senior-data-engineer

To design a scalable ETL pipeline for Snowflake, you architect batch or streaming data ingestion, transform the data using dbt, and load it while enforcing data quality checks. This ensures reliable, performant data infrastructure.

DataOps is a set of principles for building reliable data systems through pipeline orchestration, monitoring, and data quality assurance. It applies to data engineering by using tools like Airflow to automate and validate workflows.

Yes, you can use Airflow and dbt together for data pipeline orchestration. Airflow manages the pipeline scheduling and workflow, while dbt handles the SQL data modeling and transformations within your data warehouse.

The best way to implement data quality checks in a data pipeline is to establish a framework for continuous data validation, monitoring, and governance. This integrates DataOps principles directly into your ETL systems.

To model data efficiently for analytics and warehousing, you design optimized data models that support fast querying and reporting. This involves using modern data stack technologies like dbt to structure transformed data.

Yes, this approach supports both batch and streaming data pipelines. You can architect and implement scalable data infrastructure using Python, Spark, and Kafka to handle various data ingestion requirements.