Freelancers using SQL in MaharashtraFreelancers using SQL in Maharashtra
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Swaraj Dolhare's avatar
Swaraj Dolhare
Pune, India
Full-Stack Web & App Developer | React, Flutter, React Nativ
$1k+
Earned
2x
Hired
5.0
Rating
16
Followers
AGENT_EXPERT

Expert

Full-Stack Web & App Developer | React, Flutter, React Nativ
Cover image for Ovrtrack: Streamlined Supply Chain Management for Manufacturing
Ovrtrack: Streamlined Supply Chain Management for Manufacturing
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Cover image for Commonfold is a connecting bridge
Commonfold is a connecting bridge between church and congregants. It is helping connect pastors with congregants, share common knowledge, helps pastors plan visits and organise the good work.
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Cover image for React Native Website + App
React Native Website + App completed for client from Costa Rica. Barana is a customisable food subscription app to cater diet sensitive audience. I am Happy i was part of this app.
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Cover image for 🚀 Just integrated an AI chatbot on strumio.swado.in (http:/...
🚀 Just integrated an AI chatbot on strumio.swado.in (http://strumio.swado.in) to make learning guitar even smoother! 🎸 Using @Agent.so (http://Agent.so), I built a smart assistant that helps users find song chords, strumming patterns, and chord progressions instantly. The goal is to make learning feel natural, interactive, and beginner-friendly. 💬 Ask the bot about your favorite song, get real-time strumming patterns, or explore new progressions — all in one place. This is just the beginning. Excited to keep improving the experience! ⚡ #AI #Guitar #Agentso #WebDevelopment #Strumio #Chatbot #ContraCommunity
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SQL(1)
Smita S's avatar
Smita S
Pune, India
Senior Data Analyst | Business Intelligence Consultant
10
Followers
Senior Data Analyst | Business Intelligence Consultant
Cover image for Power BI Templates for Self-Service Reporting
Power BI Templates for Self-Service Reporting
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25
Cover image for Inventory Optimization with ABC-XYZ Segmentation
Inventory Optimization with ABC-XYZ Segmentation
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Cover image for MRR & Churn Analysis Dashboard for SaaS Retention and Growth
MRR & Churn Analysis Dashboard for SaaS Retention and Growth
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40
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SQL(2)
Sunny Mehta's avatar
Sunny Mehta
Pune, India
Data Analyst & BI Dev | Dashboards, SQL, Python & ETL
New to Contra
Data Analyst & BI Dev | Dashboards, SQL, Python & ETL
Cover image for Marketplace Profitability & P&L Engine Overview This
Marketplace Profitability & P&L Engine Overview This project is an end-to-end E-commerce Marketplace Analytics solution built using MySQL, SQL, Power BI, and Excel. The objective of this project is to analyze marketplace profitability across multiple sales channels by calculating revenue, product costs, marketplace commissions, shipping expenses, and net profit. The project simulates a real-world marketplace operations environment where business teams need visibility into product performance, marketplace profitability, and operational KPIs. Business Problem E-commerce businesses often track revenue but fail to understand true profitability. This project helps answer key business questions: Which marketplace generates the highest profit? Which products are most profitable? How do commissions impact margins? What is the overall profit margin? How does shipping cost affect profitability? Tech Stack MySQL SQL Power BI Microsoft Excel Data Sources Products Contains SKU-level product information. Fields: SKU Product Name Category Cost Price Selling Price Orders Contains marketplace order transactions. Fields: Order ID Order Date Marketplace SKU Quantity Marketplace Charges Marketplace commission structure. Fields: Marketplace Commission Percentage Shipping Cost Shipping cost by marketplace. Fields: Marketplace Shipping Cost Returns Order return information. Fields: Order ID Return Status SQL Implementation The project uses multiple SQL tables and joins to create a unified profitability dataset. Key SQL Concepts Used: CREATE TABLE INNER JOIN SQL Views Aggregations Calculated Metrics A consolidated analytical view was created: vw_marketplace_pnl This view combines all business logic and serves as the primary source for Power BI reporting. Profitability Metrics Revenue Revenue = Quantity × Selling Price Product Cost Product Cost = Quantity × Cost Price Commission Cost Commission Cost = Revenue × Marketplace Commission % Shipping Cost