Amit Timilsina

• Possess over 5 years of progressive experience in architecting, developing, and administering enterprise-grade relational databases using Microsoft SQL Server 2022, 2019, and 2017, with a focus on scalability, high availability, and performance. • Authored and maintained optimized T-SQL scripts including stored procedures, user-defined functions, triggers, indexed views, and Common Table Expressions (CTEs), improving code efficiency, reusability, and performance across critical business operations. • Utilized advanced features of SQL Server Management Studio (SSMS), SQL Server Profiler, and SQL Server Agent to schedule jobs, monitor system activities, and diagnose performance bottlenecks in real time. • Applied advanced data modeling and normalization techniques—up to Third Normal Form (3NF)—to design efficient OLTP systems with minimized redundancy and enhanced referential integrity. • Developed and supported stored procedures, triggers, and packages using Oracle PL/SQL in hybrid RDBMS environments, ensuring consistent business logic implementation across platforms. • Designed and reviewed scalable relational and dimensional data models using data modeling tools like Erwin, aligning database architecture with complex and evolving business processes. • Led enterprise-wide data profiling, cleansing, and scrubbing efforts to ensure data quality and readiness for analytics, reporting, and regulatory compliance purposes. • Applied normalization and de-normalization strategies judiciously to optimize data model structures, balancing performance needs in OLTP systems with aggregation requirements in OLAP environments. • Managed late arriving dimension and fact scenarios by implementing surrogate key logic, change capture techniques, and audit logging to ensure temporal accuracy and reporting integrity in data warehouses. • Developed Star and Snowflake schema-based dimensional models to support scalable business intelligence solutions, enabling high-performance ad-hoc querying and trend analysis. • Led data mapping initiatives to align legacy data structures with modern schema designs, implementing transformation logic and business rules for data migration using SQL and ETL frameworks. • Designed and implemented SSIS-based ETL workflows supporting Full, Incremental, and Re-migration strategies across staging, operational data stores (ODS), and enterprise data warehouses. • Engineered modular, reusable SSIS packages incorporating data flow transformations such as Merge Join, Conditional Split, Derived Column, Lookup, and Bulk Insert to support