Healthcare analytics professional transitioning to Data Analyst / BI Developer / Data Engineer roles.

7+ years hands-on experience with advanced SQL querying, dashboard development (Salesforce/Excel), root-cause analysis, data wrangling, and process optimization in regulated environments (HIPAA, 340B).

Actively building production-grade skills in Microsoft Fabric, Power BI, PySpark, Python (Pandas/Seaborn), and leveraging generative AI (Grok/xAI) to accelerate development, troubleshooting, and insights.

🔭 Recently completed: End-to-end data pipelines, interactive dashboards, and EDA projects
🌱 Currently learning: Advanced DAX, Fabric Lakehouse patterns, CI/CD for data workflows
📈 Focused on: Scalable data engineering and turning complex data into actionable business value

• Data Analysis: Advanced SQL (SQL Server), EDA, Root Cause Analysis, Data Cleaning/Validation, JSON flattening
• Visualization & BI: Salesforce Dashboards/Reports, Excel (Power Query, PivotTables, Advanced Charts), Power BI (DAX, candlesticks, slicers, KPIs)
• Programming: Python (Pandas, NumPy, Seaborn), PySpark (notebooks for ingestion/transformation), Java (OOP familiarity)
• Productivity & Engineering: Generative AI (Grok/xAI, Gemini) for query optimization & prompt engineering, Microsoft Fabric (pipelines, Lakehouse), Jira/Confluence, Agile/Scrum
• Domain: Healthcare Data (340B, EHR integration, claims analysis), Data Governance/Quality

Hands-on portfolio projects showcasing end-to-end analysis, visualization, and data engineering:

1. Massive Stock Data Pipeline & Power BI Dashboard
   Automated Microsoft Fabric pipeline fetching latest 30-day AAPL daily bars from Massive.com API.

   • PySpark notebooks for JSON flattening, cleaning, deduplication, and appending to growing Lakehouse Delta table
   • Interactive Power BI dashboard: candlestick/line charts, KPI cards (price, % change, volume), date slicers
   • End-to-end automation for up-to-date stock trend monitoring
     Tech: Microsoft Fabric, PySpark, Power BI (DAX), API integration

2. Healthcare Insurance Claims Analysis
   Exploratory data analysis on synthetic insurance claims to uncover cost drivers and patterns.

   • Cleaned/analyzed data with Python/Pandas/Seaborn; visualized distributions and correlations
   • Key insight: Smokers incur ~280% higher average charges than non-smokers
     Tech: Python, Pandas, Seaborn, Jupyter Notebooks

3. Superstore Sales Dashboard
   Interactive dashboard analyzing Superstore sales for trends, regional performance, and KPIs.

   • Power Query for prep, PivotTables/charts for visuals, slicers for interactivity
   • Actionable insights on sales, profit, categories, and top performers
     Tech: Excel (Power Query, PivotTables, Slicers), Power BI concepts

View all repositories → github.com/seandunleavy?tab=repositories

• 📧 Email: seandunleavy@gmail.com
• 🔗 LinkedIn: linkedin.com/in/sean-dunleavy-0494098
• 📄 Resume: [Link to your hosted resume, e.g., Google Drive / personal site / LinkedIn PDF – update when ready]
• 📍 Greer, SC (Open to remote, hybrid, or relocation in data roles)

Thanks for stopping by! Open to collaborations, feedback, or opportunities in analytics/BI/DE. Let's connect if you're hiring or building cool data stuff. 🚀