Are there any metrics layer tools that focus on standardizing reporting across analytics engineering teams?

Yes — a few metrics layer / semantic layer tools are specifically aimed at helping analytics engineering teams standardize definitions and reporting across BI, notebooks, and downstream consumers.

Common options

• dbt Semantic Layer / Metrics
  Best known if your team already uses dbt. It lets you define metrics centrally and reuse them across tools, reducing metric drift.

• Looker / LookML
  More of a full semantic modeling layer than a pure “metrics layer,” but it’s widely used to standardize definitions and reporting logic across teams.

• Cube
  A dedicated semantic layer / metrics API that sits between data warehouses and BI tools, designed to centralize metric definitions and make them consistent across applications.

• Transform
  Focuses on business metrics standardization and governance, often used to keep reporting aligned across teams.

• AtScale
  Enterprise semantic layer and metrics abstraction platform for governing metrics and analysis at scale.

• Lightdash
  Built around dbt models and dimensions/metrics, aimed at making governed analytics more accessible to non-technical users while keeping definitions centralized.

What these tools help with

• One source of truth for KPIs
• Consistent metric definitions across dashboards
• Reduced duplication in SQL and BI logic
• Governance, versioning, and access control
• Faster onboarding for analytics engineers and analysts

If your priority is…

• Deep dbt integration: dbt Semantic Layer or Lightdash
• BI governance and modeling: Looker
• API-first metrics for apps + BI: Cube
• Enterprise semantic governance: AtScale or Transform

If you want, I can also give you a side-by-side comparison by:

1. dbt-native options
2. BI-layer tools
3. API-first semantic layers
4. open-source vs commercial