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Sandeep D. Cloud, Data and ETL Platforms

My name is Sandeep D. and I have over 3 years of experience in the tech industry. I specialize in the following technologies: Python, Data Science, Amazon Web Services, AWS Glue, Apache Spark, etc.. I hold a degree in Bachelor of Engineering (BEng), Master of Science in Information Technology (MSc(IT)). Some of the notable projects I’ve worked on include: Aircraft Lead Generation - OpenAI, & Supabase, Claude AI + MCP Server/Client Python, LakeHouse Iceberg Architecture, Kubernetes Microservice Architecture, Website Data Scrapping. I am based in Perth, Australia. I've successfully completed 5 projects while developing at Softaims.

I employ a methodical and structured approach to solution development, prioritizing deep domain understanding before execution. I excel at systems analysis, creating precise technical specifications, and ensuring that the final solution perfectly maps to the complex business logic it is meant to serve.

My tenure at Softaims has reinforced the importance of careful planning and risk mitigation. I am skilled at breaking down massive, ambiguous problems into manageable, iterative development tasks, ensuring consistent progress and predictable delivery schedules.

I strive for clarity and simplicity in both my technical outputs and my communication. I believe that the most powerful solutions are often the simplest ones, and I am committed to finding those elegant answers for our clients.

Main technologies

  • Cloud, Data and ETL Platforms

    3 years

  • Python

    2 Years

  • Data Science

    2 Years

  • Amazon Web Services

    1 Year

Additional skills

  • Python
  • Data Science
  • Amazon Web Services
  • AWS Glue
  • Apache Spark
  • Apache Airflow
  • ETL Pipeline
  • Docker
  • Terraform
  • pandas
  • AWS Lambda
  • Amazon S3
  • CI/CD
  • Machine Learning
  • Database

Direct hire

Potentially possible

Previous Company

Afterpay

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Experience Highlights

Aircraft Lead Generation - OpenAI, & Supabase

Built a backend system to infer likely aircraft ownership from tail numbers using FAA/Radarbox data, business registry APIs, and flight behavior logs. The system combines this data and queries the OpenAI API (GPT-4) with a structured prompt to return ownership insights, confidence scores, and traceable sources. Results are stored in PostgreSQL with versioning and flagged if incomplete. The pipeline is modular, accurate, and runs periodic re-checks for updated insights. All APIs are integrated for smooth data flow across each step.

Claude AI + MCP Server/Client Python

Built a lightweight MCP-style Python framework that connects to multiple Supabase tables and runs an LLM agent to analyze data and answer natural language questions. The agent runs automatically every 10 minutes to perform scheduled analysis. Designed as a plug-and-play starter kit—just add your DB credentials and table names. Uses Supabase SDK, OpenAI (or any LLM), and FastAPI with background scheduling. Ideal for building AI-driven insights on top of live cloud data.

LakeHouse Iceberg Architecture

Built a data pipeline to ingest data from S3 into Apache Iceberg tables using Apache Airflow and Spark. The pipeline processes 500+ GB of data daily, with 1M+ records, and reduced ingestion latency by 60%. Query performance improved by 4x and storage efficiency by 35% through compaction and Iceberg’s features. Achieved 99.9% pipeline success rate since deployment.

Kubernetes Microservice Architecture

A Kubernetes-based microservice app for video-to-audio conversion. Auth is handled by a Python service using MySQL. An API Gateway (Python) authenticates users, receives video uploads, and publishes tasks to RabbitMQ. A separate Python converter service consumes tasks, extracts audio, stores files, and updates MongoDB with metadata. MongoDB manages video/audio records, while RabbitMQ enables async processing. All services are containerized and scalable via Kubernetes for efficient, decoupled media processing.

Website Data Scrapping

A scalable web scraping system using Playwright and Scrapy. Scrapy handles fast, structured crawling, while Playwright manages JavaScript-heavy pages and dynamic content. The system combines both tools for maximum coverage and efficiency. Playwright is used for initial rendering; Scrapy extracts data and follows links. Ideal for complex websites requiring JS execution. Supports proxy rotation, rate limiting, and export to JSON/CSV or database. Designed for modularity, scalability, and high accuracy scraping.

Education

  • Tribhuwan University

    Bachelor of Engineering (BEng) in

    2013-01-01-2017-01-01

  • Curtin University

    Master of Science in Information Technology (MSc(IT)) in

    2020-01-01-2022-01-01

Languages

  • English
  • Hindi
  • Nepali

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