Shipping Cost = Quantity × Shipping Cost Profit Profit = Revenue − Product Cost − Commission Cost − Shipping Cost Profit Margin % Profit Margin % = Profit / Revenue Power BI Dashboard Executive Summary KPIs: Total Revenue Total Profit Total Orders Profit Margin % Marketplace Analysis Revenue by Marketplace Profit by Marketplace Marketplace Performance Comparison Product Analysis Top Profitable Products Product Revenue Analysis Product Profitability Ranking Trend Analysis Revenue Trend Profit Trend Order Trend Project Architecture Excel → MySQL → SQL View → Power BI Dashboard Key Outcomes Built a scalable marketplace profitability model. Centralized business logic using SQL Views. Automated profitability calculations. Delivered interactive executive dashboards for decision-making. Simulated a real-world marketplace operations analytics workflow. Skills Demonstrated SQL Data Modeling Business Intelligence Power BI E-commerce Analytics Marketplace Operations Profitability Analysis Data Visualization Author Sunny Mehta Open to opportunities in: E-commerce Operations Marketplace Management Business Analytics Operations Analytics Data Analytics
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Cover image for Netflix Movies Analysis Dashboard using
Netflix Movies Analysis Dashboard using Power BI Project Overview Interactive Power BI dashboard analyzing Netflix movie trends, popularity, ratings, genres, and language distribution. KPIs Total Movies Total Votes Total Popularity Average Rating Dashboard Features Top 10 Movies by Popularity Release Year Trend Movies Distribution by Language Genre Analysis Popularity vs Vote Average Tools Used Power BI Power Query DAX Data Modeling
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Cover image for Banking Data Analysis Dashboard Develop a
Banking Data Analysis Dashboard Develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers Solution – With our dashboards which are created using Power BI latest tools helps the company to make a decision based on the applicant’s profile like if the applicant is likely to repay the loan then approving the loan otherwise not. About Dataset – This dataset basically contains information about bank details ,various client details which consists of multiple tables which are interlinked with each other through keys like primary key and foreign key. The various tables are Banking Relationship, Client-Banking, Gender, Investment Advisor and Period. KPI’S: In which followings KPIS are present : Total Clients : Total Clients KPI represents total number of clients in banking. Total Clients = DISTINCTCOUNT('Clients - Banking'[Client ID] ) Total Loan : Total Loan gives you information about the bank loan + Business lending + credit cards balance of particular investor , gender. Total Loan = [Bank Loan] + [Business Lending] + [Credit Cards Balance] Bank Loan : Bank Loan gives you information what is the loan amount of loan to be repaid by the client to bank. Bank Loan = SUM('Clients - Banking'[Bank Loans] ) Business Lending : Business lending gives you information about the loan amount given to small business. Business Lending = SUM('Clients - Banking'[Business Lending] ) Total Deposit Total Deposit gives you information about the amount deposited by particular investors in bank Total Deposit = [Bank Deposit] + [Savings Account] + [Foreign Currency Account] + [Checking Accounts] Total Fees : Total Fees is nothing but the amount charged by the bank for account set-up , maintenance charges etc. Total Fees = SUMX('Clients - Banking' , [Total Loan] * 'Clients - Banking'[Processing Fees] ) Bank Deposit : Bank deposit is the money put in the bank. Bank Deposit = SUM('Clients - Banking'[Bank Deposits] ) Checking Account Amount : Checking account amount is nothing but which offers easy access to your money for daily transactional needs. Checking Accounts = SUM('Clients - Banking'[Checking Accounts] ) Total CC Amount : Total CC Amount is a short-term source of financing for a company by a bank. Total CC Amount = SUM('Clients - Banking'[Amount of Credit Cards] ) Saving Account Amount : A savings account is an interest-bearing deposit account held at a bank. Savings Account = SUM('Clients - Banking'[Saving Accounts] ) Foreign Currency Amount : Foreign Currency Account means an account held in a currency that is not the currency of India or Bhutan or Nepal. Foreign Currency Account = SUM('Clients - Banking'[Foreign Currency Account] ) Engagement Account : Engagement Banking is nothing but puts the customer at the center and aims to deliver the digital experiences they expect. Engagment Length = SUM('Clients - Banking'[Engagment Days]) Credit Cards Balance : It is the total amount of money currently owned by a cardholder to their credit card bank. Credit Cards Balance = SUM('Clients - Banking'[Credit Card Balance] ) 📊 Tools Used Excel (Data Cleaning) SQL (Data Processing) Python (EDA) Power BI (Dashboard) 📈 Key Insights Private banks dominate loan distribution European segment shows highest deposits Low-income customers have high loan dependency
