Freelancers using Azure SQL Database

Freelancers using Azure SQL DatabaseFreelancers using Azure SQL Database

Projects People

Devowise Studios

Web Design Studio | Brand Identity | Framer & AI Solutions

$5k+: Earned

4x: Hired

5.0: Rating

131: Followers

expert

Expert

+4

Web Design Studio | Brand Identity | Framer & AI Solutions

Cover image for Scalable Azure Data Engineering Solution Implementation

Scalable Azure Data Engineering Solution Implementation

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Cover image for Development of Studio Lumen's Yoga & Wellness Platform

Development of Studio Lumen's Yoga & Wellness Platform

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Cover image for Zynox AI Conversational Platform Development

Zynox AI Conversational Platform Development

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1

Cover image for Lets Have Pets | Full-Stack Mobile App Development

Lets Have Pets | Full-Stack Mobile App Development

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3

Azure SQL Database(1)

Muhammad Faizan

Islamabad, Pakistan

Full Stack Mobile Engineer

$10k+: Earned

6x: Hired

5.0: Rating

29: Followers

Full Stack Mobile Engineer

Cover image for DawoHub - Apps on Google Play

DawoHub - Apps on Google Play

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Cover image for Stable Portefeuille

Stable Portefeuille

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Cover image for .NET MAUI Developer for Mobile Application

.NET MAUI Developer for Mobile Application

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Cover image for Eagle Clean - Web & Mobile Application (Android) Tools: .NET...

Eagle Clean - Web & Mobile Application (Android) Tools: .NET (MVC), .NET MAUI, Figma, Azure ✦ Description: Custodian's Proof of Work(PoW) developed for Zebra devices. ✦ Client: USPS Contribution: • Developing a web portal with .NET MVC. • Developing .NET MAUI Mobile Application Features. • Implementing pixel-perfect designs for Android from Figma. • Integrating mobile features with .NET Core APIs. • Conducting code reviews and technical discussions. • Implementing version control and facilitating App Store deployment. #dotnet #maui #figma #mobile #android #azure #web

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Azure SQL Database(1)

Full-Stack Developer | SaaS, AI & Web Apps

5.0: Rating

7: Followers

Full-Stack Developer | SaaS, AI & Web Apps

Cover image for Recently completed a full Odoo

Recently completed a full Odoo 14 → 18 migration and custom build for a multi-store cosmetics retailer. The project went far beyond a standard migration. ⚡ 12,000+ products migrated 🌐 Arabic & English catalogue 🏪 4 retail POS locations 🚚 Zone-based delivery across 600+ cities 💄 Custom makeup shade & variant experience 🔎 Advanced search and category filtering 💳 Custom website & POS pricing 🛠️ Legacy customizations rebuilt as maintainable Odoo 18 modules The goal wasn't simply to move the existing system to a newer Odoo version — it was to rebuild the underlying workflows properly while preserving critical business data. Tech: Odoo 18 · Python · PostgreSQL · OWL · QWeb #Odoo #OdooDevelopment #OdooMigration #ERP #eCommerce

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Cover image for AI Chatbots Should Do More

AI Chatbots Should Do More Than Just Answer Questions 🤖 I’ve been building AI chatbot solutions that go beyond basic Q&A. A useful business chatbot should be able to understand your company’s knowledge, answer customer questions accurately, capture and qualify leads, trigger workflows, and connect with the tools your team already uses. Recent solutions I’ve worked on include: → Custom knowledge-base / RAG chatbots → OpenAI & LLM integrations → Website AI assistants → WhatsApp & messaging integrations → Lead capture and qualification → CRM & API integrations → Human handoff workflows → Conversation history & analytics → Custom admin dashboards The goal is simple: turn AI into a practical business tool—not just another chat window. If your business has repetitive customer questions, manual lead handling, or information scattered across documents and systems, a custom AI assistant can automate a significant part of that workflow.

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350

Cover image for 🔧 Another website back online.
Recently

🔧 Another website back online. Recently worked on Luna’s Ascension, a website that had gone down and was no longer functioning correctly. I diagnosed the underlying website/server issues, restored the site, resolved the problems preventing it from running properly, and brought the platform back online. Website recovery projects aren’t always about building something new — sometimes the real challenge is understanding an existing system, identifying what broke, and restoring it without disrupting the parts that already work. Work included: • Website troubleshooting & recovery • Server-side issue diagnosis • Existing codebase debugging • Website functionality restoration • Production environment fixes • Stability checks after recovery 🌐 Luna’s Ascension From broken websites to full-stack applications, I help businesses diagnose technical problems and get their platforms running reliably again.

