Data Quality Power Bi

Pradeep Kodeboina

Columbus, OH (Open to relocate) +1-857-***-**** ****************@*****.***

Professional Summary:

• Data Analyst with over 7 years of experience specializing in business intelligence, data governance, data quality, and advanced analytics for large-scale projects across medical insurance claims, GIS mapping, and digital operations.

• Proficient in Azure cloud, Cloud Data Quality (CDQ), and Data Governance Cloud (CDGC), ensuring seamless data integration, high data quality, and compliance with regulatory standards.

• Adept at leveraging Power BI, SQL, and Python to develop interactive dashboards, automate workflows, and deliver actionable insights, driving efficiency and cost savings across healthcare, logistics, and digital analytics.

• Configured and optimized SAP BusinessObjects BI 4.3 environments, ensuring reliable enterprise reporting and reducing system downtime by 40%.

• Developed Source-to-Target Mapping (STTM) documents using CDGC, defining transformation rules and business logic to ensure data consistency in healthcare claims processing.

• Implemented advanced Cloud Data Quality (CDQ) rules, identifying data inconsistencies, duplicate claims, and anomalies, reducing claims processing errors by 30%.

• Developed comprehensive Power BI dashboards for healthcare claims trends, provider network performance, and geospatial KPIs, improving operational visibility and fraud detection by 20%.

• Designed ad hoc Power BI dashboards for operational teams, enabling real-time monitoring of claims metrics and geospatial KPIs, reducing SLA breaches by 15%.

• Optimized SQL-based ETL workflows using Azure Cloud (IDMC) and CDQ, reducing data processing times by 25% and ensuring real-time data availability for analytics and reporting.

• Automated recurring reports using Advanced Excel, macros, and VBA, saving over 30% of time on manual reporting tasks.

• Conducted root-cause analyses of data discrepancies using SQL, Python, and CDQ, resolving 90% of data quality issues and improving data accuracy across multiple systems.

• Enhanced predictive analytics capabilities by developing anomaly detection models in Python and integrating insights into Power BI dashboards to improve claims adjudication and fraud detection.

• Developed SQL-based validation scripts with CDQ to reconcile claims transactions, billing records, and provider data, ensuring 99.5% data consistency.

• Improved GIS data integration and visualization for Amazon Maps, saving $200K annually through optimized routing and delivery efficiencies.

• Trained over 50 analysts on Power BI, SQL, and Azure Cloud (IDMC), empowering teams to generate data-driven insights independently and increasing productivity by 20%.

• Created unified reporting frameworks using CDGC, standardizing claims processing reports, provider performance KPIs, and financial reconciliation metrics, improving report generation efficiency by 25%.

• Collaborated with cross-functional teams to align data governance strategies with business objectives, ensuring consistent reporting standards and regulatory compliance.

• Analyzed medical claims data using SQL and CDQ, identifying fraud patterns and anomalies, saving $250K in potential losses and improving claims approval processes by 10%.

• Optimized geospatial reporting frameworks to monitor delivery zones and KPIs, reducing operational costs by $150K annually and increasing data reliability by 95%.

Technical Skills:

Data Visualization Tools

Power BI, SAP BusinessObjects BI 4.3, Advanced Excel (Pivot Tables, VBA, Macros)

Database Tools

Snowflake, SQL SSMS Server, Oracle SQL Developer, TOAD, Postgres

Programming Languages

Python, SQL, PL/SQL

ETL Tools

Informatica PowerCenter, AWS Glue, Azure Data Factory, Promotion Management Tools

Statistical Analysis

Python (Pandas, NumPy), R

Workflow Automation

Python scripting, VBA macros, Power BI Automations

Data Modeling

Dimensional modeling, Star Schema, Snowflake Schema

Scheduling Tools

Control-M, Autosys

Data Governance Tools

Informatica Data Quality, Data Dictionary Maintenance

Methodologies

Agile, SCRUM

Professional Experience:

Client: Nationwide Insurance, Columbus, OH July 2023 – Present

Role: Data Analyst

Responsibilities:

• Performed data profiling on medical and dental claims datasets using Cloud Data Quality (CDQ) to identify data inconsistencies, duplicates, and missing values before loading data into the Data Warehouse (DW), ensuring 99% data accuracy in claims processing and reporting.

• Prepared detailed Functional Specification Documents (FSDs) by collaborating with business and technical teams, outlining business rules, transformation logic, and requirements for claims data extraction, transformation, and loading (ETL) processes using Azure Cloud (IDMC).

• Developed comprehensive Source-to-Target Mapping (STTM) documents using Data Governance Cloud (CDGC), defining joins, filters, ranking logic, and transformation rules for claims and provider data, ensuring accurate and unique record extraction for downstream analytics and reporting.

