Pranay Rishith

Developed PySpark ETL pipelines in Databricks to ingest, cleanse, and transform payment datasets for financial reporting and analytics. Implemented incremental processing with Delta Lake MERGE operations to efficiently handle new and updated payment records. Built dbt models in Snowflake, added SQL and dbt data quality checks, and optimized Snowflake transformations using execution-plan analysis. Developed Python validation and reconciliation logic, and supported reliable pipeline runs with Databricks workflows, monitoring, and failure troubleshooting.

Built SQL queries using joins, CTEs, and window functions to extract and transform retail datasets for operational reporting. Standardized and cleaned retail data with Python and pandas, then delivered structured datasets to reporting layers. Created Power BI dashboards with DAX measures and data model relationships to support KPI tracking and exception analysis. Supported Azure Data Factory ingestion by monitoring executions, validating mappings, and troubleshooting failed loads, while also using PySpark transformations for larger datasets.