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Cover image for Custom WooCommerce Delivery & Shipping Quote System

Custom WooCommerce Delivery & Shipping Quote System

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Azure SQL Database(1)

Copenhagen, Denmark

Optimising businesses with data and business intelligence.

18: Followers

Optimising businesses with data and business intelligence.

Cover image for Creating a Dimensional Model (Star Schema) in Databricks

Creating a Dimensional Model (Star Schema) in Databricks

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Cover image for Telco Business Intelligence Implementation

Telco Business Intelligence Implementation

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Cover image for Sales and Marketing Analytics Implementation for Verisure

Sales and Marketing Analytics Implementation for Verisure

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378

Cover image for Dashboard & Report Creation - Telco

Dashboard & Report Creation - Telco

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Azure SQL Database(1)

Karachi, Pakistan

Backend Engineer | DevOps | Linux, AWS, Docker,System Design

6: Followers

Backend Engineer | DevOps | Linux, AWS, Docker,System Design

Cover image for NestJS Donation Backend for School Allocation and Tax Credits

A good donation flow needs dependable systems behind the screens. For Gabriel SGO, I built the NestJS/PostgreSQL backend for donor and school workflows, with PayPal donation processing, Redis/BullMQ background jobs, Mailgun email, and AWS delivery. The public experience helps Ohio donors estimate a contribution, choose a school, and understand the tax-credit process. I documented the backend work and real site screens here: https://contra.com/p/Ls6L2o7Y-gabriel-sgo-or-donation-and-school-allocation-backend

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Cover image for Built a Node.js/TypeScript Pipeline for Madden Draft Classes

A college football roster is more than a list of stats when the goal is a Madden-ready draft class. For GridIronGC, I built the Node.js/TypeScript backend, conversion pipeline, and API, with Python stages and OpenAI-assisted data normalization. The live SaaS supports manual conversion, bulk uploads, and draft-class organization. I shared the real product screens and backend context in the case study: https://contra.com/p/3qRLLQ6c-grid-iron-gc-or-ai-assisted-player-conversion-backend

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Cover image for Multi-Tenant ESG Reporting Backend with NestJS and AWS

ESG reporting is a workflow problem as much as a data problem: teams need applicability rules, evidence, consolidation, review, and approvals before a report is ready. For OriginSustain, I built NestJS/TypeScript backend services, PostgreSQL and Redis/BullMQ processing, and AWS/Docker delivery with CI/CD. OpenAI API and RAG support part of the reporting flow. The screens here are from the product’s illustrative command center; the numbers are demo values, not client outcomes. The full backend case study: https://contra.com/p/WvNXbmQR-origin-sustain-or-multi-tenant-esg-reporting-backend

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Azure SQL Database(1)

Data Analyst | Data Engineer

6: Followers

Data Analyst | Data Engineer

Cover image for Azure End-to-End Data Pipeline Design

Azure End-to-End Data Pipeline Design

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Cover image for Sir-Muguna/end-to-end-airflow-data-pipeline

Sir-Muguna/end-to-end-airflow-data-pipeline

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Cover image for Sir-Muguna/healthcare_data_pipeline

Sir-Muguna/healthcare_data_pipeline

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Azure SQL Database(1)

Denys Dumynskyi

Full-stack developer delivering end to end software products

7: Followers

Full-stack developer delivering end to end software products

Cover image for MyCarrier: DevOps & Cloud Transformation

MyCarrier: DevOps & Cloud Transformation for Scalable Logistics SaaS Preview description: We helped MyCarrier move toward cloud-ready DevOps: adapted the system for microservices, implemented CI/CD and test automation, moved workloads to Azure, achieved 99.9% availability, decreased update delivery time to minutes. Full description: Problem MyCarrier was growing fast, and the monolithic codebase became harder and more expensive to maintain. It was heavy, resource-consuming, and less suitable for the client’s long-term scaling plans. The team also needed faster delivery processes, but changes in the codebase and cloud migration had to stay unnoticed by users. Their logistics workflows could not be interrupted. Solution IT Craft aligned the project infrastructure with a microservices architecture and prepared the system for cloud operation. The team started with a technical audit and developed the migration strategy consisting of 20-28 items. The process was divided into pre-migration, migration, and post-migration phases, thus the product could keep running while infrastructure changed step by step. The team moved non-critical standalone jobs to the cloud, connected virtual machines to Azure Storage Account, moved logging to MongoDB Atlas, and transferred user data and related processes to the cloud. New app functionality was released as microservices. IT Craft also designed and fine-tuned a CI/CD pipeline, automated infrastructure deployments, and reduced update delivery time through regression test automation. Results · Achieved 99.9% system availability. · Maintained high fault tolerance using three shared clusters. · Reduced update delivery time from hours to minutes. · Enabled Sprint focus changes every two weeks based on client priorities. Tech Stack: · Azure Cloud · MongoDB Atlas · Azure SQL · Azure App Service · Azure Functions · Azure Storage Account

