Developed a data engineering feature for the catalyst-cooperative/pudl repository, focusing on transforming EIA-176 energy data into a wide-table format that separates company-specific and aggregate information. This work involved building new data extraction and transformation modules using Python and Pandas, with an emphasis on modularity and maintainability. Comprehensive unit tests were implemented to ensure data integrity and accurate aggregation, supporting robust data validation throughout the ETL process. By enabling faster and more reliable querying, the feature improved the comparability of energy data across entities. The approach leveraged Dagster for orchestration, reflecting a methodical and test-driven development process.