My Portfolio

A showcase of my projects and my abilities.

Self-image

Hi, I’m Pranay Pentaparthy. I’m currently working as a Data Engineer Consultant, helping teams build data pipelines and systems they can actually rely on. Before I started my career at Tech Consulting, I studied at Michigan State University where I spent a lot of time learning by doing—working on projects, experimenting with different technologies, and building a strong foundation in data and software engineering. After I graduated from MSU, I joined Jackson National Life Insurance, where I got firsthand experience supporting production systems. No matter what I'm doing, I am always learning and I am motivated by work that creates real impact.

Weather App

weather_app
  • Built application tool using the tkinter graphical user interface library from Python
  • Utilized OpenWeather API to collect data about the temperature, wind, pressure, humidity, and general description of a given city

Voice Assistant

voice_assistant
  • Implemented OpenWeather and WolfRamAlpha APIs to receive information about weather and general questions
  • Crafted assistant voice with pyttsx3 and invocated SpeechRecognition and gTTS libraries to handle speech and text conversion

Rental Management system

rental_management
  • Utilized Flask to present information about father's tenants in HTML tables
  • Stored property, tenant, and payment information with the help of PostgreSQL and Python
  • Crafted login, signup, and password reset pages as a safety measure to user

Newgen Flight booking system

newgen_booking_system
  • Connected Flask app to SerpAPI Google Flights API to allow users to book nonstop one-way and round trip flights.
  • Attached Botpress chatbot in application to answer questions about content of website, such as the signup and login process, and the goal of web application.

Amazon Review analysis

amazon_reviews_analysis
  • Built an AWS-based batch and streaming pipeline using Kinesis, Lambda, S3, and Glue to analyze Amazon reviews for sales decline and fraud detection.
  • Modeled low-latency review and anomaly datasets in DynamoDB and exposed analytics through Amazon QuickSight dashboards.
  • Enabled near-real-time monitoring of ratings, review volume, and suspicious activity patterns across historical and live data.

Azure Snowflake Enterprise Risk Data Platform

azure_snowflake_risk_data_platform
  • Led the design of a unified Azure-to-Snowflake risk data platform, integrating trading, credit, and treasury data into a single governed enterprise warehouse.
  • Built automated ingestion and distributed Spark transformations using Azure Data Factory and Azure Synapse to standardize schemas, reconcile exposures, and enforce data quality.
  • Reduced reconciliation effort by 80%+, cut data latency from hours to minutes, and enabled near-real-time exposure analytics for senior leadership.