Freelance Data Engineers in PakistanFreelance Data Engineers in Pakistan
ProjectsPeople
Toolshed (Data, Automation, AI Agents, Framer, Retool)'s avatar
Toolshed (Data, Automation, AI Agents, Framer, Retool)
maxLahore, Pakistan
Data, Automation, AI Agents, Framer, Retool, Bubble
$100k+
Earned
7x
Hired
5.0
Rating
64
Followers
TOP_INDEPENDENT

Top

FRAMER_EXPERT

expert

+1
Data, Automation, AI Agents, Framer, Retool, Bubble
Cover image for Financial & Usage Analytics for YC-backed healthcare startup
Financial & Usage Analytics for YC-backed healthcare startup
1
30
Cover image for Streamlining Operations for House of Sylas
Streamlining Operations for House of Sylas
2
27
Cover image for Construction FP&A & Revenue AI Platform
Construction FP&A & Revenue AI Platform
6
307
Cover image for Luxury Framer Architecture Portfolio
Luxury Framer Architecture Portfolio
1
21
Data Engineer(3)
Faarid Qureshi's avatar
Faarid Qureshi
proKarachi, Pakistan
Full-Stack & AI Developer | Next.js, AI Agents
$25k+
Earned
7x
Hired
5.0
Rating
50
Followers
Full-Stack & AI Developer | Next.js, AI Agents
Cover image for Another successful knowledge management migration
Another successful knowledge management migration completed. This project involved migrating approximately 200 pages across 48 Quip files into Notion before Quip's sunset. Since there isn't a reliable way to automate the migration, I manually exported and rebuilt the content while preserving structure, formatting, and hierarchy. To make the workspace more valuable than the original, I used Notion AI to organize information, extract action items, clean up documentation, and transform scattered notes into a searchable, future-ready knowledge base. A smooth migration with zero compromise on content quality and a much better workspace for the team going forward.
3
5
546
Cover image for Student Grit Score Data System
Student Grit Score Data System
1
8
Cover image for The Student Grit Score Data
The Student Grit Score Data System is a real-time, effort-focused performance tracking system designed to replace traditional report cards with continuous feedback centered on consistency, engagement, and growth. The system captures daily teacher-observed signals across effort, mastery, reading, and behavior, calculates a daily and weekly Grit Score, and presents insights through clear, role-appropriate dashboards for teachers and parents.
2
385
Cover image for Legacy Building: Media Export & Community Features
Legacy Building: Media Export & Community Features
0
7
Data Engineer(3)
Devowise Studios's avatar
Devowise Studios
proPakistan
Web Design Studio | Brand Identity | Framer & AI Solutions
$5k+
Earned
3x
Hired
5.0
Rating
124
Followers
FRAMER_EXPERT

expert

ANYTHING_EXPERT

Expert

+4
Web Design Studio | Brand Identity | Framer & AI Solutions
Cover image for Qlik Sense BI Solution Development for Interactive Analytics
Qlik Sense BI Solution Development for Interactive Analytics
1
8
Cover image for Scalable Azure Data Engineering Solution Implementation
Scalable Azure Data Engineering Solution Implementation
2
18
Cover image for Automated ELT Pipeline Development
Automated ELT Pipeline Development
1
26
Cover image for Epos Now - Azure-Powered Data Engineering Pipeline
Epos Now - Azure-Powered Data Engineering Pipeline
2
35
Data Engineer(5)
Taziem Uddin's avatar
Taziem Uddin
proLahore, Pakistan
I install done-for-you analytics in high-ticket businesses.
$10k+
Earned
2x
Hired
2
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
1
33
Cover image for No More Mondays: The Same Close Rate, Told Two Ways
No More Mondays: The Same Close Rate, Told Two Ways
1
40
Cover image for No More Mondays: $1M+ Traced, Deal by Deal
No More Mondays: $1M+ Traced, Deal by Deal
1
6
Cover image for No More Mondays: Ask Your Data, Get Answers in Seconds
No More Mondays: Ask Your Data, Get Answers in Seconds
2
37
Data Engineer(3)
Advancing Data Solutions's avatar
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
0
1
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.
0
103
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
0
153
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.
0
142
Data Engineer(4)
Mirza Umer's avatar
Mirza Umer
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%.
0
42
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.
0
75
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
1
136
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.
0
43
Data Engineer(2)
Muhammad Arsalan's avatar
Muhammad Arsalan
Karachi, Pakistan
Data Engineer | Web Scraping | SQL | BI Dashboards
Data Engineer | Web Scraping | SQL | BI Dashboards
Cover image for Problem: Users needed a faster way
Problem: Users needed a faster way to find the most relevant counselor based on specialization and available data. Solution: Built a recommendation workflow using PySpark for data processing and Redis for fast lookup and recommendation serving. Tools: Python, PySpark, Redis, Docker, Docker Compose Result: Created a containerized recommendation system that processes counselor data and returns relevant matches efficiently.
0
73
Cover image for Problem: Batch data processing was not
Problem: Batch data processing was not suitable for real-time analytics and scalable cloud-based data ingestion. Solution: Created a real-time streaming pipeline using Kafka on AWS EC2, stored processed data in S3, cataloged it with AWS Glue, and queried it with Amazon Athena. Tools: Python, Apache Kafka, AWS EC2, Amazon S3, AWS Glue, Amazon Athena, Pandas Result: Built an end-to-end cloud data streaming workflow that supports real-time ingestion, storage, cataloging, and SQL-based analytics.
0
88
Cover image for Problem: Financial data from stocks and
Problem: Financial data from stocks and crypto APIs was scattered, refreshed manually, and not ready for analytics or ML use. Solution: Built an Apache Airflow pipeline to collect, transform, validate, and load real-time financial data from multiple APIs into PostgreSQL, MongoDB, AWS RDS, and Qdrant. Tools: Apache Airflow, Python, PostgreSQL, MongoDB, AWS RDS, Qdrant, APIs Result: Automated sub-hourly data refresh, processed thousands of records daily, and delivered clean data for dashboards, analytics, and vector search.
0
93
Cover image for Problem: Finance data was difficult to
Problem: Finance data was difficult to analyze across sales, profit, orders, discounts, countries, and customer segments. Solution: Created an interactive Power BI dashboard with KPI cards, sales trends, profit analysis, country performance, and segment-level insights. Tools: Power BI, Power Query, DAX, Excel, Data Modeling Result: Delivered a clear executive dashboard for tracking financial performance, identifying trends, and making faster business decisions.
0
77
Data Engineer(3)