I am a Data Engineer passionate about building scalable, automated, and reliable data pipelines. With a strong foundation in Data Science and Machine Learning, I specialize in engineering the backend infrastructure and clean data models that downstream analytics and AI applications rely on.

• ☁️ Cloud & Infrastructure: AWS (S3, EC2, RDS), Docker, Linux
• ⚙️ Data Orchestration & Modeling: Apache Airflow, dbt (data build tool)
• 💾 Databases & Querying: PostgreSQL, MySQL, MongoDB, Advanced SQL
• 🐍 Programming & APIs: Python, Pandas, FastAPI, Boto3

An end-to-end, automated Medallion Architecture data pipeline deployed entirely on AWS infrastructure.

• Tech Stack: Python, AWS (S3, EC2, RDS PostgreSQL), Apache Airflow, dbt
• Impact: Engineered a pipeline to ingest and stream 19+ million rows (~1GB) of raw retail data from an S3 Data Lake into an RDS Data Warehouse, orchestrating the daily transformations into business-ready gold marts using Airflow and dbt.

A fully containerized, reproducible ETL pipeline showcasing local infrastructure management.

• Tech Stack: Docker Compose, PostgreSQL, Apache Airflow, dbt
• Impact: Built an isolated local development environment that handles massive bulk-loading, data quality testing, and dependency management without cloud overhead.

Supply chain data optimization and warehousing project.

• Tech Stack: Advanced SQL, Relational Database Design
• Impact: Designed optimized queries and database structures to track, analyze, and improve supply chain inventory metrics.

A backend assessment system evaluating LLM capabilities across multiple domains.

• Tech Stack: MongoDB, FastAPI, Python
• Impact: Built a robust NoSQL backend and REST API server-client architecture to efficiently store, retrieve, and evaluate complex query data.

As a Data Engineer, my background in building predictive models ensures I architect data pipelines perfectly suited for Machine Learning workloads.

• 🤖 Soccer Image Classifier: Built a Computer Vision app using SVM, CNN, and a Streamlit frontend to classify elite soccer players.
• 📈 Stock Price Forecasting: Engineered a time-series forecasting dashboard using Facebook Prophet and Streamlit.
• 🏦 Loan Default Prediction: Handled heavy data cleanup and hyperparameter tuning to build robust classification models predicting loan defaults.
• 👥 Customer Churn & Segmentation: Applied K-Means clustering and predictive analytics to drive customer retention strategies.

📫 Let's Connect: https://www.linkedin.com/in/najeemdeenlamidi/ | adnaj4real@gmail.com