👋 Hi, I’m Jing You

Data Engineering & Analytics Engineering | Microsoft Fabric | Power BI | Python | SQL I build scalable data systems that turn raw information into actionable insights.
My background blends business operations, customer understanding, and technical engineering — giving me a unique edge in solving real-world data problems.

⚡ Core Skills

Data Engineering

• ETL / ELT pipeline development
• REST API integration
• Data modeling & transformation
• Data validation & quality assurance
• Batch processing & automation

Technologies

• Python: Pandas, Polars, Requests
• SQL: PostgreSQL, DuckDB
• Cloud: AWS (S3, Lambda)
• Tools: Docker, Git, VS Code Dev Containers
• Data Formats: JSON, Parquet, CSV

Currently Learning

• dbt
• Apache Airflow
• Microsoft Fabric

💼 Featured Projects

📌 NPPES Medical Provider Data Pipeline

Tech Stack: Python, AWS S3, PostgreSQL, Docker
A full batch ETL pipeline processing 8.7 million medical provider records.

Highlights

• Automated ingestion using AWS Lambda
• Efficient processing of multi-million–row datasets
• Structured logging for traceability
• End-to-end containerized workflow

👉 View Project →

📌 Weather Data Integration Pipeline

Tech Stack: Python, REST API, Pandas, PostgreSQL, DuckDB, Docker
A multi-source data pipeline combining real-time weather data with station metadata.

Core Features

merged = weather_df.merge(stations_df, on='city', how='inner')
merged.to_parquet('weather_clean.parquet')
# Data Validation with DuckDB
con.execute("""
    SELECT COUNT(*) as null_count
    FROM weather WHERE temperature_f IS NULL
""")

Project Highlights

• RESTful API integration with authentication & rate limiting
• Data cleaning & merging using Pandas (Inner Join)
• 6-layer data validation using DuckDB
• Parquet columnar storage → 10× faster queries
• Fully containerized with Docker + Dev Container

Business Impact

Enables analysis of weather-driven business patterns:

• Customer traffic changes on rainy days
• Outdoor seating optimization
• Staffing adjustments based on forecasts

🧠 How I Think About Data Engineering

Systems Over Scripts

I design pipelines that are:

• Maintainable
• Scalable
• Observable
• Documented

Data Quality First

Bad data = bad decisions.
I prioritize:

• Validation
• Schema consistency
• Reproducibility

Business-Aware Engineering

My hospitality and operations background helps me:

• Understand real business workflows
• Identify high-impact opportunities
• Communicate clearly with non-technical teams

📬 Contact

Open to Data Engineer / Analytics Engineer / BI roles (Nashville or Remote)

• 📧 Email: jingliuyou@gmail.com
• 📍 Nolensville, Tennessee