I'm a B.Tech Computer Science (Data Science) graduate from Sreyas Institute of Engineering and Technology, Hyderabad — building at the intersection of data engineering, machine learning, and analytics.

I turn raw, messy data into pipelines, predictions, and dashboards that drive real decisions.

• 🏗️ Built scalable ETL & data lakehouse pipelines with PySpark, Snowflake, and Airflow — cutting query costs by ~50%
• 🤖 Designed an LSTM/GRU trading system for NIFTY 50 stocks with 85% prediction accuracy
• 📊 Built Power BI dashboards analyzing £16M+ revenue across 1M+ records using RFM segmentation & DAX
• 🔍 Automated document extraction (OCR + NER) for Indian IDs, achieving 90% extraction accuracy

Open to roles in: Data Analyst · Data Engineer · ML/AI Engineer · Python Developer

ML · LSTM/GRU · Time Series · Telegram Bot

Automated trading intelligence system built on deep learning:

• 🧠 LSTM + GRU models with technical indicators → 85% prediction accuracy on NIFTY 50
• ⚡ Real-time Telegram alerts → 35% faster response to market signals
• 📉 Reduced manual analysis workload by 40%

Python TensorFlow LSTM GRU Pandas Telegram API

Data Engineering · PySpark · Snowflake · Cost Optimization

Production-grade data lakehouse for real-time financial transactions:

• ⚙️ PySpark + Snowflake pipeline for real-time transaction processing
• 💰 Z-Ordering, caching & compression → ~50% query cost reduction
• 🤖 AI-based load prediction models for cluster pre-scaling at peak hours

PySpark Snowflake Apache Airflow SQL Python

Data Analytics · Power BI · RFM Segmentation · ETL

Full pipeline from raw transactions to executive-level BI dashboard:

• 🧹 Cleaned and prepared 797K+ records from 1.07M+ raw retail transactions
• 📊 RFM segmentation revealed top 10% of customers drove ~63% of £16.36M revenue
• 🗂️ Star-schema modeling + 15+ DAX measures across 5,864 customers

Python Pandas Power BI DAX SQL ETL

EDA · Power BI · Business Intelligence

3-page executive dashboard for a 500K+ order dataset:

• 🔍 EDA & cleaning on 500K+ records with Pandas & NumPy
• 💵 Analyzed $347M+ revenue, delivery KPIs, and customer behavior across cities
• 📋 15+ custom DAX measures across product categories and delivery dimensions

Python NumPy Pandas Power BI DAX

NLP · OCR · PostgreSQL · Snowflake

Automated parsing and verification system for Indian ID and financial documents:

• 🔠 OCR (Tesseract + AWS Textract) + NER + regex → 90% extraction accuracy
• ✅ Confidence scoring & exception handling reduced manual review time by 20%
• 🗄️ Scalable pipeline with PostgreSQL/Snowflake storage and data quality checks

Python Tesseract AWS Textract NER PostgreSQL Snowflake

SQL · Power BI · EDA · Income Segmentation

End-to-end banking BI solution:

• 🏗️ Data cleaning, transformation & income segmentation (Low/Mid/High) on a 24-column dataset
• 📈 Multi-page dashboards (Loan, Deposit, Summary) with SQL + DAX
• 🔎 Uncovered correlations across account types to support customer behavior analysis

MySQL Power BI DAX Python SQL

Data Engineering · Airflow · PySpark · Cloud

Production ETL optimization for large-scale data workloads:

• 🚀 Incremental PySpark pipelines → 60% runtime reduction, 35% cost savings
• 🔧 Broadcast joins, vectorized UDFs, predicate pushdown at scale
• 📅 Airflow DAGs with retry & backfill logic → 90% fewer SLA breaches

PySpark Apache Airflow Python SQL

• 🔵 Cisco — Data Analytics
• 🟡 IBM — Python for Data Science
• 🔴 Scaler — Machine Learning
• 🟢 Scaler — DBMS
• ☕ Scaler — Java

• HackAttack 2K25 — 24-hour National Level Hackathon participant