Rebyl Peddity
I’m a data analyst with 4+ years of experience turning large, complex datasets into clear, actionable insights.
About
I’m a data analyst with 4+ years of experience turning large, complex datasets into clear, actionable insights. I work across SQL, Python, Power BI, Tableau, and Excel to perform data profiling, validation, reconciliation, KPI reporting, and trend/variance analysis—especially in healthcare and operations contexts.
Most recently, I’ve been working on data quality and geospatial analytics for Apple Maps, focusing on identifying inconsistencies, duplicates, and gaps and then building dashboards to track quality metrics over time. Previously at Optum and Amazon, I supported healthcare claims/EHR reporting and operational performance analytics, improving accuracy and reducing reporting cycle times by building reusable query and BI workflows.
- 4 years' experience
- Joined October 2026
Industry experience
- Healthcare
- Retail
- Professional Services
More experts in Austin, United States
Work experience
Query and evaluate large-scale geospatial datasets with SQL to identify errors, gaps, and inconsistencies across roads, points of interest, addresses, and geographic boundaries. Clean and prepare datasets using Python and Pandas to improve consistency for reporting and validation. Validate mapping records against internal GIS tools and source imagery. Perform data profiling, reconciliation, and quality checks to detect duplicate, missing, inaccurate, and inconsistent records. Build Tableau dashboards to monitor data quality trends, error rates, regional patterns, correction volumes, and KPIs; segment issues by region and feature type to prioritize fixes. Conduct root-cause analysis on recurring discrepancies and collaborate cross-functionally to clarify requirements and communicate recommendations.
Analyze healthcare claims, EHR/EMR, Medicare, Medicaid, and pharmacy data using SQL, Python, Pandas, Power BI, and Excel. Develop Power BI dashboards to monitor healthcare utilization, costs, HEDIS measures, risk adjustment, and member outcome KPIs. Analyze ICD-10-CM, CPT, HCPCS, and DRG classifications to identify utilization trends, coding discrepancies, and reporting exceptions. Audit 5M+ healthcare records to find anomalies and data discrepancies, improving operational reporting accuracy. Reduce recurring reporting cycles by 25% by building reusable SQL queries, optimizing Excel models, and standardizing BI workflows. Reconcile datasets using PivotTables, variance analysis, conditional logic, and data validation; document requirements and work in Agile/Scrum using Jira.
Analyze customer orders, delivery performance, fulfillment metrics, and operational KPIs using SQL, Python, Excel, and Amazon QuickSight. Build interactive QuickSight dashboards for regional metrics, order accuracy, delivery performance, fulfillment trends, and KPIs; use Tableau to compare performance across regions and order segments. Use Python/Pandas and statistical analysis plus Excel VBA to identify operational trends, gaps, and exceptions. Review 1M+ order records to uncover delivery/fulfillment patterns and support improved exception reporting. Reduce manual reporting timelines by 20% by optimizing SQL queries, automating Excel reporting, and standardizing QuickSight workflows. Track tasks and dependencies using Jira in Agile/Scrum and partner with operations teams to translate metrics into actionable insights.
Education
Certifications and awards
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