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Cover image for Legal Beagle – Mobile App

Legal Beagle – Mobile App Development for Legal Service Automation Preview description: Legal Beagle needed legal services to work properly on mobile. We delivered Android and iOS apps with five-minute onboarding, secure chat, video calls, e-signatures, and data that stays current across devices. Full description: Problem Attorneys and clients at Legal Beagle were stuck moving paper between tools that didn't connect. The client wanted one legal SaaS product that lived on people's phones: secure messaging, fast attorney onboarding, e-signatures, and a shared space to work a case, same on Android, same on iOS. Solution IT Craft provided full-cycle mobile app development for Android and iOS. The team reviewed legal workflows, mapped mobile user flows, and built real-time features for secure messaging, video and audio consultations, document exchange, e-signatures, and case collaboration. Both apps were built to keep user data current across devices, so lawyers and clients could continue their work without losing context. IT Craft also integrated CRM systems and digital signature tools, including Clio and DocuSign, and set up cloud infrastructure for secure legal service delivery. The product included compliance-focused security, data protection, and a white-label setup for future expansion into new legal markets. Results · Delivered Android and iOS apps for a legal SaaS platform. · Added secure chat, video consultations, document workflows, and e-signatures. · Enabled real-time synchronization across mobile devices. · Reduced mobile attorney onboarding to 5 minutes. · Integrated CRM and digital signature tools, including Clio and DocuSign. · Developed a white-label version for expansion into new legal markets. Tech Stack: · Kotlin · Android SDK · Swift UI Kit · PHP 8 · Symfony 6 · API-Platform · Node.js · AWS ECS (Fargate) · Docker · Firebase · PostgreSQL · MySQL

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Cover image for Adorama: 13 Years of 24/7

Adorama: 13 Years of 24/7 Support for a $500M+ E-Commerce Platform Preview description: We provided long-term 24/7 technical support for Adorama’s $500M+ ARR e-commerce platform, helping maintain 99.8%+ uptime, ship 2 releases per week, and keep team turnover at around 12%. Full description: Problem Adorama needed a long-term technology partner capable of supporting a complex e-commerce ecosystem while maintaining business continuity, preserving product knowledge, and scaling engineering capacity as the platform evolved. Solution Over 13 years of collaboration, IT Craft scaled from a single engineer to a 54-person team supporting multiple areas of the platform. Our engineers became part of the client’s product cycle, supporting frontend, backend, testing, monitoring, platform maintenance, and infrastructure optimization. The team helped keep the platform stable while releasing updates on a predictable schedule. IT Craft also supported recruitment, onboarding, team management, and retention, making sure new specialists joined quickly and product knowledge stayed inside the team. This setup gave Adorama continuous support for a high-revenue e-commerce platform instead of short-term development help. Results Maintained 99.8%+ uptime across a $500M+ ARR e-commerce platform. Scaled the dedicated team from 5 to 54 specialists. Delivered an average of 2 production releases per week. Maintained team turnover at approximately 12% throughout long-term cooperation. Tech Stack: C# ASP.NET (http://ASP.NET) .NET Framework JavaScript jQuery ASP MVC MS SQL

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Cover image for Role-Based AI FAQ System: Right

