Building at the intersection of data, AI, and enterprise platforms — turning complex problems into elegant, scalable solutions.
// about me
I'm a Distinguished Technical Architect on Data and AI at Salesforce, specializing in helping enterprise customers architect and deliver data strategies that power AI-driven outcomes at scale.
With dual master's degrees in Data Science (Northwestern University) and Data Engineering (WGU), I bring both theoretical depth and hands-on engineering skills to every engagement — from pipeline design and feature engineering to ML model deployment and governance.
Before tech, I served in the U.S. Military, which shaped how I approach every problem: with discipline, systems thinking, and a bias for getting things done under pressure.
I'm fluent in English and Spanish, passionate about data governance and the responsible use of AI, and always exploring the next frontier — currently LLMs, Agent-to-Agent (A2A) protocols, and MCP.
// tech stack
// featured work
Synthetic financial transaction simulator with overdraft prevention, balance tracking, and deep Salesforce Data Cloud / Snowflake integration.
Quick setup and demo of implementing MCP (Model Context Protocol) for Salesforce Data Cloud, enabling LLM-native data access patterns.
Collection of Salesforce-related code covering CRM Analytics, Einstein Discovery, BYOM (Bring Your Own Model), and TensorFlow integrations.
Personal reference guide covering core DSA concepts — Big O, linked lists, stacks, queues, trees, graphs, sorting, dynamic programming, heaps, and more.
Reference guide for building LLM applications with LangChain — prompt templates, LCEL chains, agents, tools, and end-to-end RAG pipelines.
Reference guide for the OpenAI API, Hugging Face Transformers, embeddings, semantic search, ChromaDB, and Pinecone vector databases.
Reference guide for core Python concepts — object-oriented programming, error handling, and testing with pytest and unittest.
More on GitHub →
// thought leadership
An exploration of how data clean rooms enable privacy-preserving collaboration between organizations without exposing raw data.
Read on LinkedIn →A practical introduction to data governance frameworks, ownership, lineage, and why it's the foundation of every successful data strategy.
Read on LinkedIn →A breakdown of ETL, ELT, and modern data integration patterns — when to use each and how cloud data warehouses changed the equation.
Read on LinkedIn →What vector databases are, how they differ from traditional stores, and why they're essential infrastructure for LLM and RAG applications.
Read on LinkedIn →More at analyticsmadesimple.com →
// credentials