senior-data-engineer

To build a scalable ETL pipeline from PostgreSQL to Snowflake, use Airflow for orchestration and dbt for data modeling. This architecture supports batch processing, schema optimization, and automated data quality checks for reliable data warehousing workflows.

Designing data quality frameworks requires implementing validation rules, monitoring checks, and data contracts within your pipelines. This ensures reliable processing across both batch ETL/ELT and real-time streaming architectures while maintaining DataOps principles.

Yes, Spark and Kafka work together for real-time data pipeline orchestration. Spark handles stream processing and transformations while Kafka manages event streaming, enabling scalable infrastructure for continuous data ingestion and processing workflows.

Dimensional modeling suits analytical reporting with star schemas, while data vault handles complex historical tracking and enterprise integration. Choose dimensional models for query performance or data vault when managing massive scale and regulatory compliance requirements.

Yes, this approach supports both ETL and ELT workflows using modern data stack technologies including Python, SQL, Airflow, dbt, and Spark. It covers pipeline orchestration, data modeling, and DataOps principles for scalable infrastructure design.

Data pipeline architecture fails to scale when lacking proper workflow optimization and DataOps principles. Implementing scalable infrastructure with Spark for distributed processing and Airflow for orchestrated scheduling resolves peak load bottlenecks effectively.