Bandi Akash

• Applied AI enabled data validation techniques in Python to identify anomalies, raising healthcare
dataset quality by 35% and reducing downstream claim rejections.
• Implemented AWS Glue workflows handling 500K+ healthcare files monthly from S3, with automated
alerts that cut issue resolution by 40% and ensured 99.9% reliability under HIPAA standards.
• Automated hospital data imports with SSIS packages, improving integration efficiency by 40% and
eliminating 95% of manual entry errors, saving 120 staff hours and $15K in labor costs each month.
• Delivered JasperSoft reports tailored to dynamic operational needs across departments.
• Developed Python scripts in Visual Studio Code to validate and process healthcare data, integrating
anomaly detection that improved data integrity by 30% and prevented $250K in compliance risks.
• Monitored and troubleshot Jenkins CI/CD jobs, resolving issues quickly to maintain 99.9% workflow
uptime.
• Generated and executed ad-hoc SQL queries, optimizing query response time by 50% and enabling
real-time operational insights.
• Utilized JIRA to track and deliver assignments, achieving 95% on-time completion rate across sprints.
• Handled database administration and development using tools such as DBeaver, pgAdmin, Navicat,
Azure, and ATOM, supporting databases with 10M+ patient records.
• Automated recurring reports and batch processes using FORTRA Automate, ensuring 99% timely
delivery of scheduled analytics and saving 20 hours of manual effort each week.

• Led a team of 7 data analysts, leveraging SQL, Python, and relational databases to build scalable
architectures and predictive analytics, collaborated with 4 departments to ensure 100% compliance and
improve reporting consistency by 38%.
• Built custom reports and automated pipelines in collaboration with department leads, reducing report
turnaround by 62% and enabling faster decision-making.
• Enhanced reporting systems by implementing data validation protocols and query optimization
techniques, resulting in more accurate deliverables and a 40% reduction in processing time.
• Promoted best practices in data quality, documentation, and performance benchmarking.

• Developed and optimized SQL queries to improve report performance, streamline ETL processes, and
reduce data retrieval times by up to 47%.
• Designed and implemented interactive dashboards using Power BI and Tableau, enabling stakeholders to
monitor key performance indicators in real time and improving visibility across 12 departments.
• Led database migration projects to transition structured data between platforms, ensuring data integrity
and achieving near-zero downtime during cutovers.
• Collaborated with operations teams to translate evolving business requirements into actionable visual
reports and analytical tools, supporting faster decision-making.
• Trained a team of 12 staff through internal knowledge ‑ sharing sessions, boosting data fluency across
departments. Improved BI tool adoption by 40%, enabling faster and more consistent reporting.

• Ensured strict compliance in data analysis and reporting, with emphasis on HIPAA standards, CMS-1500
forms, and secure data transfer protocols, maintaining 100% audit readiness.
• Delivered strategic healthcare reporting through Excel dashboards, improving stakeholder
decision-making on end-to-end RCM and Revenue Cycle Operations.
• Led multiple training sessions for new analysts, building team competency in medical billing and
advanced healthcare data techniques.

• Analyzed large datasets of U.S. healthcare claims using Excel and MySQL, identifying patterns that
reduced claim denials by up to 75% and improved reimbursement accuracy.
• Performed CMS-1500 analytics under HIPAA compliance, producing clear, audit-ready reports that
supported client presentations and strengthened regulatory adherence.

• Managed timely follow-ups for accounts receivable across end-to-end RCM processes using Microsoft
Excel and EMR systems, improving payment turnaround by 27% and accelerating cash flow.
• Supported claims processing and improved data accuracy, reducing payment delays and contributing to
more efficient medical billing workflows.