Energy Billing ETL Pipeline

The Problem

Nigerian energy utilities generate large volumes of billing data, but the raw records arrive fragmented and inconsistent — with missing values, duplicate entries, and mixed data types. Without cleaning and validation, that data cannot reliably support billing analytics, collection-efficiency tracking, or operational insight.

My Approach

I built a Python ETL pipeline that ingests Nigerian energy billing data from HuggingFace datasets, cleans and validates records (missing values, duplicates, type consistency), and loads the result into PostgreSQL via SQLAlchemy. The pipeline is fully reproducible, logs load metrics per run, and produces a clean analytical layer ready for utilities insight.

What I Built

• Python extraction module reading energy billing datasets from HuggingFace
• Data cleaning layer handling missing values, duplicates, and type coercion
• Validation checks enforcing referential and domain integrity before load
• PostgreSQL ingestion via SQLAlchemy with transactional, error-safe loading
• Run metrics logging (rows read, cleaned, loaded) for observability
• Data dictionary and pipeline runbook for maintenance

Impact & Results