Worked on the data-hydenv/data repository to deliver end-to-end ingestion of temperature time-series data at both 10-minute and hourly intervals, supporting high-resolution analytics and reliable historical logging. The approach involved ingesting large CSV datasets, normalizing schema by renaming time fields, and enforcing three-decimal precision for numeric values to enhance data quality and analytics readiness. Leveraged Python for ETL scripting, focusing on robust data cleaning and formatting practices. No major bugs were addressed, but minor data-quality improvements were incorporated as part of schema normalization, resulting in a streamlined pipeline that reduces downstream ETL complexity and improves overall data reliability.