Freelance Data Engineers in Punjab

Freelance Data Engineers in PunjabFreelance Data Engineers in Punjab

Projects People

Toolshed (Data, Automation, AI Agents, Buildship, Framer)

maxLahore, Pakistan

Data, Automation, AI Agents, Buildship, Framer, Bubble

$100k+: Earned

7x: Hired

5.0: Rating

74: Followers

Top

expert

+1

Data, Automation, AI Agents, Buildship, Framer, Bubble

Cover image for Financial & Usage Analytics for YC-backed healthcare startup

Financial & Usage Analytics for YC-backed healthcare startup

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Cover image for Streamlining Operations for House of Sylas

Streamlining Operations for House of Sylas

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30

Cover image for Construction FP&A & Revenue AI Platform

Construction FP&A & Revenue AI Platform

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314

Cover image for Luxury Framer Architecture Portfolio

Luxury Framer Architecture Portfolio

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Data Engineer(3)

proLahore, Pakistan

I install done-for-you analytics in high-ticket businesses.

$10k+: Earned

3x: Hired

4: Followers

I install done-for-you analytics in high-ticket businesses.

Cover image for No More Mondays: Built From Scratch, One Warehouse

No More Mondays: Built From Scratch, One Warehouse

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Cover image for No More Mondays: The Same Close Rate, Told Two Ways

No More Mondays: The Same Close Rate, Told Two Ways

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Cover image for No More Mondays: $1M+ Traced, Deal by Deal

No More Mondays: $1M+ Traced, Deal by Deal

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Cover image for No More Mondays: Ask Your Data, Get Answers in Seconds

No More Mondays: Ask Your Data, Get Answers in Seconds

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Data Engineer(3)

Advancing Data Solutions

proLahore, Pakistan

AI & Data Engineer|Data & AI Architect| AWS|Azure |Snowflake

AI & Data Engineer|Data & AI Architect| AWS|Azure |Snowflake

Cover image for Real-Time Analytics Platform on AWS

Real-Time Analytics Platform on AWS

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Cover image for Oracle to Amazon RDS PostgreSQL
Designed

Oracle to Amazon RDS PostgreSQL Designed and Implemented a secure, scalable architecture to migrate an on-prem Oracle database to Amazon RDS for PostgreSQL. Data Flow: Modeled end-to-end flow, used AWS Glue (SCT) for schema conversion and DMS for full-load and CDC. Stored artifacts in S3 and loaded into RDS. Services: Glue for conversion, DMS for Migration , S3 for artifacts, and RDS as target. Security: IAM roles with least privilege, KMS for encryption at rest. HA & DR: Architected in us-east-1 with Glue, DMS, RDS, S3, and monitoring via CloudWatch.

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Cover image for Real-Time Analytics Platform on AWS

Real-Time Analytics Platform on AWS designed to ingest, process, and visualize high-volume streaming data from IoT devices and application logs. Ingestion: Device telemetry (IoT Core) and application logs are published into Amazon Kinesis Data Streams, followed by lightweight routing and enrichment using AWS Lambda. Processing & Storage: Amazon EMR performs heavy data transformations via Spark Structured Streaming, landing the curated output into Amazon S3 as partitioned Parquet files. AWS Glue then handles batch transformations before loading the data into Amazon Redshift for the analytics workload. Orchestration & Serving: AWS Step Functions coordinate the batch processing workflows. Finally, Amazon QuickSight generates actionable business dashboards while Amazon CloudWatch monitors operational metrics and alarms

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210

Cover image for Batch ETL Data Warehouse Modernization
Led

Batch ETL Data Warehouse Modernization Led modernization of a legacy batch ETL pipeline into a scalable AWS-based data warehouse. Replaced manual SQL jobs with an automated system loading diverse sources into Amazon Redshift. Ingestion: Daily CSVs and API data land in S3; S3 events trigger Lambda to launch Glue Crawlers and update the Data Catalog. Transformation: Glue PySpark jobs join and cleanse sales, inventory, and customer data, apply quality checks, and move errors to a quarantine path. Output: Partitioned Parquet files stored in S3 for loading into Redshift.

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177

Data Engineer(4)

Rawalpindi, Pakistan

Python Automation Developer |Scripts, Bots & Data Extraction

7: Followers

Python Automation Developer |Scripts, Bots & Data Extraction

Cover image for Python Web Automation Bot |

Python Web Automation Bot | Selenium Automation & Form Submission Key Features • Automated real-time website monitoring & form submission. • Human-like browser automation using Python & Selenium. • Intelligent page state detection & automatic error recovery. • Session persistence, configurable delays & proxy support. • Packaged as a Windows EXE for easy deployment. Business Value ✔ Eliminated repetitive manual monitoring. ✔ Improved workflow reliability & efficiency. ✔ Reduced downtime with automated recovery. ✔ Delivered scalable Python automation for enterprise workflows.

