Swapnali Ghumkar

1. Designed fault-tolerant batch data pipelines using Databricks to process sensor data for water utilities analytics. 2. Enhanced processing efficiency by 50% through performance tuning, caching strategies, and parallel processing. 3. Built a PySpark-SQL based quality framework, increasing ML model accuracy by 40%. 4. Maintained CI/CD and MLOps workflows using Azure DevOps for automated deployment, model management, and version control. 5. Developed 5+ data marts using modeling practices.

1. Developed 40 RESTful APIs to manage and query pharmacy operations data in the Snowflake data warehouse. 2. Implemented a modular, object-oriented Python pagination solution to streamline data retrieval. 3. Integrated a write-through Redis cache, reducing database load by 35% and improving response times by up to 5x. 4. Engaged with stakeholders to define data needs, lead technical walkthroughs, and drive adoption of delivered solutions.

1. Integrated data from 10+ sources into an AWS S3 staging area using Talend to enhance data accessibility and governance. 2. Built Talend ETL processes to standardize and load CSV/JSON master data (~10M records/month). 3. Engineered a Spark-based framework to process ~200GB/day of sales and PMIS data for downstream applications. 4. Optimized complex Amazon Redshift SQL queries by rewriting joins and indexing strategies, boosting performance by 35%.