Ganesh Patil

• Working as a data engineer and MLOps architect, helps to manage the supply chain data by using GCP services.

Projects:

• Promo AI: This tool is designed to improve promotion effectiveness with a data-driven approach.
• Emea-Dts: I am responsible for collecting, harmonizing, and preparing supply chain data from various regions for reporting using Apache Spark and Cloud Run
• DnA: Helps collect social and analytical data from different services like SFMC, GA360, and MOAT, etc in order to generate the scorecard displaying brand performance
• Stack: GCP Platform, Snowflake, AWS
• Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.

• Helps in migrating the project to Google cloud, optimizing the existing spark job, and solving deployment issues.

Projects:

• Express: Express, Inc : Is an American fashion retailer, This project is about Data warehousing, In which we also perform data enrichments, customer matching, and data deduping using Spark and Hadoop
• Stack: GCP Platform

• Utilized the cloud services like Dataflow, Compute Engine, and BigTable to reduce the cost of maintenance, and increase the robustness and scalability of the application.

Projects:

• RTS: RTS processes real-time feed from different communication channels and re-scores the propensity score which predicts members' propensity to buy in the particular product category, hence helping generate targeted offers for members
• Churn Prediction: Retail churn prediction using LightGBM decision tree
• Used PySpark for distributed scoring in the GCP cloud
• Stack: GCP Platform, LightGBM

• Helps in collecting the HVAC, chillers realtime and batch data, process using the MapReduce and Apache Storm, Finding the anomalies in data.

Projects:

• EPAO - Extended Platform for Analysis and Opportunity: A management portal designed to provide monitoring of assets like chillers, compressors, etc, by processing the raw sensory outputs
• The processed data is displayed to the end user in the form of live dashboards for monitoring controllers, fault detection for diagnostics and maintenance, and other various reports
• SCC - Smart Chiller Connectivity: Developed using Hadoop Ecosystem (Microsoft Azure HDInsight) as main backend support
• Uses Storm and Azure Stream analytics for processing continuous events of the stream (In JSON) from Azure EventHub
• The gathered data uses to calculate the alerts, used to show on the live dashboard
• Stack: Hortanworks, Microsoft Azure, Hadoop