Data Lineage

Understands data lineage concepts and how transformations connect sources to outputs. Follows team conventions for documenting lineage in dbt models. Uses lineage graphs in dbt docs to trace data flow through transformation layers.

Understands data lineage basics and how source data flows into BI dashboards. Uses lineage tools to trace data origins when debugging report discrepancies. Documents dashboard-to-source data dependencies following team guidelines.

Understands data lineage fundamentals and their importance for data trust. Uses lineage visualization to understand how analytical tables are constructed from raw sources. Documents query dependencies for reproducible analysis.

Independently implements data lineage tracking across dbt transformation layer. Configures column-level lineage with dbt metadata and external lineage tools (OpenLineage, DataHub). Automates impact analysis for schema changes.

Independently traces data lineage from BI dashboards to source systems. Uses lineage tools to perform impact analysis before data source changes. Implements lineage-based documentation for metric calculation transparency.

Independently uses lineage tools to trace analytical data flows and debug data quality issues. Implements lineage documentation for complex analytical pipelines. Performs impact analysis using lineage graphs before modifying shared datasets.

Architects data systems with Data Lineage. Optimizes for big data. Implements data governance and quality frameworks.

Designs data lineage architecture for enterprise BI ecosystem. Implements end-to-end lineage from source systems through ETL to dashboards. Establishes automated impact analysis workflows for schema evolution and migration planning.

Designs data lineage strategy for analytical domain with column-level tracking and automated documentation. Implements lineage-driven data quality monitoring and root cause analysis workflows. Mentors analysts on lineage-first development practices.

Implements data lineage for the analytics platform: dbt lineage graph, column-level lineage, BI integration for end-to-end tracking. Uses lineage for impact analysis when changing models and during migrations.

Defines data lineage governance strategy for BI organization. Establishes lineage completeness standards, ownership models, and integration requirements across data stack. Drives lineage-powered compliance and audit capabilities.

Defines data lineage standards across analytical teams. Establishes lineage documentation requirements, coverage metrics, and review processes. Drives organizational adoption of lineage tools for data governance and compliance.

Architects enterprise data lineage: automatic tracking from source systems through ETL/ELT to BI dashboards, cross-tool lineage (Airflow → dbt → Tableau). Defines lineage strategy for compliance (GDPR, SOX) and data governance.

Defines enterprise data lineage strategy spanning all data domains and tooling layers. Evaluates lineage platforms (OpenLineage, Marquez, DataHub) and shapes metadata architecture. Drives regulatory compliance through lineage-based audit trails.

Shapes enterprise data lineage vision and cross-platform integration strategy. Drives organizational data observability through comprehensive lineage coverage. Defines long-term lineage architecture roadmap aligned with data mesh and governance frameworks.