J Nagarjuna

About J Nagarjuna

Data analytics professional with 5+ years of experience across finance, healthcare, and retail, identifying recurring patterns through statistical modeling, propensity analysis, quantitative and qualitative techniques to drive ROI, capital planning, and reduce churn contributing to $2.8M in optimized reserve allocations. Proficient in complex multi cloud data migrations and Data warehousing from ERP (Oracle, SAP) and CRM’s (Salesforce, HubSpot) systems into platforms including Snowflake, Redshift, Azure Synapse, and BigQuery. Expert in ETL development using ADF, SSIS, and Glue. Experienced in gathering business and technical requirements to deploy end-to-end Business Intelligence solutions through KPI design and dashboarding using Power BI (DAX, RLS) and Tableau (LOD) enabling data backed business strategy.

Experience

1. Data Analytics Engineer (L2)

   State Street

   May 2024 — Present

   architected analytics-ready financial data warehouses in AWS Redshift and Snowflake that supported ad-hoc analysis of over 2 million transactions guiding $1.8 million in capital-flow decisions through repayment-trend analysis and capital-reserve modeling. I deployed a recommendation engine using AWS SageMaker, to forecast portfolio P&L and adjust positions, integrating predictions into QuickSight dashboards to boost trade accuracy by 9% and generate approximately $1.2 million in Q1 gains. Using Alteryx and logistic regression, I developed credit-risk scoring models for claims and property-backed lending portfolios, eliminating $220 K in capital misallocation, and applied clustering and SQL window functions to segment payment modes improving SLA adherence by 22% through targeted KPI monitoring of failed and delayed transactions. I built scalable ETL pipelines with AWS Glue and SSIS to migrate 1.2 million financial records from Oracle ERP, Snowflake, and legacy ledgers, orchestrated via Control-M with SLA-based alerts to raise pipeline reliability by 30%. I delivered RLS enabled Power BI reports using DAX, dynamic sorting, and filtering to visualize KPIs such as claim reserves, NAV, and credit exposure for 30+ stakeholders, accelerating decision-making by 35%, while maintaining production stability through SQL and Python tuning, hash-based reconciliation checks, and JIRA-driven defect management.

Education

• Velagapudi Ramakrishna Siddhartha Engineering College

  Bachelor's degree

• University of Cincinnati

  Graduate student- Master's in Information technology