Meghana Guttameedi

Data Engineer @Rite-Hite

Kansas City, MO, US

Built interactive Power BI dashboards to support credit risk modeling, claims tracking, and engagement analytics, reducing manual data prep time by 33% and boosting adoption among finance and risk executives by 21%. Developed scalable PySpark pipelines in Databricks to integrate and standardize 12+ TB of policyholder, claims, and CRM data weekly, cutting ML model training time by 38% and enabling faster risk scoring and underwriting decisions. Designed secure, event-driven ETL workflows using AWS Glue, Lambda, and Step Functions to process 15M+ monthly banking transactions, improving data availability for fraud detection and claims teams by 65%. Leveraged the Hadoop ecosystem (HDFS and Hive) to extract historical transaction and policy data, apply distributed transformations using PySpark, and load outputs into Snowflake staging layers, improving data readiness and reducing batch pipeline runtime by 34% for downstream analytics. Constructed Snowflake data marts using normalized and denormalized models, implementing role-based views and secure joins to support finance, underwriting, and claims reporting, which improved data governance traceability and reduced audit reconciliation efforts by 27%. Implemented Change Data Capture (CDC) on PostgreSQL and SQL Server using AWS DMS, keeping Snowflake in near real-time sync (under 5s latency) and enabling accurate 360-degree member profiles for personalized product offerings. Tuned Snowflake compute/storage by applying clustering keys, auto-suspend policies, and materialized views, reducing monthly cloud costs by 28% and improving dashboard refresh speed by 41%. Integrated AWS Glue Data Catalog with Informatica EDC to track metadata, schema changes, and lineage across 200+ financial datasets, reducing manual compliance efforts during internal and regulatory audits by 40%.