Data Analytics Engineering

Scope

• Define metrics, grains, and dimensional models.

• Build transformation layers and semantic models.

• Implement data quality tests and observability.

• Document datasets, lineage, and ownership.

• Align analytics outputs with BI and product needs.

Ask For Inputs

• Business metrics and decision use cases.

• Source systems, data freshness, and latency needs.

• Existing warehouse, tooling, and orchestration.

• Expected data volumes and change cadence.

• Governance requirements and access controls.

Workflow

• Define metric dictionary and grains.

• Design staging, intermediate, and mart layers.

• Model dimensions and facts with clear keys.

• Build semantic layer and metric definitions.

• Add tests for freshness, nulls, ranges, and duplicates.

• Document lineage, owners, and SLAs.

• Plan rollout, backfills, and validation checks.

Outputs

• Metric dictionary and semantic model.

• Data model with schema and grain definitions.

• Transformation plan and dbt or SQLMesh structure.

• Data quality test suite and alerting plan.

• Documentation and ownership map.

Quality Checks

• Keep metric definitions stable and versioned.

• Treat metrics as APIs: document changes, deprecate safely, and backfill deliberately.

• Define data contracts for core tables (schema, freshness, keys) to control downstream breakage.

• Avoid mixed grains in a single model.

• Ensure tests cover critical joins and aggregates.

• Validate against source of truth and historical baselines.

Templates

• assets/metric-dictionary.md for metric definitions and owners.

• assets/semantic-layer-spec.md for entities, measures, and dimensions.

• assets/data-quality-test-plan.md for test coverage planning.

Resources

• references/modeling-patterns.md for modeling guidance and data quality patterns.

• references/tool-comparison-2026.md for dbt vs SQLMesh vs Coalesce decision matrix.

• references/semantic-layer-patterns.md for semantic layer implementation (Cube, dbt Semantic Layer, AtScale, warehouse-native).

• references/data-quality-testing.md for data quality test strategies, dbt tests, Great Expectations, and alert design.

• references/metric-governance.md for metric lifecycle management, ownership models, deprecation policies, and metric debt prevention.

• data/sources.json for curated vendor docs and trend-tracking sources (use as a WebSearch seed list).

Related Skills

• Use data-lake-platform for platform architecture.

• Use data-sql-optimization for query tuning.

• Use ai-ml-data-science for modeling and experiments.

Trend Awareness Protocol

IMPORTANT: When users ask recommendation questions about analytics engineering, data modeling, or BI, you MUST use WebSearch to check current trends before answering. If WebSearch is unavailable, use data/sources.json

• web browsing and state what you verified vs assumed.

Trigger Conditions

• "What's the best tool for [analytics engineering/data modeling/BI]?"

• "What should I use for [transformation/semantic layer/metrics]?"

• "What's the latest in analytics engineering?"

• "Current best practices for [dbt/metrics layers/data quality]?"

• "Is [tool/approach] still relevant in 2026?"

• "[dbt] vs [SQLMesh] vs [other]?"

• "Best BI tool for [use case]?"

• "SQLMesh acquisition" or "Fivetran transformation"

• "Agentic analytics" or "AI data workflows"

• "Metric debt" or "metric governance"

Required Searches

• Search: "analytics engineering best practices 2026"

• Search: "[dbt/SQLMesh/semantic layer] vs alternatives 2026"

• Search: "analytics engineering trends January 2026"

• Search: "[specific tool] new releases 2026"

• Search: "agentic analytics AI data 2026" (for AI-related queries)

What to Report

After searching, provide:

• Current landscape: What analytics tools/patterns are popular NOW

• Emerging trends: New tools, patterns, or standards gaining traction

• Deprecated/declining: Tools/approaches losing relevance or support

• Recommendation: Based on fresh data, not just static knowledge

Example Topics (verify with fresh search)

• Transformation tools (dbt, SQLMesh, Coalesce)

• Semantic layers (dbt Semantic Layer, Cube, AtScale, warehouse-native)

• Metrics stores and headless BI

• Data quality tools (dbt tests, Elementary, dbt-expectations/Metaplane)

• BI platforms (Metabase, Superset, Lightdash, Hex)

• Data modeling patterns (dimensional, wide tables, activity schema)

• Analytics engineering workflows and CI/CD

• Agentic AI workflows for analytics

• Data mesh and domain-owned data products

Fact-Checking

• Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.

• Prefer primary sources; report source links and dates for volatile information.

• If web access is unavailable, state the limitation and mark guidance as unverified.