Eswari Raparla
Professional Summary
Data Analyst with 5+ years of experience delivering actionable insights, predictive models, and executive dashboards across healthcare and financial services. Proficient in Python and SQL for data wrangling, statistical modeling, and ETL automation, with deep expertise in Tableau, Power BI, and Looker for stakeholder-facing visualization. Built and optimized cloud-based data pipelines on AWS and Snowflake, processing clinical and financial datasets exceeding 10M records. Applied machine learning techniques including XGBoost and cohort analysis to reduce patient readmission risk and drive product growth decisions. Experienced working across cross-functional teams in Agile environments to translate complex business requirements into scalable, data-driven solutions that improve reporting efficiency and operational outcomes.
Technical Skills
Work Experience
- Designed interactive Tableau and Power BI dashboards to monitor clinical KPIs, patient engagement metrics, and operational benchmarks, enabling data-driven decision-making for healthcare leadership and clinical stakeholders.
- Built automated Python and SQL validation pipelines to detect anomalies in migrated clinical data, reducing downstream data quality failures and significantly improving ETL reliability across ingestion workflows.
- Instrumented Adobe Analytics to track digital engagement and user behavior across the patient portal web and mobile platforms, surfacing UX insights that drove a 20% improvement in patient portal conversion.
- Developed cloud-based ETL and analytics workflows on AWS and Azure, enabling secure and scalable ingestion of clinical and operational datasets for downstream reporting and compliance use cases.
- Constructed scalable data pipelines using dbt and Snowflake, optimizing data models to lower query latency for time-sensitive clinical decision-making and executive reporting.
- Applied XGBoost-based predictive modeling within an A/B testing framework to analyze patient readmission risk factors, delivering process optimizations that reduced predicted readmission risk.
- Leveraged R for statistical analysis of large clinical datasets, generating actionable insights that improved reporting efficiency and informed patient care strategy decisions.
- Designed and optimized complex Oracle PL/SQL queries, stored procedures, and ETL pipelines to process large-scale financial transaction datasets, improving reporting accuracy by 25%.
- Built and maintained Tableau and Looker dashboards that translated complex financial data into clear visual reports, boosting reporting efficiency and enabling real-time decisions for product and sales teams.
- Developed interactive Power BI dashboards using advanced DAX formulas against Snowflake and MySQL data sources, delivering real-time financial insights and cutting manual analysis time by 45 hours per month.
- Automated recurring data validation checks using Python scripts and Excel VBA macros, reducing manual QA effort and improving the reliability of financial reporting outputs.
- Performed exploratory data analysis on risk and operations datasets using Pandas and NumPy, identifying performance trends and anomalies that directly influenced business strategy and risk mitigation decisions.
- Partnered with product, finance, and operations stakeholders to translate business requirements into scalable MongoDB-backed data solutions, improving cross-functional reporting visibility.
- Backed end-to-end UAT for BI dashboards and reporting solutions, coordinating with business stakeholders to resolve defects and validate that financial reporting met release-quality standards.
Projects
- Built Python and Tableau analytics workflows using cohort analysis and behavioral segmentation to identify feature adoption, retention, and user engagement trends across 5,000+ SaaS transactions.
- Performed cohort retention and growth analytics, improving simulated feature activation by 17% and increasing user engagement by 25% through data-driven product recommendations.
- Built a content-based recommendation engine using Python and Scikit-learn with NLP and cosine similarity to generate personalized recommendations across 8,800+ Netflix titles.
- Developed Tableau dashboards to analyze content trends, genres, and catalog growth, enabling data-driven recommendation and content strategy insights.
Education
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