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Camunda enterprise workflow automation platform
I led the architecture and development of an enterprise workflow orchestration platform built around Camunda and BPMN, designed to automate complex, mission-critical business processes.
I developed scalable backend services using Node.js/NestJS and Python/FastAPI, with React and Next.js powering process management and analytics experiences. I also integrated OpenAI and LangChain to introduce AI-driven decision capabilities, enabling workflows to interpret documents, classify incoming data, make contextual decisions, and automatically trigger actions across connected services.
To support high-volume workflow execution, I designed a distributed, event-driven processing architecture using queue-based systems, reducing workflow latency by 35%. I optimized GraphQL and REST APIs with Redis caching and asynchronous processing, improving API response times by 30%.
I also built interactive process analytics dashboards that gave teams visibility into workflow status, execution metrics, failures, and bottlenecks, contributing to a 40% improvement in operational efficiency.
The platform was containerized with Docker and deployed through Kubernetes-based CI/CD pipelines, reducing release cycle time by 25%.
Technologies: Camunda, BPMN, Node.js, NestJS, Python, FastAPI, React, Next.js, TypeScript, OpenAI API, LangChain, GraphQL, REST APIs, Redis, Docker, Kubernetes, CI/CD
The network for creativity
Join 1.25M professional creatives like you
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that gold bottle swirl looks so good. how long was that clip?
I analyzed 3M+ job postings across 161 countries and 93K companies to identify hiring trends, in-demand roles, work-mode patterns, and salary insights.
I used Python for data cleaning and preprocessing, SQL for analysis, and Power BI to build an interactive dashboard.
Key areas analyzed:
• Global hiring demand
• Top job roles by demand
• Remote vs. onsite/hybrid work patterns
• Average salary trends
• Monthly hiring trends
This project helped me turn a large dataset into clear, actionable insights for understanding the global job market.
AI-Assisted Sales Intelligence Dashboard
Project Overview
This project is an end-to-end Sales Analytics solution built using Python, SQL, and Power BI.
The workflow includes:
Data Cleaning using Python (Pandas)
Data Storage using SQLite
Business Analysis using SQL Queries
Interactive Dashboard using Power BI
Automated Business Report Generation using Python
The goal of this project is to analyze sales performance, identify profitable products, monitor regional performance, and generate business recommendations.
Dataset
Dataset: Sample Superstore Dataset
Records: 9,994
Features:
Order Details
Customer Information
Product Information
Sales
Profit
Discount
Region
Category
Sub-Category
Tools & Technologies
Python
Pandas
SQLite
SQL
Power BI
Data Visualization
Business Intelligence
Project Workflow
CSV Dataset
↓
Data Cleaning (Python)
↓
SQLite Database
↓
SQL Analysis
↓
Power BI Dashboard
↓
AI Business Report
Dashboard Pages
Executive Overview Total Sales Total Profit Total Orders Profit Margin Regional Sales Analysis Category Performance Analysis
Product Performance Analysis Top Profitable Products Loss-Making Products Profit by Category Sales by Sub-Category
Sales Trends Analysis Sales Trends Over Time Profit Trends Seasonal Performance Insights Key Business Insights West Region generated the highest sales. Technology is the most profitable category. Several products generate significant losses and require pricing review. Sales performance varies significantly across regions. Automated AI Report The project includes a Python-based reporting system that automatically generates:
Total Sales
Total Profit
Business Insights
Strategic Recommendations
Author
Gourav Jangid
Aspiring Data Analyst
Open to Data Analytics Opportunities
