Building Production Data Systems | Business Operations → Data → ML → LLM Engineering
Currently: Stage 1 (Data Analyst) | Building in Public | 37-Month Journey

Most entry-level candidates have tutorial projects. I have production code with measurable business impact.

The Combination: Deep domain expertise (data + finance + trading) + Production systems + Technical skills + Clear growth trajectory = Immediate value + long-term potential

For Recruiters (START HERE):

1. 💼 Projects Portfolio - Production + learning projects with business impact
2. 🧾 1099 ETL Pipeline - Live production system (public code)
3. 🔗 LinkedIn Profile - Professional background & recommendations

For Technical Review:

• 📊 37-Month Roadmap - Complete skill progression plan
• 📚 Learning Journey - Daily practice & documentation

For Collaboration:

• 📈 Trading Projects - Data + finance intersection

Status: ✅ Live in production | 🌐 Public repository with synthetic data

Business Context:
Manual reconciliation of retirement plan distributions took 4-6 hours weekly, was error-prone, and blocked critical 1099-R tax reporting at Daybright Financial.

Technical Solution:
Automated Python ETL pipeline extracting, transforming, validating, and reconciling data between two financial systems (Relius and Matrix).

Measurable Impact:

• ⚡ 95% time reduction (4-6 hours → 15 minutes weekly)
• 💰 $15,000+ annual savings in labor costs
• 📊 10x scalability (300+ accounts vs. 30 manual capacity)
• ✅ Zero errors since deployment (comprehensive validation)
• 🔒 Production-ready code visible on GitHub (smart privacy with synthetic data)

Why This Matters:

• Real enterprise system (not tutorial project)
• Measurable business ROI
• Professional data governance (public code, private data)
• Demonstrates production best practices from day one

Tech Stack: Python • pandas • openpyxl • Excel • matplotlib • data validation • faker

→ View Code & Technical Documentation

What I Can Do Right Now:

# Data Analysis & ETL
✅ Python data pipelines (production-deployed)
✅ SQL queries (SELECT, JOIN, WHERE, aggregations)
✅ Pandas data manipulation & cleaning
✅ Data validation & error handling
✅ Excel automation (openpyxl)
✅ API integration & data extraction
✅ Data visualization (Matplotlib, Seaborn, Plotly)

# Business Skills
✅ Process automation & efficiency analysis
✅ Financial data reconciliation
✅ Stakeholder reporting
✅ ROI analysis & business impact measurement
✅ Production system deployment

Currently Building:

• 📈 Trading Attention Tracker - Multi-source data pipeline correlating trading volume with public attention
• 🎓 Certifications: CS50, Python for Everybody, IBM Data Analyst, Google Data Analytics

Next Milestone:

• 🎯 Land Data Analyst role (Target: Month 5)
• 📊 Complete Stage 1 capstone projects

Systematic evolution from Data Analyst to Senior LLM Engineer:

Current Focus: Stage 1 (Data Analyst)
Ultimate Goal: Production AI Trading Assistant (Month 37)

→ View Interactive Roadmap

Languages:     Python 3.11+, SQL
Data Analysis: pandas, NumPy, Matplotlib, Seaborn, Plotly
Databases:     SQLite, PostgreSQL (learning)
Tools:         Jupyter, Git/GitHub, VS Code, Excel (advanced)
APIs & Web:    REST APIs, JSON/XML, BeautifulSoup
Production:    ETL pipelines, data validation, error handling

Data Operations: 15+ years working with data (manufacturing, bookkeeping, finance)
Financial Services: 8 years (retirement plans, compliance, reconciliation)
Trading: 6 years active trading (technical analysis, quantitative strategies)
Business: Process automation, ROI analysis, stakeholder communication

Stage 2 (Data Engineer): AWS, Airflow, PySpark, Redshift, Docker
Stage 3 (ML Engineer): scikit-learn, TensorFlow, PyTorch, MLOps
Stage 4 (LLM Specialist): LangChain, RAG, vector databases, fine-tuning
Stage 5 (Senior): Production AI systems, architecture, leadership

Status: 🚧 Active Development | Phase 2 (v1.1)

Multi-source data pipeline testing hypothesis: Public attention spikes correlate with trading volume increases.

Technical Highlights:

• End-to-end pipeline: Multiple APIs → SQLite → Analysis → Visualization
• Data sources: yfinance, Wikipedia API, RSS feeds, web scraping
• Normalized database design (5 related tables)
• Time-series correlation analysis
• Sentiment analysis integration

Evolution Path:

• ✅ v1.0: Core pipeline, 3 tickers, basic analysis
• 🚧 v1.1: Expand to 10+ tickers, CSV exports, enhanced validation
• 📅 v2.0: Interactive Streamlit dashboard
• 📅 v3.0: ML models predicting volume from attention

Tech Stack: Python • SQLite • pandas • yfinance • Wikipedia API • BeautifulSoup • Matplotlib

→ View Project

Central repository linking all projects with business context, technical details, and impact metrics.

