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Vineet Venkata Kalluri

Business Intelligence Analyst • Arlington, TX • v****************@gmail.com • 682****359 • linkedin.com/••••• • drivetube.ai/•••••

Professional Summary

Business Intelligence Analyst with 6+ years of experience delivering BI, data engineering, and analytics solutions for healthcare, banking, and HR SaaS domains. Expert in T-SQL, Power BI (DAX, Power Query, RLS), Microsoft Fabric, Databricks (PySpark), and dimensional data modeling. Proven record of designing HIPAA-compliant pipelines, optimizing query performance, and producing audit-ready reports that reduce manual work and improve decision-making.

Technical Skills

Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy,Scikit-learn
Databases: T-SQL,SQL Server,MySQL,Oracle SQL,PostgreSQL
Cloud and DevOps: Microsoft Fabric,Azure Data Factory,Azure Synapse,Databricks,Azure Data Lake Storage Gen2,Databricks Delta Lake
Data and Analytics: Power BI,DAX,Power Query M,Tableau,SSRS,Excel,Star Schema,Snowflake Schema,Dimensional Modeling,ER Modeling,Data Warehousing
Tools and Methodologies: HIPAA,PHI Handling,Data Lineage,Data Quality Frameworks,Row-Level Security,Azure DevOps,Git
Skills: PySpark
ETL, OLAP & BI Engine: SSIS,SSAS,OLAP,ETL control frameworks