• Optimized SQL queries for claims processing and medical reimbursement data, reducing query execution time by 30%, improving efficiency in claims adjudication and fraud detection.

• Designed and validated draft Data Models for healthcare claims and provider datasets, ensuring alignment with business objectives, regulatory requirements, and data governance best practices using CDGC before finalizing the Data Warehouse schema.

• Led User Acceptance Testing (UAT) and business checkout sessions, executing SQL-based test scripts and leveraging Cloud Data Quality (CDQ) validation to ensure migrated claims data integrity, provider network consistency, and patient billing accuracy, reducing post-migration defects by 25%.

• Conducted gap analysis between legacy and cloud-based healthcare systems, leveraging IDMC's data integration capabilities to identify data discrepancies, missing attributes, and transformation gaps, ensuring seamless data integration with healthcare payers, providers, and regulatory agencies.

• Performed data extraction and transformation using SQL and Azure Cloud (IDMC), ensuring structured healthcare data (EHR, claims, eligibility, provider, and billing records) is correctly formatted for downstream analytics and operational reporting.

• Developed advanced SQL-based validation scripts and Cloud Data Quality (CDQ) rules to reconcile claims transaction data across multiple source systems, identifying fraudulent patterns, billing anomalies, and duplicate claims, improving fraud detection accuracy by 20%.

• Designed ad hoc and scheduled reports using SQL and BI tools, integrating CDGC for metadata management, enabling healthcare providers and finance teams to track claims reimbursement trends, provider performance metrics, and revenue cycle KPIs.

• Developed dynamic SQL scripts for data extraction from on-premise and cloud-based healthcare systems, ensuring efficient data integration with analytics and reporting platforms while maintaining 99.5% data consistency.

• Standardized documentation for migration workflows, creating technical specifications, process flows, and business logic documentation with CDGC metadata lineage tracking, improving knowledge transfer and accelerating claims processing enhancements.

• Implemented automation for data validation and reconciliation using CDQ, reducing manual efforts in claims processing verification by 40%, increasing operational efficiency and compliance with regulatory audits.

• Conducted data analysis on healthcare claims, member enrollment, and provider networks, identifying trends and anomalies that improved claims adjudication efficiency by 20%.

• Developed complex SQL queries to extract, clean, and analyze medical claims, eligibility, and billing data, ensuring 99% accuracy in financial reconciliation reports, leveraging CDQ for data cleansing.

• Performed data profiling and quality assessments using Cloud Data Quality (CDQ) to detect inconsistencies, duplicates, and missing values in claims and member data, reducing data errors by 30%.

• Created interactive dashboards and reports integrating CDGC metadata management, visualizing claims processing trends, provider performance, and patient demographics, supporting strategic decision-making for healthcare operations.

• Collaborated with business teams, actuaries, and healthcare analysts to translate data insights into actionable strategies, optimizing payer-provider contract negotiations.

• Conducted gap analysis between legacy and cloud-based healthcare data systems, ensuring seamless migration and integration of medical and pharmacy claims data using IDMC.

• Assisted in predictive modeling initiatives for cost containment and fraud detection, leveraging historical claims data and IDMC workflows to identify potential fraudulent billing patterns.

• Documented data dictionaries, source-to-target mappings, and ETL workflows using Data Governance Cloud (CDGC), ensuring standardization in data extraction, transformation, and reporting processes.

Client: Amazon, Hyderabad, India December 2019 – July 2021

Role: Senior Data Analyst

Responsibilities:

• Configured SSMS to support data ingestion and reporting for Amazon Maps, ensuring seamless processing of geospatial datasets.

• Optimized SSMS Server configurations, reducing outages by 40%. Developed Universes in Power BI to handle GIS data queries, enabling advanced reporting capabilities for geospatial analyses.

• Improved data retrieval efficiency by 20%. Migrated over 500 geospatial reports using Promotion Management, ensuring continuity and accuracy in Amazon Maps analytics workflows.

• Built advanced Power BI dashboards for GIS data, enabling visualization of delivery zones, fuel efficiency, and SLA adherence, saving $150K annually in operational costs.

• Developed SQL queries to extract geospatial data from Oracle databases, improving query execution times by 25% for mapping-related analyses.

• Utilized Python for automating GIS data extraction workflows, reducing data processing time by 30% and increasing the timeliness of analytics outputs.

• Designed ad hoc Power BI dashboards for operational teams to monitor geospatial KPIs, such as delivery accuracy and route efficiency, reducing SLA breaches by 15%.

• Created advanced Excel-based tools with macros and Pivot Tables to track GIS data quality metrics, ensuring 95% data accuracy in reports.

• Collaborated with cross-functional teams to improve GIS data integration into the Data Warehouse, streamlining ETL processes and reducing errors by 20%.

• Developed predictive models for route optimization using Python and Power BI, enhancing delivery times and cutting logistics costs by $200K annually.

