Jaswanth Srinivas Chandu Meesala
Professional Summary
Data Analyst with 3+ years of experience delivering healthcare analytics, ETL, and self-serve reporting using SQL, Python, Power BI, and Tableau. I design and automate ETL pipelines with PySpark, Snowflake, Databricks and AWS Glue to transform millions of EHR and claims records into analysis-ready datasets. I build executive and operational dashboards that eliminated 12–15 hours of manual reporting weekly and enabled capacity planning across 180+ acute-care facilities. I apply statistical methods including regression, time-series forecasting, statsmodels, and SPC in R to surface process deviations and guide clinical-quality improvements. I optimize analytical queries and Redshift/Snowflake schemas to cut runtimes and increase throughput for enterprise reporting workloads. I partner with clinical and operations teams to convert HL7/FHIR and MEDITECH data into KPIs and decision-ready insights. I take end-to-end ownership from data ingestion to validated dashboards and model-driven interventions.
Technical Skills
Work Experience
- Modeled emergency department arrival patterns using Python and statsmodels to inform staffing plans and reduce average patient wait time by 18 minutes.
- Built automated Tableau dashboards for bed occupancy, patient flow, and length-of-stay to replace manual reporting and enable operational monitoring.
- Cleaned and standardized 1M+ MEDITECH EHR records with PySpark and AWS Glue to create a validated clinical dataset for quality reporting.
- Analyzed bed utilization, admissions, discharges, emergency department activity, and LOS trends to support capacity planning across 180+ acute-care facilities.
- Applied statistical process control in R to monitor door-to-treatment performance and generate exception signals for quality-improvement teams.
- Developed Power BI reports for patient safety and treatment timeliness so leaders could self-serve clinical quality metrics without manual spreadsheets.
- Investigated reporting discrepancies across clinical and operational datasets, reconciled data mismatches, and improved accuracy of recurring reports.
- Automated claims-validation workflows using Talend and Snowflake, improving end-to-end data processing speed by 35%.
- Optimized SQL queries and Amazon Redshift indexing to cut analytical query runtimes from 45 minutes to under 6 minutes for large claims workloads.
- Extracted and validated 5M+ historical medical claims using SQL and Snowflake to assemble accurate datasets for enterprise reporting.
- Built Power BI dashboards to track claim denials, auto-adjudication rates, and provider billing trends used by business stakeholders.
- Standardized EHR, EMR, and lab data to HL7 and FHIR formats using PySpark and Amazon S3 to enable downstream analytics.
- Orchestrated ETL on Databricks to support automated Tableau reporting and reduce manual report preparation for preventive-care visibility.
- Developed scikit-learn models in Python to prioritize suspicious billing patterns and support targeted claims investigation efforts.
Projects
- Analyzed 300,000+ CDC records across 50 states to identify regional and demographic mental-health patterns and at-risk populations.
- Translated comparative statistical findings into interactive Tableau visualizations to support public-health planning and awareness.
- Processed 2.2M+ collision records to identify high-risk locations, peak periods, and severity patterns to inform safety recommendations.
- Built visual analytics to compare borough, time, severity, and contributing-factor trends and support data-driven traffic-safety priorities.
Education
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