engineering-data-engineer

To build data pipelines with Medallion Architecture, you design Bronze, Silver, and Gold layers using Apache Spark and Delta Lake to progressively refine raw data into analytics-ready formats. This approach ensures data quality and reliable delivery for downstream consumption.

Data contract enforcement defines strict schema and quality expectations between data producers and consumers. It prevents pipeline failures by validating data against agreed-upon rules, ensuring reliable downstream analytics and tracking data lineage across the platform.

Designing ETL pipelines with idempotency ensures re-running the same operation yields identical results without duplication. Self-healing pipelines automatically detect and recover from transient failures, maintaining reliable data infrastructure and continuous data flow.

Yes, dbt integrates seamlessly with cloud data platforms like AWS, Azure, and GCP to build scalable ELT pipelines. It transforms raw data already loaded into the data lakehouse, enforcing data quality and defining analytical models within the Medallion Architecture.

Build event-driven streaming pipelines with Kafka by leveraging stream processing frameworks to ingest and transform real-time data. This architecture processes continuous data flows, enabling low-latency analytics and immediate operational insights.

Architecting a cloud-native data lakehouse involves combining open table formats with scalable cloud infrastructure on platforms like Azure, AWS, or GCP. This design unifies data lake storage flexibility with data warehouse performance, supporting reliable analytics and AI workloads.