Data Engineer

To build idempotent ETL pipelines with explicit schema contracts, design scalable data platforms that automate end-to-end deployment and enforce data quality instrumentation, preventing regressions during raw source ingestion.

A medallion lakehouse architecture structures data into Bronze, Silver, and Gold layers for progressive cleansing and aggregation. Use it to turn raw, messy data into reliable, analytics-ready assets with robust governance and lineage.

Set up a minimal Bronze-Silver-Gold pipeline using Spark and Delta Lake by ingesting raw data into Bronze, cleansing it in Silver, and aggregating it in Gold to produce analytics-ready assets.

Yes, this data pipeline approach supports real-time streaming and event-driven architectures. It enables low-latency processing and ingestion to turn continuous raw sources into analytics-ready assets.

Implement data observability and lineage across cloud-native pipelines by applying comprehensive monitoring, metadata management, and data quality checks to prevent regressions and ensure platform reliability.

The best way to ensure data quality and prevent regressions in scalable data platforms is by implementing explicit schema contracts, data quality instrumentation, and comprehensive monitoring throughout your pipeline deployment.