Chitresh Katarpawar

Role summary: Owned backend design and delivery as a senior individual contributor; implemented CI/CD, stabilized tests, and designed event‑driven integrations.

Project: Revin Forecast Application

• Designed event-driven architecture using Kafka for resource, pricing, and KPI updates, forecasting and P&L propagation across services.
• Established integration with multiple systems to ensure accurate resource profiles and pricing.
• Enabled resource-level and role-level forecasting within the platform.
  Facilitated legacy methodology support through Excel data uploads for user convenience.
• Developed Revin Forecast application for budgeting and forecasting at the organizational level.
• Provided comprehensive overview of actuals and P&L data at resource level.
  Enabled forecasting capabilities based on the actual financial performance.
• Provided comprehensive overview of actuals and P&L data at resource level.Enabled forecasting capabilities based on actual financial performance.

Project: Revin Forecast Application

• Designed and developed rate design tools for electricity utilities to enhance existing rate plans.
• Built models according to utility specifications to ensure accurate billing values.
• Tested models before delivering to rate design analytics for further configuration.
• Facilitated analysis of customer segments to assess impact of rate changes.

Role summary: Individual Contributor — Spark ETL & Data Engineering • High‑Throughput Pipelines • CI/CD

Project: Trade Surveillance

• Produced automated KPI reports, ensuring accuracy through comprehensive data quality checks.
• Developed CSV files using Java Spark application to facilitate trade surveillance initiatives.
• Implemented trade surveillance methods on reports, improving gap detection efficiency.
• Streamlined existing workflows, empowering users to pinpoint data deficiencies for remediation.

Role summary: Senior Software Engineer — Billing Revenue Assurance & Early BIN Prediction (Spark Streaming • Kafka).

Project: Billing Revenue Assurance

As Senior Software Engineer, I implemented a near‑real‑time Spark Streaming and Kafka‑based pipeline to predict telecom billing issues (early BIN prediction) before bill generation. I owned end‑to‑end data ingestion, feature engineering and downstream event publication.

• Streaming ingestion & processing: Built Spark Streaming jobs to ingest usage and incident events; implemented windowed aggregations, enrichment, and real‑time feature extraction for model input.
• Event pipeline & messaging: Designed Kafka topic schemas, partitioning strategy, published processed features and prediction signals to Kafka topics for downstream consumers (alerting, case creation, billing workflows).
• Modeling: Trained and validated supervised ML models (logistic regression / tree models) on historical billing data.
• Outcome: Enabled early detection of billing issues prior to bill generation, reduced manual investigation and mean time to resolution, and provided near‑real‑time visibility of predicted incidents via Kafka‑driven downstream workflows.

Role summary: Senior Software Engineer — Java Backend • REST APIs • Feature Delivery • Production Support • Cross Functional Team

Project: IBhakti Web Portal

• Developed ibhakti web portal for devotees to conduct worship from home and view deities remotely.
• Facilitated online booking of worship services, including sweet offerings, through platform.
• Created e-commerce functionality allowing users to purchase holy items via web portal.

Role summary: Senior Software Engineer — Embedded & Server Software • Device Telemetry • Protocol Integration • Scalable Backend Systems.

Project: AMR, WCLS, IEMS

• Engineered home products for AMR WCLS IEMS projects to oversee hardware module operations.
• Utilized Zigbee networks, COM ports, and server sockets for reliable communications.
• Introduced enhancements such as full data fetching, trip feeder, and transformer features.
• Resolved bugs to elevate product performance and user experience.
• Worked closely with hardware and QA teams to ensure compliance with integration protocols.
• Optimized integration of hardware modules with software applications through effective collaboration.