Role-Based AI FAQ System: Right Answer for Every User, Instantly Preview description: We built a modular, AI-assisted FAQ platform where the LLM acts as an intelligent query router to a centralized knowledge base, reducing support workload by 40%–70% and providing public, investors, partners, and employees with accurate information. Problem The client is a manufacturer of advanced multifunctional materials. He wanted to ensure all interested users had access to relevant information quickly, reliably, and without placing an extra burden on the company’s experts. A conversational interface was required to enable users to ask questions in natural language and receive responses while keeping AI behavior controlled and transparent. Solution We designed a standalone OpenAI-powered FAQ module with a controlled-response engine that generates answers based on predefined information, taking roles and permissions into account. As it is separated from the client’s Vercel-based website, the integration takes place via an iframe, while account management and access level assignment are handled within Vercel CMS. When a user asks a question, the system filters the query based on the identified role (public, investor, partner, or employee) to eliminate data leakage. Then it applies the vector search to deliver an answer that is stored in the admin panel. The system also ensures that each response is accompanied by documents and links to Vercel pages based on available tags. Results The FAQ system reduces the average time needed to find information from 10–15 minutes to under 30 seconds through conversational search and centralized knowledge management. The support workload decreased by 40%–70%. Onboarding time for employees, partners, and investors decreases by 50–80% while the system grants instant access to structured answers. Tech stack: - OpenAI API for query understanding - VectorDB (pgvector) database for semantic question matching - PostgreSQL as a structured database for storing FAQs, tags, roles, and permissions - Python/FastAPI for backend and admin panel development - iframe for secure communication - Vercel CMS Vercel-hosted frontend integration

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Azure SQL Database(1)

Python & Django developer for web apps and APIs

New to Contra

Python & Django developer for web apps and APIs

Cover image for Spent an afternoon this week

Spent an afternoon this week chasing down why a client's order dashboard took around 2.5s to load. Turned out the page was firing 300+ database queries. Classic N+1 problem. Every order in the loop was making its own separate trip to the DB just to grab the customer and product. One line fixed it. select_related() tells Django to pull the related rows in a single JOIN instead of querying them one at a time. 312 queries down to 3. Page load went from 2.4s to 0.08s. The annoying part is this never shows up on small datasets in dev. It only bites you in production once the table grows. So now I always check the query count in Django Debug Toolbar before shipping any list view. What's the worst N+1 you've run into?

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Cover image for An AI-driven trading engine that

An AI-driven trading engine that analyzes 200-DMA breakouts and real-time market sentiment to generate long/short recommendations. It scans 1,500+ NSE stocks in under 2 seconds and uses generative AI to build option strategies, delivering event-driven trade signals end-to-end. I built the full Django backend, async APIs, and the signal-generation logic. Accomplishments and responsibilities: Built an AI-driven trading engine analyzing 200-DMA breakouts and market sentiment, generating long/short signals with ~70% directional accuracy — outperforming baseline strategies by 35%; Integrated generative-AI insights for automated option-strategy creation (spreads, straddles, condors), improving Sharpe ratio by 1.6× and cutting manual analysis time by 60%; Developed a Django backend with async APIs scanning 1,500+ NSE stocks in under 2 seconds, achieving 40% lower latency with event-driven alerts.

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Cover image for Hit a classic Django trap

Hit a classic Django trap this week and figured it's worth sharing since so many people are running into it now. I was adding an LLM feature to a Django app. The AI call takes a few seconds, so naturally I made the view async so it doesn't block a worker while waiting. Wrote the async view, called the ORM like I always do, and boom: SynchronousOnlyOperation: You cannot call this from an async context. Turns out Django's ORM can't just be called normally inside async code. The classic sync API isn't safe in an event loop, so Django protects it and throws this error instead. The fix is simpler than most people think. Since Django 4.1 the ORM has async versions of everything, same names with an "a" prefix. So objects.get() becomes await objects.aget(), create() becomes acreate(), save() becomes asave(). For loops over querysets, async for works directly. And for old sync code or third party libraries you can't change, wrap them with sync_to_async(). Why this matters right now: everyone is bolting AI features onto Django apps, and LLM calls are exactly the slow I/O that async is made for. Which means a lot of devs who never touched async Django are suddenly hitting this error for the first time. One honest caveat: transactions still don't fully work in async mode, so if you need atomic blocks, keep that path sync and wrap it. Anyone else made the jump to async views yet, or still happily on WSGI?

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Cover image for An AI-powered news platform that

An AI-powered news platform that delivers concise, real-time AI-industry updates to 2,500+ active users. It scrapes and aggregates 500+ sources daily, removes duplicates, and uses generative-AI summarization to cut reading time significantly while surfacing the most relevant stories. I built the backend responsible for scraping, deduplication, and the LLM summarization pipeline.

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Azure SQL Database(2)