Adebanke Sobanke

I’m an aspiring data engineer focused on building reliable data solutions through batch and incremental ETL pipelines. I work end-to-end—turning raw data into analytics-ready datasets with strong attention to data modeling, validation, and query performance.

In my projects, I’ve designed production-style incremental processing using AWS Glue, PySpark, and Amazon S3, including watermark-based ingestion and partitioned outputs for efficient analytical querying. I also configured workflow orchestration with scheduled and conditional triggers and added CloudWatch logging for observability and monitoring.

I’ve built cloud data warehousing workflows in Google BigQuery, where I loaded and transformed CSV weather datasets using Python and Pandas, refined table schemas for better query efficiency, and performed SQL-based validation and exploratory analysis. Along the way, I troubleshot authentication issues to ensure smooth deployment and reliable warehouse integrity checks.

For streaming and application-facing work, I developed a real-time Kafka ETL pipeline with a producer-consumer architecture and PostgreSQL integration for persistent storage, including schema alignment and debugging of distributed system issues. I also built a RESTful Flask + PostgreSQL patient management API using SQLAlchemy, emphasizing scalable endpoints and practical backend validation—always with a mindset of continuous improvement in modern data engineering.