Farooq User

Architected and maintained enterprise-scale Lakehouse platforms supporting clinical, financial, and operational analytics for millions of healthcare records. Led scalable ETL/ELT and streaming pipelines with Databricks/Spark, Delta Lake, and Kafka, improving distributed processing performance and reducing execution time and operational overhead.

Architected and led enterprise-scale data platforms using Databricks, Spark, Python, SQL, Snowflake, and Azure, optimizing pipelines that process millions of healthcare records and improving release velocity with CI/CD automation.

Architected and maintained an enterprise Lakehouse platform supporting clinical, financial, and operational analytics; optimized distributed pipelines with Databricks and Spark to improve performance and reliability while enforcing HIPAA-compliant governance.

Designed a cloud-native Snowflake data warehouse integrating AWS and Azure, consolidating data from 25+ heterogeneous sources and building scalable ETL/ELT pipelines processing 10+ TB/day. Implemented real-time Kafka/Kinesis streaming, improved reporting performance 55%, reduced monthly cloud costs by $45K, and cut production incidents 70% through DataOps CI/CD and automated testing.

Designed and maintained high-performance ETL/ELT pipelines integrating relational and NoSQL sources, APIs, and cloud storage in Azure, improving pipeline reliability, scalability, and analytics readiness.

Designed and developed enterprise ETL/ELT pipelines integrating APIs, relational databases, cloud storage platforms, and NoSQL systems. Built scalable cloud-based data warehouses using Azure, Snowflake, SQL Server, and Databricks, and implemented batch and real-time workflows with Spark, Python, SQL, and Airflow plus automated monitoring, logging, and data-quality validation.

Designed and built scalable ETL/ELT pipelines and cloud data warehouses in Azure to improve analytics performance and operational efficiency, while implementing monitoring and CI/CD to increase deployment reliability.

Built and maintained enterprise-scale data pipelines with Python, SQL, Spark, and Databricks, improving data extraction, transformation, and delivery for analytics and production workloads.

Built and maintained ETL pipelines in Python and SQL to support enterprise reporting and analytics systems. Performed extraction, transformation, cleansing, validation, and reconciliation, monitored batch processing jobs, and created automated reporting workflows with supporting documentation.

Assisted in building and maintaining ETL pipelines using Python and SQL, performing data validation, cleansing, reconciliation, and operational monitoring to support enterprise analytics.

Built and maintained ETL pipelines using Python and SQL to support enterprise reporting, performing extraction, transformation, validation, and batch job monitoring to ensure data accuracy and reliability.

Built and maintained ETL pipelines using Python and SQL supporting enterprise reporting and analytics. Performed data extraction, transformation, cleansing, validation, and reconciliation; monitored batch processing jobs; and created automated reporting and data-integration workflows with supporting documentation and operational procedures.

Bachelor of Science in Computer Science completed; focused on foundational computer science and software engineering principles.

Completed a Bachelor of Science in Computer Science.