Syamson Polepalli

WORK HISTORY

EDUCATION

ABOUT SYAMSON POLEPALLI

Create and configure Snowpipe to enable continuous data loading from Oracle, Salesforce, and file-based sources (JSON, CSV) into Snowflake tables. • Use the COPY command for bulk data loading, ensuring high-performance and scalable data ingestion for large datasets. • Troubleshoot and resolve any issues related to data ingestion, transformation, or loading processes. • Develop common data Ingestion framework to load various source data formats to Data Lake using Control-M and UNIX scripting. • Leverage Snowflake’s Secure Data Sharing feature, which allows sharing of data without physically copying or transferring it, ensuring efficiency and cost-effectiveness. • Designed and implemented strategies to flatten semi-structured data (e.g, JSON, XML, or nested arrays) stored in Snowflake, transforming it into a structured, query-friendly format. • Identified key nested fields and arrays within semi-structured data to determine the optimal flattening approach. • Build robust ETL pipelines in Snowflake to handle SCD Type 1 and Type 2 scenarios, ensuring efficient data extraction, transformation, and loading. • Use Snowflake’s MERGE statement to handle Upserts (update or insert) for SCD Type 1 and Type 2, ensuring data accuracy and consistency. • Utilized Time Travel to create zero-copy clones of tables and schemas at specific points in time, enabling efficient testing and development without impacting production data. • Created separate virtual warehouses for different teams and workloads (e.g, development, testing, production) to ensure resource isolation and prevent contention. • Designed and implemented cluster keys on large tables to optimize query performance by physically organizing data based on frequently queried columns. • Evaluated and selected appropriate clustering keys to minimize micro-partition scanning and improve query efficiency. • Restored dropped objects (tables, schemas) within the retention period, ensuring data continuity and reducing operational risks. • Configured dbt (data build tool) to integrate with Snowflake, setting up connections, profiles, and environments for seamless data transformation workflows. • Established best practices for dbt project structure, including modularization, version control, and documentation. • Implemented staging models to cleanse and standardize raw data, followed by intermediate and core models to build business logic and aggregations. • Wrote efficient and optimized SQL queries within dbt to perform complex data transformations, including joins, aggregations, and window functions.