Senior Data Engineer at Johnson & Johnson

Designed a cloud-native Azure Lakehouse architecture using Azure Data Factory, Azure Databricks, ADLS Gen2, and Azure Synapse to support enterprise clinical and commercial analytics. Built scalable ETL/ELT ingestion frameworks with Python and SQL to onboard data from clinical trials, ERP, and external APIs. Implemented Delta Lake with ACID, schema evolution, and time travel; developed PySpark-based data processing pipelines for large-scale datasets; designed dimensional models (star and snowflake) for downstream analytics in Power BI and Tableau. Enabled near real-time ingestion via Azure Event Hubs and Structured Streaming. Built reusable data-validation libraries; implemented data quality frameworks; tuned Databricks clusters for performance; enforced RBAC and encryption; integrated Snowflake for downstream consumption; built CI/CD pipelines with Azure DevOps; orchestrated workflows with ADF triggers and Databricks Jobs; monitored with Azure Monitor; automated tasks with PowerShell a

Senior Data Engineer at Freddie Mac

Architected an enterprise risk analytics platform using Azure Databricks, Azure Data Factory, and Azure Data Lake. Built high-volume ETL/ELT pipelines to ingest loan, servicing, and market datasets (batch and near-real-time). Developed Delta tables to support regulatory reporting and auditability; created RESTful and SOAP API integrations with Python, WSDL, and Azure Logic Apps, secured with Azure AD/OAuth. Tuned Spark jobs for improved performance; designed dimensional models (star/snowflake) for risk, pricing, and portfolio analytics; built curated data marts for Power BI and Tableau dashboards; automated workflow orchestration with Apache Airflow and cloud schedulers; implemented data validation, governance, lineage, and metadata management for regulatory compliance; supported ad-hoc analytics via Databricks SQL Analytics; integrated Teradata legacy systems; automated deployments via Git CI/CD; provided production support and performance troubleshooting; collaborated with risk analy

Data Engineer at Insurance Services Office

Designed and implemented an enterprise-scale insurance analytics data platform using Hadoop, Apache Spark, and Snowflake to support risk modeling, underwriting, pricing, and regulatory reporting. Built end-to-end ETL/ELT pipelines with Python, SQL, and Spark to ingest high-volume policy, claims, and third-party data. Migrated large datasets from Teradata to Snowflake using Sqoop and Python; implemented SnowPipe and SnowSQL for continuous ingestion; parsed JSON and XML feeds; designed dimensional models (star and snowflake) for underwriting and claims analytics; automated workflow orchestration with Apache Airflow and Oozie; implemented data quality checks, reconciliation logic, and exception handling; implemented IAM, RBAC, and encryption for secure access; built analytics-ready datasets for Tableau; supported feature engineering for actuarial and predictive risk models; contributed to cloud migration and modernizing legacy platforms; provided production support and incident resolution