senior-data-engineer

To design a scalable ETL pipeline from PostgreSQL to Snowflake, you orchestrate data ingestion and transformation using Python, Airflow, and dbt. This ensures automated batch processing, data quality validation, and reliable warehouse loading.

The best way to orchestrate batch and streaming data pipelines is using Airflow for scheduling, Spark for distributed processing, and Kafka for streaming ingestion. This combination ensures robust data infrastructure and scalable DataOps workflows.

Data modeling in a modern data warehouse involves structuring data using dimensional models or Data Vault architectures. This approach optimizes analytical querying and supports scalable data infrastructure within lakehouse environments.

Yes, you can use Kafka for real-time data ingestion and dbt for SQL-based transformations within your data pipeline. This combination supports both streaming and batch processing workflows while maintaining data quality and DataOps principles.

Yes, you need Python and SQL expertise to implement DataOps best practices, as they are fundamental for writing data transformations, orchestrating pipelines with Airflow, and ensuring data quality validation across your infrastructure.

You should choose Data Vault modeling over dimensional modeling when your data warehouse requires high scalability for integrating multiple sources and historical tracking, whereas dimensional modeling is better suited for optimized business intelligence querying.