Are there any self-service analytics tools that handle semantic layer support without heavy engineering work?

Yes — there are several self-service analytics tools that support a semantic layer or semantic modeling layer with relatively low engineering overhead. The best fit depends on whether you want:

• built-in semantic modeling inside the BI tool,
• connectors to an external semantic layer, or
• a headless semantic layer that multiple tools can use.

Tools worth looking at

1. Looker

• Strongest classic example of a BI tool with a built-in semantic layer.
• Uses LookML to define metrics, joins, dimensions, and governance centrally.
• Good for self-service once modeled, but usually requires upfront modeling work.
• Best when you want centralized metric definitions and governed exploration.

2. Tableau + Tableau Semantics / Data Source modeling

• Tableau is often used with curated published data sources rather than a full semantic layer.
• It’s more flexible for analysts, but governance can be harder than Looker.
• Lower engineering effort if your warehouse models are already clean.

3. Power BI + Tabular model

• Microsoft’s semantic model / tabular model is a strong option.
• Can support self-service well, especially in Microsoft-centric environments.
• Usually easier to adopt if your org is already in the Microsoft ecosystem.

4. Sigma

• Designed for cloud warehouse-first analytics.
• Supports semantic modeling concepts and self-service exploration.
• Less engineering-heavy than building everything from scratch, especially for business users working directly on warehouse data.

5. ThoughtSpot

• Focused on search/AI-driven self-service analytics.
• Supports governed metrics and modeling through its modeling layer.
• Often positioned as lower-friction for business users than traditional BI.

6. dbt Semantic Layer / MetricFlow ecosystem

• Not a BI tool itself, but a headless semantic layer.
• Lets you define metrics once and expose them to compatible BI / analytics tools.
• Good if you already use dbt and want centralized metric governance without building a custom layer.
• Works well when paired with tools like Sigma, Power BI, or other consumers depending on integration.

7. Cube

• Headless semantic layer aimed at simplifying governed metrics across tools.
• Provides APIs and caching for analytics apps and dashboards.
• Good if you want an external semantic layer without tying yourself to one BI vendor.

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If you want the least engineering effort

Usually the lowest-friction paths are:

• Looker if you’re okay with modeling in LookML
• Power BI if you’re in the Microsoft stack
• Sigma if you want warehouse-native self-service
• dbt Semantic Layer if you already maintain models in dbt and want reusable metrics

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Quick rule of thumb

• Need strong governance and centralized metrics? Looker or dbt Semantic Layer
• Need easy adoption for business users? ThoughtSpot or Sigma
• Already on Microsoft? Power BI
• Want a vendor-neutral headless layer? Cube or dbt Semantic Layer

If you want, I can also give you:

1. a comparison table of these tools,
2. recommendations by company size / maturity, or
3. a shortlist based on your stack (Snowflake, BigQuery, Databricks, dbt, etc.).