daemon-blockint-tech/agentic-enteprises-skill

analytics-data-engineer

Guides analytics engineering—dbt (or equivalent) project structure, staging/intermediate/mart models, dimensional and wide-mart patterns, incremental and CDC loads, data tests and contracts, documentation and lineage, CI for analytics code, and handoff to BI semantic layers. Use when building or refactoring analytics models, writing dbt SQL, defining tests and exposures, debugging mart freshness, or aligning warehouse tables to business metrics—not for leading product-embedded analytics teams o…

First seen May 20, 2026

Installation

$ npx skills add daemon-blockint-tech/agentic-enteprises-skill --skill analytics-data-engineer

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 8
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,860 B
  • docs SUMMARY.md 772 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 31 installs

SKILL.md

Analytics Data Engineer

When to Use

  • Structure a dbt project (layers, naming, materializations)
  • Build staging → intermediate → mart pipelines in the warehouse
  • Implement incremental, snapshot, or CDC-driven models
  • Add tests (unique, not null, relationships, custom SQL) and freshness checks
  • Document models and expose lineage for BI and stakeholders
  • Define marts that map to metrics and dashboards
  • Set up CI for analytics SQL (compile, test, slim CI)
  • Debug metric mismatches between mart and dashboard

When NOT to Use

  • Enterprise mesh, governance program, platform selection → data-architect
  • Partition/cluster tuning without dbt context → data-warehouse-engineer
  • Chart choice, executive dashboards, stakeholder storytelling → bi-analyst
  • Feature engineering, training, experiments → data-scientist
  • Data org roadmap and steward operations → data-manager
  • Analytics eng hiring, squad roadmap, launch governance → analytics-data-engineering-manager-product
  • Generic app CI/CD without analytics patterns → devops

Related skills

Need Skill
Warehouse SQL tuning, star schema theory data-warehouse-engineer
KPI definitions and dashboards bi-analyst
Platform and domain architecture data-architect
Pipeline on-call and platform SLOs data-system-ops-lead
ML and advanced stats data-scientist
Requirements and metric business rules business-analyst

Core Workflows

1. Project layout and conventions

Layering, naming (stg, int, fct, dim), materialization defaults, env targets.

See references/dbtprojectstructure.md.

2. Modeling for analytics

Facts, dimensions, wide marts, grain, degenerate dimensions, bridge tables.

See references/analytics_modeling.md.

3. Incremental and CDC

Merge strategies, full-refresh exceptions, late-arriving facts.

See references/incremental_cdc.md.

4. Quality and contracts

Tests, severity, source freshness, optional contracts with downstream.

See references/testing_quality.md.

5. Docs, lineage, exposures

Model descriptions, column docs, exposures to BI tools.

See references/docslineageexposures.md.

6. Metrics alignment

Grain, definitions, ownership with bi-analyst and business-analyst.

See references/metrics_alignment.md.

Output standards

  • Every mart documents grain and primary key in YAML
  • Tests on keys and critical business rules before merge
  • PR includes: models changed, test plan, backfill impact, downstream exposures
  • No breaking grain change without migration note to BI

When to load references

  • dbt layoutreferences/dbtprojectstructure.md
  • Modelingreferences/analytics_modeling.md
  • Incrementalreferences/incremental_cdc.md
  • Testsreferences/testing_quality.md
  • Docs/CIreferences/docslineageexposures.md
  • Metricsreferences/metrics_alignment.md