Ram Darshan

Current project:

Domain: Banking.

Environment: Snowflake, DB, ORACLE, AWS S3.

Roles and Responsibilities:

• Executed data migration from Oracle DB to Snowflake for a major application, utilizing optimized flat files to facilitate the transition between platforms.
• Contributed to Snowflake object development, including the design and deployment of warehouses, schemas, tables, views, pipes, stages, and dynamic scaling policies.
• Managed structured data migration by establishing internal and external stages, configuring various COPY INTO options, and implementing copy on error and robust file format handling.
• Established a security framework and defined access roles for structured data, ensuring controlled migration paths and appropriate user utilization.
• Gained hands-on experience loading complex, unstructured data, leveraging VARIANT data types, FLATTEN functions, and parquet formats for minimal latency during ingestion.
• Developed custom Snowflake procedures, scalar functions, and user-defined table functions (UDTFs) to encapsulate complex, reusable business logic and calculations.
• Created an external table solution to query source data residing in AWS S3 files, overcoming security restrictions where direct file access was initially denied.

PREVIOUS PROJECT:

Domain: Banking.

Role: Snowflake Developer.

Client: Alpha Banking

Environment: Oracle, DB, Snowflake, AWS S3.

Roles and Responsibilities:

• Designed and implemented a suite of PL/SQL packages, procedures, functions, and triggers for retrieving, manipulating, and validating complex banking data sets.
• Engineered robust exception handling routines within PL/SQL code to ensure transactional integrity and accuracy during the critical data migration process.
• Optimized query performance through advanced tuning techniques, including strategic indexing, partitioning, and deep tracing of slow-running queries in Oracle PL/SQL.
• Translated intricate business needs into technical specifications, delivering highly complex SQL queries and subqueries for data conversion and reporting.
• Leveraged sophisticated analytical functions, specialized JOINs, and operators to model and transform data, satisfying diverse reporting and technical requirements.
• Deployed a hierarchical snow task system, utilizing snow stream objects to ensure continuous data backup and integrity tracking during DML operations on the target Snowflake database.
• Configured snow pipes to automate the reading and loading of continuous data feeds from AWS S3, facilitating seamless, low-latency data integration between the source and target environments.