I build data engineering and analytics systems for finance — from ingestion pipelines and warehouse modeling to optimization and decision-ready dashboards.
- 🎓 MS in Business Analytics, UT Austin
- 🧱 Focus: data engineering, financial data platforms, portfolio analytics, and applied ML
- ⚙️ Interests: reliable ETL/ELT, dimensional modeling, forecasting, risk-aware optimization, and production-style analytics apps
A portfolio optimization engine focused on tax-loss harvesting, rebalancing constraints, and scenario analysis.
Highlights
- Constraint-based optimization workflow for allocation decisions
- Tax-aware rebalancing logic and portfolio health metrics
- Streamlit front end for interactive analysis and what-if testing
A full O2C analytics system built end-to-end with a strong data engineering backbone.
Highlights
- Relational operational schema for transactional workflows
- ETL pipeline into a star schema analytics layer
- KPI-ready SQL models for sales, invoicing, inventory, and operations
- Multi-page Streamlit dashboard for business monitoring and drill-down
Automated extraction and transformation pipeline for network analysis datasets.
Highlights
- Selenium-based ingestion of communication metadata
- Data cleaning + transformation into graph-friendly structures
- Enables downstream centrality and relationship analytics
Core Languages
Python, SQL
Data Engineering
ETL/ELT pipelines, data modeling, star schemas, analytical SQL, data quality checks
Finance & Analytics
Portfolio optimization, tax-loss harvesting logic, scenario analysis, statistical modeling, machine learning
Platform & Delivery
Streamlit, Snowflake, GitHub, Google Colab
- Building robust data workflows for financial analytics use cases
- Improving observability, data validation, and model reliability in end-to-end pipelines
- Expanding reusable components for portfolio/risk analytics applications
- LinkedIn: linkedin.com/in/joshuaringler