Stack Inventory

Engineering Stack

A weighted matrix of tools and domains—categorized by operational depth and strategic focus across analysis, ML, BI, economics, and computer vision.

Data Engineering

Machine Learning

Business Intelligence

Economics & Quant

Computer Vision

CORE

Daily execution

Data Analysis Machine Learning Business Intelligence Economics Computer Vision Python SQL

STRONG

Architecture

Power BI Google Data Studio BigQuery Scikit-learn Statistical Modeling Tableau Salesforce Stata OpenCV Computer Vision Pipelines ETL/ELT REST APIs Qlik Econometrics Data Annotation

GROWTH

Acquisition

Experimental Design Plotly Web Scraping NLP Geospatial Analysis RAG Ollama MCP

Philosophy

The stack favors explainable pipelines and decision-ready outputs over tool sprawl. Mature tools handle persistence and reporting; emerging layers stay scoped and honest.

Connectivity

Tools connect through reproducible SQL/Python workflows, API integrations, and shared metric definitions—not ad hoc spreadsheet exports.

Automation

Python ETL, scheduled dashboards, and validated models reduce manual handoffs. Production means repeatable pipelines, not one-off heroics.

Stack