Gagan Sharma is an experienced Data Engineer specializing in cloud-based data solutions. With over 10 years of experience, he has developed expertise in GCP services, ETL pipeline development, and data security compliance. Gagan has successfully led projects in retail, finance, healthcare, and IoT, demonstrating his ability to deliver scalable and secure data platforms. He is passionate about leveraging data to drive business insights and improve operational efficiency.
Led design and implementation of a petabyte-scale data lake on GCP.
Optimized query execution times by 40% for e-commerce analytics.
Developed real-time fraud detection system processing millions of transactions daily.
Led design and implementation of a petabyte-scale data lake.
Optimized query execution times by 40%.
Successfully migrated a legacy on-premise data warehouse to GCP.
Overview: Designed and implemented a petabyte-scale data lake on GCP for enterprise data analytics and reporting. Responsibilities: Developed scalable ETL pipelines using Apache Airflow and Python. Implemented data security protocols for a healthcare analytics platform.
Key outcomes:
Successfully designed and implemented a petabyte-scale data lake.
Overview: Developed a real-time fraud detection system for a banking institution to identify suspicious transactions and prevent fraud. Responsibilities: Designed a data pipeline for processing millions of transactions daily. Implemented machine learning models for fraud pattern detection.
Key outcomes:
Designed a data pipeline processing millions of transactions daily.
Overview: Implemented data security protocols for a healthcare analytics platform. Responsibilities: Designed encryption mechanisms for PII data. Ensured compliance with HIPAA and GDPR.
Key outcomes:
Successfully designed encryption mechanisms for PII data.
Healthcare Data Security Compliance — encryption + RBAC + HIPAA/GDPR + PII data masking.
Key outcomes:
Successfully designed encryption mechanisms for PII data.
Ensured compliance with HIPAA and GDPR regulations.
Predictive Analytics for Finance — financial risk + NLP sentiment + ML features.
Key outcomes:
Developed predictive analytics models for financial risk assessment.
Integrated NLP techniques for sentiment analysis.
Optimized query performance and cost efficiency.
Gagan Sharma
Data Engineer