Building end-to-end data platforms and decision-ready KPI systems.
I design reliable data models, metric layers, and analytics pipelines—then ship dashboards and AI-enabled tools that help teams explain what changed, why it changed, and what to do next.
PostgreSQL
Python
SQL
Streamlit
Tableau
GitHub Actions
Featured Projects
A curated set of projects that demonstrate data modeling, analytics engineering, KPI design, and production-minded delivery. Each card links to a full GitHub repo with documentation and code.
End-to-End Retail Data Warehouse
Star schema DW with staging → warehouse → marts, metric-ready tables, and executive KPI dashboards. Focus on reproducible transformations and decision-aligned metrics.
Olist SQL Analytics
Business KPI analytics with cohorts, funnel analysis, and revenue driver decomposition. Designed to answer “what changed” and “what caused it” with clear SQL artifacts.
AI Executive KPI Intelligence (Micro-SaaS)
LLM-powered KPI insights grounded in a metrics layer. Emphasis on production structure: consistent schemas, reproducible results, and clean API-style outputs.
Baseball Analytics Platform
Data pipelines + interactive dashboards for lineups/prospects and decision-style insights. A vertical analytics product direction that showcases applied DE + analytics.
Skills
I focus on building analytics systems that are trustworthy, measurable, and easy for stakeholders to consume.
Data Engineering
Data modeling (star schema), ETL/ELT patterns, data quality checks, and “dashboard-ready” marts.
Analytics Engineering
KPI definition, metric consistency, cohort/funnel analysis, and driver decomposition for executive reporting.
AI & Delivery
LLM-enabled analytics tooling grounded in schemas/metrics, plus reproducible repos with clean documentation.
About
How I work
I approach analytics like a product: define reliable metrics, build reproducible pipelines, and ship outputs that help stakeholders make decisions. I care about clarity (docs/README), correctness (validation checks), and usability (dashboards).
- Metric-first: KPI definitions and consistent calculations
- Modeling: star schema + marts aligned to reporting needs
- Decision focus: drivers, cohorts, funnels, and operational impact
Quick Facts
• Rutgers MITA (focus: DE + AI Analytics)
• Marketing Analytics background
• Strong in SQL storytelling + system design
Want a fast overview? Start with the 4 featured projects above.
Contact
Best way to reach me is via LinkedIn or GitHub. (You can also add an email link below.)