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Cover image for Python Job Scraper | Selenium

Python Job Scraper | Selenium Web Automation Key Features • Automated job scraping from Indeed Pakistan. • Extracted job titles, company names & job links. • Selenium automation with undetected ChromeDriver. • Structured data collection for further processing. • Reliable browser automation with dynamic page handling. Business Value ✔ Automated repetitive job data collection. ✔ Saved time with accurate data extraction. ✔ Reduced manual search efforts. ✔ Delivered scalable Python web scraping solutions.

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Cover image for Python Automation Platform for Regulatory

Python Automation Platform for Regulatory Monitoring & Data Extraction Key Features • Automated monitoring of regulatory websites and RSS feeds • Web scraping and structured data extraction using Python • Automated data processing and risk analysis • Multi-agent workflow for compliance automation • PDF and Excel report generation • API integration for intelligent document analysis Business Value ✔ Eliminates repetitive manual monitoring of regulatory updates ✔ Accelerates compliance review workflows ✔ Improves data accuracy through automated extraction ✔ Reduces time spent on compliance-related administrative tasks ✔ Generates structured reports automatically

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Cover image for AI Workflow Automation for Property

AI Workflow Automation for Property Search & Database Management Key Features • AI-powered workflow automation with n8n. • Intelligent property search using Supabase. • Stateful conversation & preference tracking. • Automated routing between database & Weather API. • Dynamic filtering, caching & workflow optimization. Business Value ✔ Reduced manual property search efforts. ✔ Faster responses with intelligent caching. ✔ Automated data retrieval & decision making. ✔ Scalable workflow automation for real estate applications.

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Data Engineer(3)

Sialkot, Pakistan

AI Chatbot and Automation Expert

New to Contra

AI Chatbot and Automation Expert

Cover image for The client was losing high-value

The client was losing high-value business leads due to delayed follow-ups and manual data entry across disconnected platforms (CRM, email, and advertising channels). The internal team was spending upwards of three hours a day manually copying lead data, resulting in data fragmentation and an average response time of over 12 hours. The Tech Stack 1. n8n (Advanced Workflow Orchestration). 2. Webhooks & REST APIs (Data Ingestion). 3. PostgreSQL / Airtable (Centralized Data Warehouse). 4. Slack API (Instant Internal Notifications). The Solution. I engineered an autonomous, multi-stage lead processing engine using n8n. The system captures inbound leads via instant webhooks from front-end marketing platforms, normalizes and enriches the data using secondary API lookups, and instantly populates the centralized data warehouse. Simultaneously, the workflow routes the lead based on custom logic (such as budget or region) and fires an immediate, structured notification to the sales team's Slack channel, allowing for a sub-5-minute response time. The Business Impact. The automated pipeline eliminated manual data entry, recovering roughly 15 hours of administrative time per week for the internal team. More importantly, reducing the lead response time from 12 hours down to under 5 minutes directly improved lead-to-opportunity conversion rates by over 30%.

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Cover image for The legal services firm required

The legal services firm required a highly secure, instantaneous method for employees to query a massive internal knowledge base of over 200 complex contracts and compliance documents. The existing manual search process was highly inefficient, taking employees an average of 15 minutes per query and creating significant operational bottlenecks. The Tech Stack 1. Python (Data Ingestion & Formatting). 2. LangChain (RAG Orchestration). 3. Pinecone (Vector Database). 4. OpenAI GPT-4 (Contextual Synthesis). 5. Streamlit (Custom Web Interface). The Solution I engineered a secure Retrieval-Augmented Generation (RAG) pipeline to automate document retrieval. The system ingests and chunks hundreds of legal documents, storing them in a Pinecone vector database. When an employee submits a query, the system retrieves the most relevant semantic chunks and uses GPT-4 to synthesize a coherent response. To ensure zero hallucinations regarding strict legal terminology, the retrieval pipeline is engineered to explicitly cite its sources (e.g., "According to NDA_Template.pdf, Page 4") for every generated answer. The Business Impact The chatbot successfully reduced the time employees spent searching for specific legal clauses from an average of 15 minutes per query down to mere seconds. This drastically improved team productivity, reduced billable hour bloat, and ensured absolute accuracy in document retrieval.

