engineering-data-engineer

Design a scalable ETL data pipeline by ingesting raw data into the Bronze layer, refining it in Silver, and delivering analytics-ready tables in Gold. This approach ensures reliable schema management, data quality checks, and structured lakehouse architecture governance.

A lakehouse architecture combines scalable storage with analytical modeling, requiring data contracts when you need strict schema governance. You need this setup to enforce reliable ingestion, maintain data quality, and provide consistent lineage tracking across modern cloud data platforms.

Yes, you can build streaming and incremental ingestion workflows for a cloud data platform. The architecture supports scalable ETL/ELT workflows that process data incrementally, satisfying requirements for reliable ingestion and cost-conscious processing in modern data platforms.

Add data quality checks and observability to dbt and Spark pipelines by implementing automated validation rules, lineage tracking, and robust monitoring. This ensures your data pipeline maintains consistent schemas and delivers reliable, analytics-ready data outputs.

The best way to govern schemas and manage data lineage in an ELT workflow is through a lakehouse blueprint that enforces data contracts. This method provides structured governance, tracks data transformations across layers, and maintains reliable analytical models.