Farooq User

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.

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.

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 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 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.

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.

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