Project 1
Business Development (BD) Automation Pipeline
An end-to-end automated job acquisition and application pipeline engineered to streamline outbound business development workflows and accelerate lead generation.
- • Built resilient scraping architectures using Selenium and Playwright to continuously harvest opportunities across 30+ enterprise job platforms.
- • Automated data ingestion into AWS RDS (MySQL) with clean schema design and centralized lead tracking.
- • Integrated ChatGPT API with a Retrieval-Augmented Generation (RAG) pipeline to generate tailored resumes and pitch cover letters for each job posting.
- • Automated application submission with Playwright, enabling complete execution from discovery to application.
Impact: Reduced manual BD effort by 80%–90%, significantly increasing job outreach velocity and lead turnaround time.
Tech Stack: Python, Playwright, Selenium, RAG, OpenAI API, AWS RDS (MySQL)
Project 2
Mavericks United Enterprise CRM Platform
A full-stack, analytics-driven Customer Relationship Management system designed to centralize organizational workflows, track team productivity, and manage sales leads.
- • Engineered high-performance RESTful APIs using FastAPI and MySQL with secure authentication and granular Role-Based Access Control (RBAC).
- • Built interactive analytics dashboards in React for real-time team performance metrics and client lead pipeline monitoring.
- • Created unified lead management modules for submission, lifecycle tracking, and automated performance evaluation across departments.
Impact: Boosted overall operational efficiency and team productivity across Mavericks United by 40%–45%.
Tech Stack: FastAPI, React, MySQL, Python, RESTful APIs, RBAC
Project 3
Enterprise Maritime Sanctions ETL & Data Lakehouse Architecture
A production-grade, cloud-native data engine designed to ingest, process, and standardize global maritime vessel sanctions data for downstream analytics.
- • Built automated ingestion pipelines extracting data from 9+ disparate maritime sanctions sources.
- • Architected Bronze/Silver/Gold medallion flow in Amazon S3, loading analytics-ready datasets into Snowflake.
- • Deployed and orchestrated all scraping and processing workflows on Amazon Managed Workflows for Apache Airflow (MWAA), optimizing DAG scheduling and infrastructure costs.
- • Automated infrastructure with Terraform, containerized workloads with Helm charts, and implemented CI/CD through GitHub Actions with strict quality checks.
Tech Stack: Apache Airflow (MWAA), AWS (S3), Snowflake, Medallion Architecture, Terraform, Docker, Helm, GitHub Actions, Python