Categories:

• 🏭 Production Systems: ETL pipelines, automation tools
• 📈 Finance & Trading: Market analysis, quantitative strategies
• 📚 Learning Projects: Capstone projects, certifications
• 🧪 Experiments: Proof-of-concepts, testing new tools

→ View All Projects

• 🚧 CS50: Introduction to Computer Science (Harvard)
• 🚧 Python for Everybody Specialization (University of Michigan)
• 🚧 Google Data Analytics Professional Certificate
• 🚧 IBM Data Analyst Professional Certificate (11 courses)
• 📅 Statistics with Python Specialization (University of Michigan)

• Stage 2: AWS Certified Data Engineer, Solutions Architect Associate
• Stage 3: TensorFlow Developer Certificate, Deep Learning Specialization
• Stage 4: Advanced LLM Engineering, RAG Systems
• Stage 5: Leadership & Architecture courses

I've worked with data my entire career—manufacturing operations, bookkeeping, financial reporting, financial services, and 6 years of active trading. One truth became undeniable: data-driven decisions consistently outperform gut feeling.

I watched companies make million-dollar mistakes by ignoring their numbers. I saw traders succeed or fail based purely on their data analysis discipline. The pattern was crystal clear.

But I hit a ceiling: I could analyze data brilliantly, but I couldn't build the automated systems to scale these insights. I couldn't create the real-time pipelines, ML models, or AI assistants I envisioned.

So I'm building them myself—following a systematic 37-month path from Data Analyst to Senior LLM Engineer. I'm doing it publicly because career transformation shouldn't be mysterious—it should inspire others to do the same.

Why share everything openly?

✅ Transparency: Real learning is messy—showing actual process, not just polished results
✅ Accountability: Public commits = public commitment (can't fake progress)
✅ Community: Others learn from my journey, I learn from theirs
✅ Portfolio: This profile IS proof of ability, work ethic, and trajectory
✅ Authenticity: Career transitions should be documented honestly

What makes this different:

• Not just completing courses → Enhancing exercises beyond requirements
• Not just learning → Deploying production systems with measurable impact
• Not just building projects → Solving real business problems
• Not just showing code → Explaining business context and ROI
• Not staying private → Sharing progress, challenges, and breakthroughs

Building toward a production-grade LLM-powered trading system that:

• 🔍 Analyzes markets in real-time using multi-source data
• 🤖 Generates trading signals with ML models
• 💬 Provides natural language insights (LLM-powered)
• ⚡ Executes algorithmic strategies automatically
• 📊 Learns and adapts continuously

Why This Is The Perfect Target:

• Combines deep domain expertise (data + finance + trading) with cutting-edge AI
• Solves real problem (trading analysis is time-consuming and emotional)
• Rare skill combination in the market
• Demonstrates end-to-end capability (data engineering → ML → LLM → production)
• Foundation for consulting/startup opportunities

Progressive Build Across 5 Stages:

• Stage 1: Data pipeline + market analysis (Current)
• Stage 2: Cloud infrastructure + real-time data streams
• Stage 3: ML models + backtesting frameworks
• Stage 4: LLM integration + natural language insights
• Stage 5: Production deployment + monetization strategy

💼 Data Analyst Opportunities

• Remote positions preferred
• Business operations or finance/trading sectors
• Teams that value production experience and domain expertise

🤝 Professional Connections

• Data professionals and traders
• Code reviews and technical discussions
• Collaborations on trading + tech projects

🎓 Knowledge Exchange

• Mentorship (giving or receiving)
• Career transition advice
• Data-driven decision making discussions

• Value production code over tutorial completions
• Are building data-driven trading systems
• Believe in transparent career development
• Want to discuss data + AI + finance intersection
• Are on a similar learning journey
• Are hiring Data Analysts with proven delivery capability

• 📈 6+ years active trading (swing & day strategies)
• 🌅 4:30 AM club (early morning focused study)
• ♟️ Chess enthusiast (strategy translates to markets!)
• 🤖 Fascinated by LLMs transforming financial analysis
• 📚 Reading: Machine Learning for Algorithmic Trading + Hands-On LLMs
• 🎯 Obsessed with data-driven decision making
• 💪 Proving systematic learning beats raw talent

Production Systems:

• 🧾 1099_reconciliation_pipeline - Live ETL system ($15K/year savings)

Active Projects:

• 📈 trading_attention_tracker - Stage 1 capstone
• 📊 data-portfolio - Project hub with business impact

Learning Documentation:

• 📚 learning_journey - 37-month public documentation