• Design, implement, and maintain backend services and data pipelines using Python • Work extensively with dataframes (Pandas, PySpark, or similar) to process large datasets • Build and optimize data pipelines using Parquet and columnar storage formats • Develop and maintain workflows on Databricks • Optimize performance for large-scale, distributed data processing • Design and maintain APIs that support data-heavy applications • Collaborate closely with front-end, data, and product teams • Ensure data quality, reliability, and observability • Participate in architectural decisions and technical planning • Mentor other engineers and lead code reviews