Senior Data Analyst & Engineer bridging the gap between raw infrastructure and executive strategy. I build automated, self-healing data systems that drive revenue.
Four AI-powered solutions built to solve real-world data challenges. Each one live, deployed, and ready to use.
Upload raw CSV/Parquet files and receive end-to-end ETL pipelines (SQL + Python + YAML) tailored to Postgres/MySQL/Snowflake. FastAPI backend with LLM orchestrator automatically generates cleaning, transformation logic, and DDL/DML scripts.
One-click data quality scoring engine with ML-based anomaly detection. Runs rule-based and ML checks (IsolationForest/LOF), computes quality scores (0-100), and generates AI-driven root cause analysis with remediation steps in PDF format.
Transforms arbitrary datasets into interactive visual analytics + automated narratives. Combines EDA, Plotly visualizations, Prophet/ARIMA forecasting, and LLM-based summarization to generate executive summaries with charts, KPIs, and AI-written commentary.
AI-driven pricing optimization platform running A/B tests, price elasticity analysis, and revenue forecasting. Features Bayesian testing, regression-based elasticity modeling, scenario simulation, and LLM-generated pricing strategies with risk assessment.
Teams struggle with manual data extraction, spending hours on repetitive ETL tasks. Data pipelines are fragile, error-prone, and require constant maintenance. Every new dataset means starting from scratch.
Weeks of waiting for data analysis reports. By the time insights arrive, market conditions have changed. Decision-makers operate in the dark, missing critical opportunities and unable to respond to emerging trends.
Data quality problems go undetected until it's too late. Anomalies, outliers, and drift silently corrupt analytics. Without automated monitoring, bad data leads to bad decisions and costly mistakes.
Upload any dataset and get production-ready ETL pipelines in minutes. AI-generated SQL, Python, and YAML configs tailored to your database. Zero manual coding, 100% automation. That's what modern data engineering looks like.
Real-time dashboards with AI-generated insights. Interactive visualizations, automated forecasting, and executive summaries delivered in seconds. Decision-makers get the intelligence they need, when they need it. Speed is a competitive advantage.
ML-powered anomaly detection catches issues before they spread. Automated quality scores, drift monitoring, and AI-driven root cause analysis. Every dataset validated, every problem diagnosed, every solution documented. Trust your data again.
Clark University, MA, USA
Aug 2022 – May 2024
Sreenidhi University (SNIST), Hyderabad, India
Aug 2017 – Jun 2021
Open to opportunities in Data Analytics and Data Engineering
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