Anish Krishna Kalisetti
Senior Business Data Analyst
- Role
- Senior Business Data Analyst at Ibx
- Location
- New Bedford, MA, US
- LinkedIn followers
- 500 followers
About Anish Krishna Kalisetti
Experienced Senior Business Analyst with 9+ years of expertise in Healthcare, Financial…
Experience
Senior Business Data Analyst
Dec 2022 — Present · Philadelphia, PA, US
Designed and optimized stored procedures, functions, and triggers using T-SQL for claims data validation, reconciliation, and real-time processing in high-volume SQL Server healthcare environments.• Built and maintained SSIS packages for automated extraction, transformation, and loading of large-scale healthcare data, integrating multiple sources such as Epic, Salesforce, and Cerner systems.• Developed Azure Data Factory (ADF) pipelines to orchestrate data ingestion and movement from external APIs and flat files into centralized data marts and Azure SQL environments.• Authored complex SQL queries and views for EDI transaction reporting (837, 835, 270/271), significantly improving reporting accuracy, eligibility validation, and remittance audit processes.• Automated ETL workflows for Medicaid claims and provider payments using Python scripts and SSIS, ensuring 99% success rate in batch operations with exception handling.• Collaborated with MDM teams to standardize Common Data Elements (CDEs) across ERP systems (SAP, Epicor, Dynamics 365), improving data consistency and master record accuracy.• Designed Power BI dashboards with advanced DAX logic and drill-through features for Medicaid KPIs, provider payments, and claims denial analytics, enhancing data-driven decision-making.• Created and maintained real-time APIs using C# and.NET Core, enabling seamless integration between data repositories, third-party apps, and Power BI for interactive visualization layers.• Integrated REST and GraphQL APIs for secure transmission of member eligibility and payment data across Salesforce CRM and healthcare platforms, supporting operational automation.• Conducted database indexing, partitioning, and performance tuning to improve SQL Server and Azure SQL query performance, reducing dashboard load times by 40%.
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