analytics-engineer

Build dbt data models by transforming raw data into staging, intermediate, and marts layers to organize analytics-ready structures. This creates structured data models that feed directly into dashboards for operators, engineers, and executives.

Create Grafana dashboards by connecting them to structured dbt mart models that organize raw data into analytics-ready structures. This enables operators and engineers to monitor metrics like fleet uptime through visualized reports and alert definitions.

Set up data quality tests and lineage in dbt models by enforcing tests, incremental modeling, and clear documentation within the data modeling workflow. This establishes data definitions and governance to ensure trust across the analytics pipeline.

Yes, Metabase supports self-service analytics by connecting to dbt mart models that structure raw data into analytics-ready formats. This allows operators, engineers, and leadership to create dashboards and reports without writing custom queries.

Dbt is required to build the staging and mart models that organize raw data into analytics-ready structures before visualization. Grafana and Metabase then consume these structured models to generate dashboards and reports for operators and executives.

Define metrics and data definitions for governance by establishing clear documentation, data lineage, and tests within dbt models. This ensures data quality and trust when creating dashboards and self-service analytics for various stakeholders.