I build end-to-end data and analytics solutions: ingestion pipelines, warehouse models, semantic layers, and dashboards that turn raw operational data into business-ready insight.

My current portfolio focuses on modern data engineering and analytics engineering across Azure, Snowflake, Microsoft Fabric, dbt, SQL, and Power BI. I also build practical AI automation projects with Python, FastAPI, LangGraph, and LLM APIs.

• Data engineering pipelines with medallion architecture and dimensional modeling
• Analytics engineering projects with dbt, SQL, star schemas, and BI-ready marts
• Cloud data platforms including Azure, Snowflake, Microsoft Fabric, Synapse, and Databricks
• Power BI dashboards with clear KPIs, semantic models, and DAX measures
• Python automation and AI agents for real-world workflows

Data Platforms: Snowflake, Azure Synapse, Microsoft Fabric, ADLS Gen2, PostgreSQL
Engineering: Azure Data Factory, Databricks, dbt Core, Docker, SQL, Python
BI and Analytics: Power BI, DAX, Metabase, dimensional modeling, KPI design
AI and Apps: FastAPI, Streamlit, LangGraph, LangChain, OpenAI, Groq, Gemini
Workflow: Git, GitHub, VS Code, Jupyter Notebook

End-to-end analytics engineering project using Snowflake, Microsoft Fabric, and Power BI on the Olist e-commerce dataset.

Built a layered Snowflake model from raw ingestion through staging, intermediate, and mart tables; loaded curated tables into a Fabric warehouse; and created Power BI dashboards for order volume, delivery performance, and shipping exceptions.

Stack: Snowflake, Microsoft Fabric, Power BI, SQL, DAX
Repo: o2c-analytics-engineering-snowflake-fabric-powerbi

End-to-end Azure data engineering pipeline using medallion architecture across bronze, silver, and gold layers.

Implemented dynamic ingestion with Azure Data Factory, transformations in Databricks, Synapse serverless views, a dedicated SQL Pool star schema warehouse, stored procedure orchestration, and a Power BI DirectQuery reporting layer.

Stack: Azure Data Factory, ADLS Gen2, Databricks, Synapse Analytics, Dedicated SQL Pool, Power BI
Repo: Azure-DataEngineering-Project

Analytics engineering project for a simulated e-commerce business using dbt, PostgreSQL, Docker, and Metabase.

Generated synthetic sales data with Python, transformed it through bronze, silver, and gold dbt layers, added data quality tests, and produced analytics-ready fact and dimension tables for reporting.

Stack: Python, PostgreSQL, dbt Core, Docker, Metabase
Repo: Ecommerce_DBT

Agentic travel planning application that turns natural-language trip requests into structured itineraries.

Built a FastAPI backend and Streamlit frontend with LangGraph orchestration, tool calling, weather lookup, place search, currency conversion, and expense estimation.

Stack: Python, FastAPI, Streamlit, LangGraph, LangChain, Groq, OpenAI
Repo: AI-Trip-Planner

Email-driven freight quote automation prototype.

The system monitors Gmail, classifies quote requests, extracts shipment details with Gemini, generates pricing, creates PDF quotes, stores results in SQLite, and replies by email.

Stack: Python, Gemini, Gmail API, SQLite, ReportLab, FastAPI
Repo: cargo-quote-assistant

I also maintain earlier learning and practice repositories covering:

• Snowflake SQL
• Power BI projects
• SQL assignments and practice
• Retail data analysis
• MLflow and MLOps experiments

• Building portfolio-ready data engineering and analytics engineering projects
• Improving warehouse design, SQL modeling, and orchestration patterns
• Creating Power BI dashboards that connect technical data models to business decisions
• Applying AI agents and LLM tools to practical automation workflows

• GitHub: PiyushGaidhani
• LinkedIn: Piyush Gaidhani