Aspiring Data Engineer with hands-on experience in Python, SQL, AWS S3, Snowflake, and dbt. Built end-to-end data engineering projects, ETL/ELT pipelines, and SQL-based data transformations. Comfortable writing SQL queries, cleaning datasets, and explaining design trade-offs clearly.

Built a production-inspired ELT pipeline that ingests retail data, stores it in a cloud data warehouse, and transforms it into business-ready analytics models.

PostgreSQL
      ↓
Python ETL Pipeline
      ↓
Amazon S3 (Parquet)
      ↓
Snowflake (Bronze)
      ↓
dbt (Staging → Intermediate → Fact & Dimensions → Business Marts)
      ↓
Power BI Dashboard

• Extracted retail data from PostgreSQL using Python.
• Converted data to Parquet and stored it in Amazon S3.
• Loaded raw data into Snowflake using COPY INTO.
• Built a layered dbt project with:
  • Staging Models
  • Intermediate Models
  • Fact & Dimension Tables
  • Business Data Marts
• Implemented data quality validations and reusable dbt macros.
• Designed a Star Schema for analytics.
• Prepared business-ready datasets for Power BI reporting.

• Python
• SQL
• PostgreSQL
• Amazon S3
• Snowflake
• dbt
• Power BI
• Git & GitHub

• Apache Airflow
• Advanced dbt
• Data Warehouse Design
• Production Data Engineering

I'm actively looking for a Data Engineering Internship where I can contribute, learn from experienced engineers, and continue building real-world data platforms.

Feel free to connect if you'd like to discuss Data Engineering, dbt, Snowflake, or modern ELT architectures.