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Cover image for Umair Pets Clinic in Sialkot

Umair Pets Clinic in Sialkot had thousands of Facebook followers and strong reviews but no website or online booking system. I built them a complete booking platform on Base44 including a client booking flow, a staff dashboard, and an automated confirmation system that emails the pet owner and updates the dashboard the moment a booking is made. The live site is at umairpetsclinic.base44.app (http://umairpetsclinic.base44.app). Built for the Base44 Give It A Glow Challenge. #base44giveitaglowchallenge #GiveItAGlow @base44 https://x.com/MirzaUmerContra/status/2076317805700698525

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Cover image for Premier Dental's front desk was

Premier Dental's front desk was overwhelmed with repetitive appointment inquiries during peak hours. This volume caused excessive hold times, missed calls, and ultimately, lost booking opportunities. The staff needed a way to offload routine scheduling without sacrificing the natural, empathetic tone expected in healthcare. The Tech Stack 1. Vapi (Low-Latency Voice AI Infrastructure). 2. Custom LLM System Prompts (Conversational Logic). 3. REST API (Real-Time Calendar Synchronization). 4. Twilio (Telephony Integration). The Solution: I deployed an autonomous, low-latency AI voice agent named "Sarah" to handle inbound calls 24/7. Engineered on the Vapi platform, the agent answers calls instantly, greets patients naturally, and collects necessary intake details (name, preferred date, appointment type). Through REST API integrations, the system checks availability, confirms new bookings, and reschedules existing appointments in real time, while intelligently escalating complex edge cases to a human receptionist. The Business Impact: The voice agent successfully handled 1,240 inbound calls with an 87% resolution success rate and an average call duration of 2 minutes and 34 seconds. By eliminating front desk call overflow, the clinic ensured zero missed booking opportunities, allowing the human staff to focus exclusively on high-touch, in-person patient care.

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Data Engineer(2)

Mohammad Roshaan Butt

Rawalpindi, Pakistan

Data Engineer | Data Scientist

Data Engineer | Data Scientist

Cover image for streaming pipeline and data warehousing with stock market data

streaming pipeline and data warehousing with stock market data

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6

Cover image for Automated batch processing for POS transactions

Automated batch processing for POS transactions

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4

Cover image for E Commerce Analytics Pipeline

E Commerce Analytics Pipeline

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Data Engineer(3)

Lahore, Pakistan

ML AI | Backend | Computer Vision | GenAI | LLM Agents

New to Contra

ML AI | Backend | Computer Vision | GenAI | LLM Agents

Cover image for AI-Powered PDF Data Extraction
My role:

AI-Powered PDF Data Extraction My role: AI Data Processing and Extracton Engineer Organizations often struggle to extract structured and useful information from large volumes of unstructured PDF documents. I developed a flexible AI-powered data extraction solution that allows users to define the specific entities and fields they want to retrieve. The system processes different PDF formats, identifies relevant information, and converts it into structured, usable data. The solution reduces manual document processing, improves retrieval accuracy, and can be adapted to different document types and business requirements. A working demo link is attached.

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Cover image for LakeShield - AI-Powered Video Monitoring

LakeShield - AI-Powered Video Monitoring and Vessel Intelligence Platform I led the development of LakeShield as the Senior AI/ML Engineer and Lead Developer, taking the platform from initial research and experimentation to a scalable production system. My responsibilities included: 🔹 Designing the end-to-end AI and video-processing architecture 🔹 Building YOLO-based boat and vehicle detection pipelines 🔹 Developing object tracking and movement-analysis workflows 🔹 Implementing OCR for extracting boat registration information 🔹 Creating scalable pipelines for processing thousands of surveillance videos 🔹 Developing FastAPI backend services and automated data workflows 🔹 Building a Next.js analytics dashboard integrated with Supabase 🔹 Deploying and operating the AI pipeline on cloud GPU infrastructure 🔹 Optimizing model accuracy, inference speed, infrastructure costs, and reliability 🔹 Managing production monitoring, troubleshooting, maintenance, and continuous improvements The platform transforms raw surveillance footage into structured operational insights, enabling automated vessel monitoring, vehicle activity analysis, registration extraction, and reporting. This project involved complete technical ownership across Computer Vision, AI/ML, backend development, cloud infrastructure, data engineering, MLOps, and production operations. #ComputerVision #VideoAnalytics #ArtificialIntelligence #ObjectDetection #OCR #MLOps #FastAPI #NextJS #Supabase #CloudEngineering

