About

Data Engineer at Mavericks United with a BS in Computer Science from PUCIT. My work centers on designing and maintaining scalable data platforms, building reliable data pipelines, and enabling analytics-ready datasets through modern data engineering practices.

I specialize in Python-based data engineering, developing ETL/ELT pipelines, orchestrating workflows with Apache Airflow, and implementing cloud-native data solutions on AWS. My experience includes AWS S3, AWS Glue, AWS Athena, Docker, and Pandas, with a strong focus on data ingestion, transformation, storage, and governance. I actively apply architectural patterns such as the Medallion Architecture to structure data across bronze, silver, and gold layers, ensuring scalability, maintainability, and data quality.

My responsibilities include ingesting data from diverse internal and external sources, developing automated data acquisition workflows, implementing transformation pipelines, enforcing validation rules, and optimizing data movement across data lakes, warehouses, and lakehouse environments. I work extensively on workflow orchestration, metadata-driven processing, and building repeatable data pipelines that support downstream analytics and reporting requirements.

Before transitioning into data engineering, I worked extensively in software development and automation, designing RESTful APIs, developing database-centric applications, and building large-scale data extraction systems using Python, Selenium, and BeautifulSoup. This foundation provides a strong understanding of distributed systems, backend engineering, automation frameworks, and data lifecycle management, allowing me to deliver end-to-end data solutions from source acquisition to analytical consumption.

My technical interests extend beyond data engineering into Machine Learning, Artificial Intelligence, and Data Science, with a focus on strengthening the intersection between data platforms, predictive analytics, and intelligent systems.