Apache Airflow

Understands basic Airflow concepts: DAGs, operators, and task dependencies. Follows established DAG templates to build simple transformation pipelines. Uses dbt + Airflow integration patterns defined by the team.

Understands basic Airflow DAG structure and scheduling concepts. Monitors scheduled report refresh pipelines and identifies failures. Follows team guidelines for triggering dashboard data updates through Airflow UI.

Understands basic Airflow concepts and DAG scheduling. Monitors data pipeline runs that feed analytical datasets. Follows team documentation to trigger ad-hoc DAG runs for data refresh and extraction tasks.

Independently builds Airflow DAGs for ELT pipelines with dbt operators and data quality checks. Configures retry policies, SLAs, and alerting for transformation jobs. Optimizes task parallelism and resource pools.

Independently configures Airflow DAGs for scheduled report generation and dashboard data refresh. Implements data quality sensors to validate source data before BI layer updates. Troubleshoots pipeline failures affecting reporting.

Independently builds Airflow DAGs for automated data extraction and cohort preparation pipelines. Implements data validation tasks with Great Expectations integration. Configures scheduling for recurring analytical data refreshes.

Architects data systems with Apache Airflow. Optimizes for big data. Implements data governance and quality frameworks.

Designs Airflow-based data pipeline architecture for enterprise BI platform. Implements complex dependency graphs across multiple data sources with SLA monitoring. Mentors team on DAG design patterns for reporting workflows.

Designs Airflow pipeline architecture for complex analytical workflows with cross-dataset dependencies. Implements data lineage tracking and audit logging. Optimizes DAG performance for large-scale analytical data processing.

Defines orchestration strategy for the analytics pipeline: Airflow for coordinating dbt runs, sensors for upstream data dependencies. Implements DAG design standards: idempotency, retry policies, SLA monitoring for analytical models.

Defines BI data pipeline strategy and Airflow platform standards. Establishes DAG development guidelines, code review practices, and deployment workflows for reporting team. Coordinates data freshness SLAs with stakeholders.

Defines analytical data pipeline strategy and Airflow governance standards. Establishes DAG naming conventions, testing requirements, and monitoring practices. Drives adoption of self-service pipeline creation among analyst teams.

Architects enterprise analytics platform orchestration: Airflow/Dagster for multi-project dbt, event-driven triggers, cross-team dependency management. Defines migration strategy to managed orchestration (dbt Cloud, Dagster Cloud).

Defines enterprise data orchestration strategy spanning Airflow, dbt, and BI tools. Evaluates orchestration platforms and migration paths. Shapes organizational data delivery standards and cross-team pipeline governance.

Defines enterprise analytical data orchestration strategy. Shapes organizational standards for pipeline reliability and data delivery guarantees. Evaluates next-gen orchestration tools and drives platform evolution decisions.