Work Experience

McLaren Health Care
Remote
Business Intelligence Analyst II
Jan 2025 – Present
Integrated BI and data engineering for a large regional health system; supported claims, member, billing, Medicare/Medicaid and quality reporting.
Tech Stack: T-SQL, SQL Server, HealthEdge, Microsoft Fabric, Azure Data Factory, PySpark, Power BI, DAX, Power Query, Row-Level Security, HIPAA, Azure DevOps
  • Engineered HIPAA-compliant T-SQL queries, stored procedures and indexed views across HealthEdge modules (Claims, Member, Billing) to extract PHI-containing claims data, cutting report generation time by 30% and enforcing audit-ready access controls.
  • Tuned complex SQL jobs and implemented targeted indexes to reduce runtimes from 30+ minutes to under 3 minutes for daily claims and eligibility feeds, improving SLA adherence for clinical and operations teams.
  • Owned requirements, data modeling, and dashboard delivery for VBC and clinical quality programs; translated clinical rules into curated datasets and reports that reduced manual chart review by 25%.
  • Designed fact and dimension tables in the Fabric Lakehouse using star-schema modeling to standardize metrics across claims, eligibility, and provider domains, reducing reporting defects by 20%.
  • Defined KPI logic for utilization, quality, provider performance and pediatric gap-closure metrics and built Power BI reports (DAX, Power Query, Direct Lake) to improve refresh performance and reduce clarification requests.
  • Developed ingestion pipelines for claims, eligibility and provider datasets: extracted from HealthEdge, orchestrated incremental loads in Fabric Data Factory, validated PySpark transformations and migrated curated SQL datasets into Fabric Lakehouse models.
US Bank
Texas
Business Intelligence Developer
May 2024 – Jan 2025
Supported fraud and risk reporting for a major U.S. bank by integrating cloud data lakes and delivering audit-ready BI for compliance teams.
Tech Stack: Power BI, DAX, Databricks, Databricks Delta Lake, Azure Data Lake Storage Gen2, PySpark, SQL
  • Enhanced Power BI reporting for the fraud team by integrating curated datasets from Databricks Delta Lake and ADLS Gen2, designing a semantic model and optimizing DAX to improve data load performance by 28%.
  • Restructured fragmented fraud datasets into a star schema using SQL, replacing slow multi-join legacy flows and improving query performance by 22% while standardizing KPI definitions across risk and onboarding teams.
  • Implemented SQL validation checks and ETL control rules to detect schema drift, missing fields and inconsistent account mappings, reducing compliance and risk reporting errors by 35%.
  • Automated ingestion of customer, account and transaction feeds into Databricks Delta Lake using PySpark workflows, improving pipeline reliability and reducing manual refresh effort by 32%.
  • Supported production reporting through anomaly validation and record reconciliation for daily fraud risk processing, reducing false-positive investigations and improving SLA adherence.
  • Collaborated with fraud analysts and engineering to produce audit-ready datasets and documentation for compliance reviews, accelerating audit response cycles.
The University of Texas at Arlington
Arlington, TX
Reporting Data Analyst
May 2023 – May 2024
Delivered analytics and executive reporting for the university entrepreneurship center, supporting program evaluation, donor relations and events.
Tech Stack: Power BI, DAX, SSRS, SQL, Python, Pandas, NumPy, Power Query
  • Analyzed venture performance and cohort outcomes using SQL window functions, CTEs and Python (NumPy, Pandas) to build statistical scoring that improved cohort selection accuracy by 22%.
  • Redesigned Power BI executive dashboards integrating CRM and event-registration data, built a streamlined semantic model and optimized DAX measures to reduce refresh failures by 40%.
  • Built automated SSRS reports backed by SQL stored procedures to support entrepreneurship programs, reducing manual data pulls and recurring reporting workload by 38%.
  • Gathered cross-functional requirements and translated informal needs into SQL-ready datasets and measurable KPIs, reducing requirement ambiguity and rework by 30%.
  • Converted ad-hoc analyses into repeatable ETL processes, migrating manual Excel workflows into automated SQL and Power BI pipelines to increase reliability.
  • Adapted quickly to new reporting platforms and stakeholder needs, delivering timely datasets that supported strategic planning and donor reporting.
Westpac Banking Corp (via TCS)
India
Risk Reporting BI Analyst
Apr 2021 – Aug 2022
Worked on risk and credit reporting for a major retail bank via an IT services provider; supported reporting used by fraud, credit-risk and compliance teams.
Tech Stack: Power BI, SQL, Azure BI, JSON, APIs, ETL validation
  • Profiled a slow-loading credit-risk report and rebuilt the model into a star schema, cutting load time by 28% and improving access during month-end close.
  • Designed a near real-time Power BI operational dashboard with automated amber-alert thresholds based on transaction-load analysis, replacing manual 4-hour server health checks and eliminating recurring downtime.
  • Developed complex SQL queries, derived tables and multi-table joins to support weekly and monthly risk reporting, delivering audit-ready outputs to senior risk officers.
  • Implemented monitoring and validation controls for Azure BI pipelines, resolving data inconsistencies and improving dashboard refresh stability by 40% during peak transaction periods.
  • Gathered requirements from fraud, credit-risk and compliance teams and translated business rules into SQL logic and analytical datasets to enable faster, more accurate risk assessments.
  • Analyzed JSON logs, API responses and transaction feeds to troubleshoot data quality issues, align timestamp logic and enrich risk datasets to improve fraud-trigger accuracy.
Keka HR
India
Product Business Intelligence Analyst
Aug 2019 – Apr 2021
Built analytics and product reporting for an enterprise HR SaaS platform; enabled product and client teams to track usage, payroll and HR metrics.
Tech Stack: Power BI, DAX, SQL, Star Schema, Dimensional Modeling, SSAS
  • Designed star-schema data models, KPI definitions and SQL metric layer powering the People Intelligence analytics module to enable HR leaders to track workforce trends and payroll accuracy.
  • Partnered with product managers and engineering to translate enterprise client analytics requirements into SQL metric definitions, dimensional data flows and validation rules, reducing engineering rework by 30%.
  • Built Power BI dashboards tracking product usage, feature adoption and module engagement; insights led to prioritization decisions that increased module adoption by 18% among enterprise clients.
  • Conducted deep-dive SQL analyses on client workflow behavior to identify feature drop-offs, low-usage cohorts and regional adoption gaps, enabling Product and Sales to improve onboarding journeys and raise activation rates by 15%.
  • Implemented validation rules and dimensional flows across HRIS, Payroll, Attendance, Performance and ATS modules to ensure metric consistency across reports.
  • Documented SQL-based ETL scripts and metric logic to automate calculations and support enterprise client reporting and onboarding.

Education

University of Texas at Arlington
M.S. Computer Science (Data and Business Analytics) • Arlington, TX • Aug 2022 – May 2024

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