers and Databricks Genie, mentor data engineers, and contribute to budgeting, cost optimization, and vendor selection.

I optimized cloud infrastructure through process improvements and pipeline refactoring, reducing monthly expenses by 15%. I implemented a modern Lakehouse medallion architecture using Bronze, Silver, and Gold layers, leveraging Delta Lake ACID compliance, versioning, and schema evolution.

I designed and built scalable enterprise ETL/ELT pipelines with Azure Data Factory and Databricks for large-scale SCADA data ingestion and transformation of more than 100 GB per day. I optimized Delta tables through clustering, partitioning, and file compaction, promoted reusable environment-agnostic code and CI/CD practices, and collaborated with analytics, data science, and business stakeholders on BI and machine-learning data models.

I delivered data-pipeline modernization initiatives by migrating legacy ETL solutions to Azure, improving performance, reliability, security, and governance. I designed conceptual, logical, and physical enterprise data models that improved reporting consistency and enabled scalable analytics solutions.

I streamlined metadata and master-data-management processes into a unified automated ETL process, improving data quality and trust. I also integrated Control Center Application data using REST APIs and SSIS.