Freelancers using Amazon Redshift in Lahore

Freelancers using Amazon Redshift in LahoreFreelancers using Amazon Redshift in Lahore

Projects People

Advancing Data Solutions

proLahore, Pakistan

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

Real-Time Analytics Platform on AWS

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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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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Cover image for OnPrem .NET to AWS Migration

Migrated

OnPrem .NET to AWS Migration Migrated an on‐premises .NET web application and SQL Server database to AWS to achieve scalability, high availability, and cross‐region resilience. The legacy environment ran on Windows servers with a clustered SQL Server, resulting in high maintenance and limited elasticity. The new AWS architecture features EC2 Auto Scaling behind an Application Load Balancer (ALB), Amazon RDS for SQL Server with Multi‐AZ and a cross‐region read replica, and DNS failover via Route 53. ◦ Deployed the .NET application on an EC2 Auto Scaling group (Windows AMI) across private subnets in us‐east‐1. 5- Build Scalable Real-Time Data Analytics on AWS for Instant Insights Designed and implemented a real-time data analytics platform on AWS, enabling stream processing, transformation, and visualization for high-volume IoT and log data. This system was built to process and analyze millions of records per second, providing actionable insights for business decision-making. The solution leveraged AWS native services like Kinesis, Glue, Lambda, and Redshift, integrating Apache Spark Structured Streaming to handle real-time data ingestion and transformation. Airflow and Step Functions were used for workflow automation.

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Amazon Redshift(1)

Hamza Hameed

Lahore, Pakistan

Backend Python Engineer | Django, FastAPI, REST APIs & Cloud

New to Contra

Backend Python Engineer | Django, FastAPI, REST APIs & Cloud

Cover image for Fynite is a data-driven SaaS

Fynite is a data-driven SaaS platform designed to help businesses manage products, analyze market data, integrate external data sources, and create intelligent pricing strategies. I worked on the backend and data engineering side of the platform, building APIs, integrations, data pipelines, pricing workflows, and the infrastructure required to process and manage large volumes of product and pricing data. Key Features Dynamic Pricing Engine — Create and apply pricing strategies based on stock, demand, promotions, competitors, volume, and time-based rules. Product Management — Manage product catalogs, variations, SKUs, pricing, inventory, POS data, and dynamic pricing status. Competitor Pricing Intelligence — Collect and compare competitor pricing data to support data-driven pricing decisions. ETL & Data Pipelines — Ingest, transform, and synchronize data from multiple external sources. Third-Party Integrations — Built integrations with platforms such as Shopify, Square, Toast, Clover, NCR, and other data sources. Analytics & Dashboards — Provide revenue, net-value, price-index, competitor, and business performance insights. Pricing Strategies — Support promotion-based, stock-based, volume, time-based, demand-based, competitor-based, and fixed-markup pricing. Lead & Company Management — Manage leads, companies, assignments, sales tasks, and recruitment/business workflows within the broader platform. API Integrations — Connect external services and data providers through backend APIs and integration workflows. Scalable Data Architecture — Designed backend workflows for handling large datasets and continuously synchronized business data. My Role Backend Engineer / Python & Django Developer I focused primarily on the backend architecture and data layer, including: Designing and developing Django/REST APIs Building data ingestion and ETL workflows Integrating third-party platforms and APIs Implementing dynamic pricing logic and business rules Working with relational databases and analytical data stores Building automated data synchronization workflows Developing backend services for dashboards and analytics Handling product, pricing, inventory, competitor, and integration data Supporting cloud-based data processing and deployment Technology Python · Django · Django REST Framework · PostgreSQL · Amazon Redshift · AWS · Docker · REST APIs · Celery · ETL · Data Pipelines · Third-Party APIs

Cover image for DarrenCaddle is a recruitment-focused CRM

DarrenCaddle is a recruitment-focused CRM and lead management platform designed to help recruitment teams organize large volumes of leads, companies, candidates, job opportunities, and sales activities in one centralized system. I worked on the backend development and business logic, building the systems required to manage recruitment data, lead workflows, company assignments, sales tasks, and administrative operations. Key Features Lead Management — Create, track, search, filter, and manage large volumes of recruitment leads. Advanced Lead Filtering — Filter leads by source, team, assigned user, country, status, sales task, outreach activity, and other attributes. Company Management — Maintain company records, domains, assignments, statuses, and associated recruitment activity. Candidate & Person Management — Organize candidate/person information and connect it with companies, jobs, and leads. Sales Workflow Management — Track sales tasks, responses, resume submissions, interest, contracts, and other recruitment stages. Job Management — Associate leads and candidates with relevant job opportunities. Lead Assignment — Assign companies and leads to specific team members and manage ownership across the recruitment workflow. Search & Reporting — Provide administrative tools for quickly finding and managing large datasets. Scalable Data Management — Built the backend around high-volume recruitment data, with the interface showing 250K+ leads and 200K+ company records. Admin Operations — Developed interfaces and backend functionality for managing the recruitment operation from a centralized platform. My Role Backend Engineer / Django Developer I was responsible for the backend development, including: Designing and developing backend APIs and business logic Building lead and company management workflows Implementing search, filtering, assignment, and status-management functionality Designing data relationships between leads, companies, people, jobs, and sales activities Building administrative workflows for managing large datasets Supporting recruitment and sales operations through custom backend functionality Technology Python · Django · Django REST Framework · PostgreSQL · REST APIs · Database Design · Backend Development

Cover image for Fashion Studio is an AI-powered

Fashion Studio is an AI-powered fashion creation platform designed to help fashion brands and creators generate professional product visuals without traditional photoshoots. The platform combines virtual try-on, AI model generation, image generation, video generation, face swapping, and quality assurance into a single workflow. I worked on the backend and AI integration layer, building APIs and workflows that connect the platform with multiple AI services and handle the generation lifecycle from user input to final output. Key features include: 👗 Virtual Try-On — Apply garments and accessories to generated or uploaded models. 🧑‍🎨 AI Model Generation — Create realistic fashion models with configurable gender, appearance, measurements, poses, and backgrounds. 🖼️ AI Image Generation — Generate fashion campaigns and product visuals from prompts and references. 🎬 AI Video Generation — Turn generated fashion imagery into promotional videos. 🔄 Face Swap — Generate customized fashion content using face-swapping workflows. 🛡️ AI Quality Assurance — Automatically evaluate generated results and retry failed generations when necessary. 📚 Generation Library — Track and manage generated assets and their parameters. 💳 Credits & Billing — Manage generation credits, subscriptions, usage, and add-on credits. ⚙️ Generation Parameters — Store model configuration, garments, poses, backgrounds, aspect ratios, and generation metadata. Technology Python · Django · FastAPI · PostgreSQL · REST APIs · Replicate · OpenAI · Docker · AI Image Generation · AI Video Generation My Role Backend Engineer / AI Integration Engineer I focused on designing the backend APIs, integrating AI generation services, building generation workflows, handling asynchronous processing and failures, and connecting the AI capabilities with the product's user-facing workflows.

Django - SkillSwap

Amazon Redshift(1)