Professional Experience

Key Responsibilities:

• Designed and implemented a scalable Azure Data Lake Gen2 medallion architecture (Bronze, Silver, Gold layers) to support structured data processing and analytics
• Led the setup and configuration of Azure Databricks workspaces across DEV and PROD environments, integrating with Unity Catalog and implementing data lineage via Azure Purview
• Automated data ingestion pipelines using Azure Data Factory and Databricks Autoloader, enabling incremental load and near real-time ingestion of structured and semi-structured data
• Established robust CI/CD workflows using Azure DevOps for infrastructure as code, job deployment, and configuration management of Azure Data Services
• Designed secure networking with VNets, subnets, private endpoints, and firewall rules to ensure isolation and compliance across DEV and PROD subscriptions
• Implemented RBAC and resource locks to protect critical resources and ensured adherence to data governance policies using managed identities and service principals

Technologies:

Key Responsibilities:

• Architected and implemented end-to-end data solutions using Snowflake, establishing enterprise-grade data warehouse architecture
• Led successful migration of legacy SAP models to GCP, utilizing DBT, BigQuery, Airflow, and Cloud Run, achieving 40% improved efficiency
• Designed and implemented IaC using Terraform for automated resource provisioning and access management
• Orchestrated complex data pipelines using Apache Airflow, achieving 99.9% pipeline reliability
• Implemented dynamic data masking and row-level security in Snowflake, ensuring GDPR and HIPAA compliance
• Enhanced multi-cloud data operations leveraging AWS EMR for batch processing and DynamoDB for NoSQL workloads

Technologies:

Key Responsibilities:

• Spearheaded migration from on-premises Teradata/Informatica to GCP, reducing operational costs by 30%
• Designed and implemented cloud-native data warehouse architecture using BigQuery and Cloud Storage
• Developed serverless ETL workflows using AWS Lambda and S3, improving data processing efficiency by 25%
• Implemented automated data quality monitoring and validation frameworks
• Created comprehensive data documentation and governance protocols

Technologies:

Key Responsibilities:

• Designed and implemented cloud-native data architecture using AWS services (Glue, Redshift, S3)
• Developed automated ETL processes reducing manual data processing time by 60%
• Created real-time analytics dashboards for student engagement monitoring
• Implemented data quality frameworks ensuring 99.9% data accuracy

Technologies:

Key Responsibilities:

• Delivered end-to-end BI solutions using Power BI, Tableau, and Data Studio
• Developed standardized reporting templates improving report creation efficiency by 40%
• Led data visualization workshops for stakeholders

Technologies:

Key Responsibilities:

• Developed predictive models for customer behavior analysis
• Created automated reporting solutions reducing manual reporting time by 50%
• Conducted A/B testing and statistical analysis for marketing campaigns

Technologies:

Key Responsibilities:

• Implemented data collection and validation processes
• Developed Python scripts for automated data cleaning and analysis
• Created executive dashboards for KPI monitoring

Technologies: