Dynamic and results-driven Data Engineering Specialist with over 10 years of experience designing, building, and optimizing cloud-native data ecosystems across healthcare, finance, and enterprise domains. Expert in modern lakehouse architectures, scalable ETL/ELT pipelines, and real-time streaming frameworks using Databricks, Snowflake, Spark, and Kafka. Skilled in Azure, AWS, and GCP with deep expertise in data modeling, orchestration, and automation through Airflow, dbt, Terraform, and CI/CD workflows. Recognized for driving modernization, compliance (HIPAA, GDPR, SOC2), and cost optimization while collaborating across global teams. Passionate about enabling AI-driven analytics, governance, and data democratization through robust engineering and scalable architecture.
Description: Architected and migrated on-premise ETL workflows into Databricks + Snowflake lakehouse using Delta Lake and dbt. Structured Bronze–Silver–Gold layers, integrated Apache Atlas, and enforced HIPAA compliance enabling predictive analytics and self-service BI.
Description: Re-engineered legacy batch jobs into modular, cloud-native ETL pipelines using Talend, Python, and Airflow. Integrated Great Expectations-based data validation, reducing recovery time by 50% and improving resilience.
Description: Designed streaming pipelines using Kafka, Spark Streaming, and AWS Lambda integrated with S3 and Snowflake for sub-second fraud detection and analytics.