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Cover image for Shelfr - AI-Powered Retail Shelf

Shelfr - AI-Powered Retail Shelf Intelligence Platform I led the development of Shelfr as the Senior Computer Vision Engineer and Lead Developer, taking the platform from the initial idea and system architecture through development, deployment, and production operations. My work included: 🔹 Designing the complete computer vision and backend architecture 🔹 Building product detection, shelf analysis, OCR, and image-processing pipelines 🔹 Developing APIs and scalable data-processing workflows 🔹 Deploying and managing production systems on GCP cloud servers 🔹 Optimizing model accuracy, processing speed, and infrastructure performance 🔹 Managing production monitoring, reliability, troubleshooting, and ongoing improvements 🔹 Leading technical decisions across AI, backend, cloud infrastructure, and DevOps The platform converts real-world retail shelf images into structured product and shelf-level insights, helping automate retail auditing, product visibility analysis, and inventory workflows. #ComputerVision #RetailAI #LeadDeveloper #AIEngineering #GCP #MLOps #Python #CloudEngineering

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Cover image for ⚖️ Built a French Legal

⚖️ Built a French Legal AI Assistant powered by advanced RAG and LLM technology. The system enables users to ask complex legal questions and receive accurate, context-aware answers grounded in French legal documents. Key features include: 🔹 Custom legal document ingestion and chunking 🔹 Metadata-based vector search 🔹 Hybrid retrieval and reranking 🔹 Agentic RAG workflows using LangGraph 🔹 Source-grounded answers with legal references 🔹 Private deployment on an Azure VM using locally hosted LLMs The main focus was improving retrieval accuracy, reducing hallucinations, and making large collections of legal documents easier to search and understand. #LegalAI #RAG #LLM #ArtificialIntelligence #LangGraph #Azure #GenerativeAI #MachineLearning

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Data Engineer(1)

Lahore, Pakistan

I build offline AI tools that make documents talk.

I build offline AI tools that make documents talk.

Cover image for AuraExtract — Intelligent Invoice &

AuraExtract — Intelligent Invoice & Receipt Data Extractor The extraction engine uses intelligent regex pattern matching that handles real-world invoice layouts — column-per-line PDF formats, inline tabular formats, and plain text documents. It detects 10 fields automatically and parses up to 20 line items per invoice. Supports PDF, TXT, and DOCX formats. Includes a raw text preview panel so users can verify exactly what the engine is reading. CSV export includes both the summary fields and full line items table — ready to open directly in Excel. Pure Python. Zero external dependencies beyond pypdf for PDF reading.

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Cover image for AuraSort scans any folder and

AuraSort scans any folder and automatically sorts files into named subfolders by type — Documents, Images, Videos, Audio, Code, Archives, and more. Files are renamed to clean, consistent lowercase format. Every operation is logged live on screen as it happens. Built with a Dry Run mode so users can preview exactly what will move before anything is touched. Full undo restores every file to its original location with one click. An HTML report is generated after each sort showing every file moved, every category created, and total time taken. Pure Python. Zero external libraries. Works on any machine without installation.

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Cover image for A fully offline document summarizer

A fully offline document summarizer built in pure Python. Uses TF-IDF scoring, position weighting, and Jaccard deduplication to extract the most important sentences from any PDF, DOCX, or TXT file — each labeled with a relevance percentage. The result looks like this: [1] [100% relevance] The algorithm achieved 94% accuracy on benchmark tests. [2] [81% relevance] Training was performed on 50,000 labeled samples. [3] [67% relevance] Results were validated using 5-fold cross validation. Supports PDF, Word, and TXT files. Saves summaries to your computer. Runs completely offline. No subscriptions, no API keys, no internet required.

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Cover image for Built AuraChat v3.0 — a

Built AuraChat v3.0 — a fully offline Document Intelligence desktop app in pure Python. Users upload any PDF, Word, or TXT file and ask questions in plain English. The system returns cited answers with confidence scores instantly. Technical highlights: — Custom NLP engine using TF-IDF scoring + hybrid token overlap analysis — 1,700× faster indexing than baseline on 500-page documents — Multi-threaded processing — UI never freezes during heavy indexing — Supports PDF, DOCX, and TXT file formats — Zero external APIs — runs completely offline on the user's machine — 23 production-grade bugs identified and resolved before delivery This is not a demo. This is production-ready software built with clean architecture, full error handling, keyboard shortcuts, chat export, source citations, and confidence indicators

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Data Engineer(1)