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Cover image for Blinkit-Sales-Dashboard This project is an interactive
Blinkit-Sales-Dashboard This project is an interactive Power BI dashboard developed to analyse Blinkit's sales performance, outlet operations, product distribution, and customer-related metrics. The dashboard converts raw retail sales data into meaningful business insights through visualisation and KPI tracking. The objective of this project is to monitor business performance, identify sales trends, evaluate outlet efficiency, and support data-driven decision-making. Key KPIs Total Sales: $1.20M Average Sales: $140.99 Number of Items: 8,523 Average Rating: 3.9 Dashboard Features Sales Analysis Tracked total revenue and average sales performance to understand overall business growth. Outlet Performance Analysed outlet sales based on the following: Outlet Size Outlet Location Type Outlet Establishment Year Outlet Type Product Category Analysis Identified top-performing product categories contributing the highest revenue. Customer & Product Insights Compared low-fat and regular products to understand purchasing trends and monitored customer ratings for performance evaluation. Interactive Filters Added slicers for: Outlet Location Type Outlet Size Item Type Outlet Type Outlet Identifier These filters allow users to dynamically explore data and generate business insights. Project Outcome This project demonstrates practical skills in business intelligence, KPI reporting, analytical thinking, and dashboard development using Power BI. It highlights the ability to transform raw business data into business insights for decision-making. Developed By Sunny Mehta
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SQL(3)
Daniyal Shaikh's avatar
Daniyal Shaikh
Pune, India
Expert Data Analyst & Machine Learning Specialist | Python
Expert Data Analyst & Machine Learning Specialist | Python
Cover image for E-Commerce Reverse Logistics Analysis and Prediction
E-Commerce Reverse Logistics Analysis and Prediction
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Cover image for Online Retail Price Analysis - Flipkart vs Amazon
Online Retail Price Analysis - Flipkart vs Amazon
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Cover image for Flipkart Laptop Web Scraping Project
Flipkart Laptop Web Scraping Project
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SQL(1)
Ashish Kumbhare's avatar
Ashish Kumbhare
Pune, India
Senior Software Engineer |Mobile & Web Application Developer
New to Contra
Senior Software Engineer |Mobile & Web Application Developer
Cover image for One auditable system that links
One auditable system that links regulations, risks, documents, and corrective actions — so medical device companies are always inspection-ready.
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Cover image for AuctionIQ is an AI-powered platform for
AuctionIQ is an AI-powered platform for discovering and analyzing Indian bank auction properties. Banks auction properties when borrowers default on loans. These properties are often 15–35% below market price but carry legal risks most buyers don't understand. AuctionIQ helps investors by: Browsing live auction listings from banks across India AI Risk Scoring — 1–10 score based on possession type, SARFAESI status, DRT cases, bank type and discount Property Detail Analysis — legal red flags, payment schedule, due diligence checklist PDF Upload — admin uploads bank auction notice PDFs, Claude extracts listing data automatically ROI Calculator — estimate returns before bidding Smart Alerts — get notified when properties matching your criteria go live PDF Reports — detailed property analysis reports on demand Users: Free (browse only) → Pro ₹499/mo (risk scores, alerts, AI chat) → Investor ₹1499/mo (reports, ROI tools) Tech: React + Node.js + PostgreSQL + Claude AI (Anthropic)
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Cover image for An introductory landing page for
An introductory landing page for one of my client . Basic, Crisp & Clear.
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Cover image for Developed and enhanced the Caringly
Developed and enhanced the Caringly Yours web and mobile application using React and React Native, implementing responsive UI components, improving accessibility, integrating REST APIs, fixing bugs, optimizing performance, and collaborating with cross-functional teams to deliver a seamless user experience across platforms.
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SQL(2)