• 🔭 Building production-grade dbt + BigQuery data pipelines
• 📊 I don't just move data — I make it trustworthy, queryable, and useful
• 💬 Ask me about SQL, data modeling, dbt, ETL/ELT, Tableau, Python
• 📫 contactwithjkm@gmail.com · 📍 Phoenix, Arizona

I sit at the intersection of data engineering and analytics — I build pipelines and make sure the data coming out of them is correct, documented, and ready to drive decisions.

my_work = {
    "engineering side" : ["ELT pipelines", "data modeling", "dbt", "SQL optimization"],
    "analytics side"   : ["reporting", "KPI frameworks", "data validation", "UAT"],
    "the overlap"      : ["staging → marts architecture", "data quality tests", "documentation"]
}

stack = {
    "transformation" : ["dbt Core", "dbt Cloud"],
    "warehouses"     : ["BigQuery", "Snowflake", "SQL Server"],
    "languages"      : ["SQL (T-SQL, Oracle)", "Python"],
    "bi_tools"       : ["Tableau", "Power BI"],
    "tools"          : ["Git", "GitHub", "Jira", "Airflow"]
}

AI-powered dbt documentation · Python · OpenAI API · GPT-4o

• Built a Python script that automatically generates dbt model and column descriptions from SQL source code using GPT-4o
• Eliminates manual schema.yml documentation — one command documents an entire dbt project
• Demonstrates LLM integration into analytics engineering workflows — prompt engineering, structured JSON output, YAML parsing
• Follows clean project structure with staging and mart layers based on TPC-H schema

End-to-end analytics engineering project · dbt Cloud · BigQuery

• Built full staging → intermediate → marts architecture on TPC-H supply chain data
• Modeled star schema with dimension and fact tables for analytics consumption
• Added data quality tests on all primary keys
• Followed feature branch Git workflow with pull requests and clean commit history

B2B SaaS revenue analytics · Python · Tableau Public · Live Dashboard →

• Designed a customer-month grain data model mirroring the output of a fct_mrr dbt mart layer
• Built synthetic dataset of 1,720 records across 74 customers, 3 regions, and 3 plan tiers using Python
• Published a 5-panel executive dashboard tracking MRR trends, plan tier distribution, regional revenue, customer segmentation, and expansion MRR by acquisition channel
• Applied a principled two-color system — blue family for revenue metrics, teal for expansion metrics

• 🎓 M.S. Information Technology — Arizona State University (GPA: 4.0)