I will clean, transform, and build etl pipelines

I help businesses and researchers turn messy, inconsistent, or raw data into clean, reliable, and analysis-ready datasets with full pipelines built in Python or R. I also offer consulting for teams that need advice or a second opinion rather than (or in addition to) hands-on build work.

Hands-on Engineering Work

• Clean and validate messy datasets (missing values, corrupted entries, inconsistent formats, duplicate records)
• Build ETL pipelines to automate data extraction, transformation, and loading from CSV, Excel, APIs, or databases
• Normalize inconsistent column names, data types, and structures from multiple sources (data integration)
• Design data models and schemas for clean, scalable storage
• Set up and manage data warehouses (Snowflake, BigQuery, Redshift)
• Build transformation pipelines with dbt and orchestrate recurring runs with Apache Airflow
• Store and manage data in cloud storage (Amazon S3)
• Data governance: validation rules, quality checks, and documentation standards
• Data migration between systems, formats, or platforms
• Handle time-series data: gaps, sensor dropout, seasonal patterns, interpolation
• Basic forecasting and trend analysis on cleaned time-series data