• Conducted geospatial data analysis using SQL and Power BI, identifying underperforming regions and providing actionable insights to reduce delivery errors by 18%.

• Standardized Power BI dashboards across teams, improving accessibility to geospatial insights and reducing duplication in report creation.

• Performed root-cause analysis of mapping discrepancies using SQL, resolving 90% of reported errors and improving route planning efficiency.

• Trained 20+ analysts in Power BI and Advanced Excel techniques, fostering data-driven decision-making across the Amazon Maps project.

• Designed a unified reporting framework for Amazon Maps, integrating GIS data with operational KPIs, which improved reporting efficiency by 25%.

Client: Amazon, Hyderabad, India Nov 2017 – Nov 2019

Role: Data Analyst

Responsibilities:

• Implemented Power BI tools to manage and analyze GIS data, enabling geospatial reporting for Amazon Maps. Configured Universes to optimize GIS query performance, reducing data retrieval times by 30%.

• Maintained SAP BusinessObjects environments, ensuring data availability for 1M+ daily mapping transactions. Reduced service interruptions by 20% through proactive monitoring.

• Automated migration of 300+ reports across POWER BI environments using Promotion Management tools, maintaining consistency and reliability of geospatial analytics.

• Developed Power BI dashboards for GIS analytics, visualizing delivery zones, fuel consumption, and route efficiency. These dashboards identified inefficiencies that saved $100K annually. Analyzed GIS data using SQL and Python, identifying trends that improved delivery route planning and reduced SLA breaches by 15%.

• Created interactive Excel dashboards with VBA and Pivot Tables for tracking and resolving GIS data errors, improving data accuracy by 25%.

• Designed ad hoc Power BI dashboards for regional teams to monitor performance, enabling real-time adjustments and reducing logistics delays.

• Conducted root-cause analyses on GIS data discrepancies using SQL, resolving 85% of recurring errors and enhancing map accuracy.

• Streamlined ETL processes for GIS data ingestion into the Data Warehouse, reducing pipeline failures by 30%.

• Developed Python scripts for geospatial data cleaning, automating workflows and reducing processing times by 20%.

• Collaborated with operational teams to align Power BI dashboards with Amazon Maps goals, improving stakeholder decision-making through data visualization.

• Enhanced geospatial compliance tracking dashboards to monitor delivery KPIs, ensuring regulatory adherence and improving SLA compliance rates.

• Trained 15 team members in Power BI and SQL best practices, standardizing the development of dashboards across teams and improving productivity.

• Optimized SQL queries for GIS data retrieval, achieving a 25% improvement in query performance and reducing report generation time.

• Developed a unified data governance framework, ensuring consistency in metrics definitions and improving cross-team collaboration.

Client: The Orange Leaf, Hyderabad, India Jun 2016 – Oct 2017

Role: Data Analyst

Responsibilities:

• Configured BI tools to automate campaign reporting, integrating marketing data into dashboards for leadership insights. Optimized Universes to support campaign-level analyses, reducing manual efforts by 30%.

• Deployed BI workflows for PPC campaign monitoring, ensuring timely reporting on ROI and engagement metrics, improving marketing efficiency by 20%.

• Performed migration of legacy reports to SAP BusinessObjects, standardizing analytics outputs and reducing data inconsistencies by 25%.

• Created Power BI dashboards to monitor digital marketing campaigns, providing insights into traffic, engagement, and donor trends.

• Improved campaign ROI by 25% through actionable analytics. Analyzed PPC performance using SQL and Excel, identifying underperforming keywords and reallocating budgets to maximize ROI, increasing conversion rates by 30%.

• Automated weekly performance reports using Advanced Excel (VBA, Macros), reducing report generation time by 40%.

• Designed ad hoc dashboards in Power BI for donor behavior analysis, increasing donor retention by 18% through targeted insights.

• Developed a predictive model in Python to identify high-value donors, leading to a 15% increase in donations within three months.

• Streamlined ETL workflows for digital marketing data, integrating data from multiple platforms like Google Ads and Facebook Ads. reduced processing times by 25%.

• Conducted root-cause analyses on marketing data anomalies using SQL and Power BI, resolving 90% of data discrepancies and improving trust in analytics.

• Collaborated with the marketing team to design dynamic Power BI dashboards, enhancing visibility into campaign performance and improving decision-making speed.

• Built and maintained a centralized Data Dictionary for marketing analytics, standardizing metrics and ensuring consistency across reporting.

• Optimized SEO analytics workflows, tracking engagement metrics in Power BI and identifying strategies that increased website traffic by 30%.

• Provided training sessions on Power BI and Advanced Excel to 10+ marketing analysts, empowering them to build their own analytics tools.

• Developed anomaly detection workflows using Python and SQL for campaign analytics, reducing undetected issues by 20% and improving reporting accuracy.

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