data-engineer

Scalable data pipelines are designed by automating the deployment of robust ETL/ELT and streaming architectures. This approach uses a modern data stack including dbt, Airflow, and Spark to handle ingestion, validation, and monitoring for production analytics.

Orchestrating ETL workflows with governance requires automating pipeline design and applying quality checks across the data platform. Using tools like Airflow and dbt ensures reliable analytics, schema evolution, and cost-aware monitoring throughout the data lifecycle.

A modern data stack including dbt, Airflow, and Spark is required to build reliable data pipelines. This Skill assumes your organization has tooling for ingestion, validation, and monitoring to deploy robust batch and streaming architectures effectively.

Airflow orchestrates both batch and streaming data architectures within a unified data platform. It coordinates the automated deployment of scalable pipelines, ensuring data quality and governance checks are applied consistently across all processing workflows.

To start building a data pipeline, define your data sources, schemas, and SLAs first. You can then deploy a minimal end-to-end pipeline to validate the workflow before scaling up to full production environments with governance and monitoring.

Data pipeline schema evolution and validation monitoring are required to ensure reliable analytics and maintain data quality in production. Applying governance and automated checks prevents downstream errors and supports scalable, cost-aware orchestration across the platform.