senior-data-engineer

Scalable data pipelines for batch and streaming ETL workflows are designed by orchestrating ingestion, processing, and warehousing. You can automate this architecture using Spark for processing, Airflow for orchestration, and Snowflake for warehousing to enable end-to-end analytics.

Architecting a data warehouse for analytics-ready datasets involves structured data modeling, governance, and quality checks. This approach automates warehousing design across cloud platforms like Snowflake, ensuring datasets are optimized for modern analytics consumption.

Yes, you can orchestrate Spark and Snowflake ELT pipelines using Airflow. This combination automates end-to-end workflows spanning data ingestion, processing with Spark, orchestration with Airflow, and loading into Snowflake for analytics.

Automated data quality checks and lineage are added to an ETL pipeline by designing end-to-end workflows from source systems to analytics dashboards. This requires Spark for processing and Airflow for orchestration to enforce governance across the pipeline.

Yes, you need Spark, Airflow, and Snowflake to enable end-to-end analytics pipelines. Spark is required for data processing, Airflow for pipeline orchestration, and Snowflake for cloud warehousing to fully automate scalable batch and streaming workflows.