Pravalika Bollampalli

• Received the Spot Award for Outstanding Contributions to 21C Product Deliverables at Greenway Health.
• Twice honoured with the 'Star of the Month' Award in 02/22 and 08/22 by Greenway Health.
• Winner of the Innovation Award at the Smart India Hackathon 2018 Software Edition, presented by the Ministry of Human Resource Development, Government of India.
• Ranked among the Top 5 finalists in the Smart India Hackathon 2018 Hardware Edition.
• Guided a team in the research, proof of concept, and implementation of HL7 USCDI standards in a patient-facing application.

• Tamil
• English
• Malayalam
• Hindi

• Customer-Centric
• Adaptability
• Analytical Thinking
• Attention to Detail
• Communication Skills
• Problem-Solving
• Leadership Qualities

• Data Engineering and building ETL pipelines
• Build Data Visualization dashboards
• Scalable and cloud based ETL Architectures
• Real‑Time & Batch Data Pipelines
• Test-Driven Development (TDD)
• Site Reliability, Production support and Maintenance
• Infrastructure as Code (IaC)
• CI/CD Pipeline Design
• SRE & Observability Practices
• Agile & Scrum Methodologies
• Version Control with Git
• Customer-Centric Product Engineering
• Cross-Functional Team Collaboration
• Strong Analytical & Problem-Solving Abilities
• Communication & Leadership Skills

• Got a notable appreciation from the Client and was awarded Star of the Month (07/01/21) for Stormy Check Automation which significantly reduced the expenses for each quarter.
• Was awarded 'On Spot' award by TCS for quick development on the Simulcast changes (09/01/21).
• Was awarded multiple on-spot awards in LTIMindtree for continuous contribution towards the project.
• Was awarded Star of the Month (07/01/23) at LTI Mindtree for significant productivity
• Was Awarded Excellence Engineering award for my initiative towards codeless architecture in Athena Health during the month December 2024
• Won the Best Collaborator award in the Athena hackathon(March 2025) for providing the best PI, which proposed a saving of around $70,000 per year in the removal of legacy scripts and to automate the data regression pipelines
• Was Awarded best team player for the month June 2025 in Athena Health

Perishable Inventory Management Dashboard (Power BI) (Retail)

• Objective:
  Enable small‑scale retailers to move from manual spreadsheets to an interactive, data‑driven approach for tracking, forecasting, and replenishing perishable stock—minimizing waste and stock‑outs.
• Solution:
  Developed a Power BI analytics dashboard that:
  Ingests sales, spoilage and supplier data via Power Query
  Models it into a star schema (fact “Sales” table + “Products,” “Suppliers,” “Calendar” dimensions)
  Defines dynamic DAX measures for spoilage rates, sales velocity, and turnover gaps
• Key Features:
  Real‑Time Expiry & Stock Alerts: Automatic low‑stock and impending‑expiry warnings, delivered via Power BI subscriptions or mobile alerts.
  Spoilage Analytics: Interactive treemaps and bar charts showing spoilage cost by category and product, plus “spoilage‑to‑sales” efficiency matrices.
  Time‑Series Demand Forecasting: Built‑in line charts and heatmaps slicing by day‑of‑week and season to expose peak demand periods.
  Supplier Performance Comparison: Clustered visuals comparing sales volume vs. spoilage rate for national vs. local suppliers.
  Promotion ROI Dashboard: Scatter plots correlating discount levels with sales lift and post‑promo spoilage “echo.”
  Turnover Gap Heatmaps: Matrix visuals of Days‑in‑Stock distributions to flag slow‑moving SKUs.
• Technologies Used:
  Power BI (Power Query & DAX), CSV/Excel data sources (POS exports), Azure SQL (or local SQL Server), Power BI Service for sharing and mobile notifications.

Real‑Time E‑Commerce Personalization via Hybrid Recommendation Engine (Retail)

• Objective:
  Overcome the limitations of static, batch‑mode recommenders by delivering highly relevant, context‑aware product suggestions in real time—boosting engagement and conversion on e‑commerce platforms.
• Solution:
  Designed and prototyped a hybrid recommendation architecture that seamlessly ingests streaming user interactions, updates models on the fly, and serves low‑latency predictions through a scalable pipeline.
• Key Features:
  Streaming Data Ingestion: Captures clickstreams, searches, add‑to‑cart events, and purchase logs via Apache Kafka for immediate processing.
  Explainable Recommendations: Generates feature‑level attributions (via attention scores) so stakeholders can interpret “why” an item was suggested.
• Technologies Used:
  Data & Streaming: Apache Kafka, Apache Flink (or Spark Streaming), Redis/Feast feature store
  Modeling: Python, StreamLit
  Deployment: Docker, AWS Lambda for realtime scoring
  Evaluation & Monitoring: Grafana dashboard

• English
• Telugu
• Tamil
• Hindi
• German(Pursuing)