Portfolio - Kaushik Yeddanapudi

Hello, I am

Kaushik Yeddanapudi

Data Aspirant

About Me.

Data professional passionate about building scalable data systems that turn raw data into reliable business insights. Experienced in designing analytics solutions, ETL pipelines, dashboards, and cloud-based data platforms using modern data engineering and analytics tools. I enjoy solving complex data problems, automating manual processes, and creating systems that help businesses make faster, data-driven decisions. Driven by continuous learning and excited by opportunities to build products, platforms, and processes that create measurable impact.

1

PowerBI Project

1

Databricks Project

Skills.

DataBricks

Power BI

SQL

Python

Microsoft Azure

Projects.

Awards & Certificate.

SQL Beginner to Advanced For Data Professionals

AI Automation for Data Professionals

Python: Beginner to Advanced For Data Professionals

Data Engineering Basics for Data Analysts

Project: Securities Pricing Data Pipeline using Docker, Airflow, Snowflake and AWS

Personal Branding (LinkedIn & Beyond) for All Professionals

Project: Build E-commerce Data Pipeline using Spark & Databricks

Contact.

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

[email protected]

Call Me

+91 7993273549

https://www.linkedin.com/in/kaushik-yeddanapudi-a7603a323/

Databricks Project

ShopVista Retail Intelligence — End-to-End Data Pipeline using Spark & Azure Databricks

Domain/Function: Data Engineering / E-Commerce Retail Analytics

An end-to-end Medallion Architecture data pipeline for ShopVista e-commerce, unifying 9 source datasets across orders, shipments, returns, customers, products and brands — with automated Bronze→Silver→Gold refresh and Power BI reporting.

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Engagement

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ShopVista Retail Intelligence — End-to-End Data Pipeline using Spark & Azure Databricks

Azure Pipeline Architecture showing ShopVista CSV data flowing into Azure Data Lake Storage, processed by Azure Databricks with Unity Catalog ETL/ELT through Bronze, Silver, and Gold layers into Power BI

Databricks Workflow DAG showing two parallel pipelines: dim_bronze→dim_silver→dim_gold and fact_bronze→fact_silver→fact_gold→daily_summary, with daily_refresh_dim and daily_refresh_fact orchestration jobs

Project Details

Built a complete data engineering platform for ShopVista, a rapidly growing e-commerce company with fragmented data across 9 source systems. Implemented Medallion Architecture on Azure Databricks with Unity Catalog enforcing data trust at every layer. Bronze layer ingests raw data directly into Azure Data Lake Storage. Silver layer handles schema enforcement, deduplication, null handling, type casting, and standardization. Gold layer builds a business-ready dimensional model with 3 dimension tables and 2 fact tables optimized for analytics. Databricks Workflows orchestrate the full pipeline automatically every day via daily_refresh_dim and daily_refresh_fact jobs. Power BI Executive Dashboard connects directly to Gold layer delivering Sales, Units Sold, Customer Count, and Revenue by Channel, Brand, and Category performance. Eliminated manual reporting and gave leadership a single trusted source of truth. Tools: Azure Databricks, PySpark, Delta Lake, Unity Catalog, Azure ADLS Gen2, SQL

PowerBI Project

Product Growth Funnel Analysis- SaaS User Journey & Experimentation

Domain/Function: Product Analytics / SaaS Growth

A complete, production-grade analytics pipeline that tracks 51,000+ user events across the full PLG funnel — from signup to revenue — with A/B testing, cohort analysis, churn prediction, and ₹570K recoverable revenue opportunity identified.

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Engagement

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Product Growth Funnel Analysis- SaaS User Journey & Experimentation

PLG Analytics Funnel Conversion page showing 10K signups, 7K activated users, 3K feature adopters, 1K PQL qualified, and 378 paid customers with a waterfall chart showing 100% to 3.8% stage-wise drop-off

Star schema ERD with 4 tables: fact_user_events (event_id, user_id, event_type, event_value), dim_users (demographics, device, platform), fact_ab_tests (variant, conversion), and fact_cohort_data (activation, PQL, payment dates)

Project Details

Built a full-stack Product LED Growth Funnel Analytics system analyzing 51,000+ user events across acquisition, activation, retention, and revenue stages. Key outcomes: 47% historical adoption rate, ₹570K recoverable revenue from 74% deactivated users, 94.6% data confidence across all A/B tests. Methodology included SQL ETL, funnel drop-off mapping, cohort retention analysis, churn risk segmentation via Scikit-learn, and A/B experiment reporting. Delivered 6 business impact phases: Funnel Infrastructure, Visualization & Insights, Statistical Validation, Business Impact Modeling, Device & Consumer Intelligence, and full Documentation. Dashboard covers Funnel Overview, Retention Page, and Experiments Page. Recommended actions include feature tour prompts, pricing tier restructuring, and onboarding flow optimization. Tools: Python, SQL, Power BI.