analytics-data-engineer
Skill8 starsChanged 6 months agoMITView on GitHub
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 or launch roadmaps (analytics-data-engineering-manager-product), enterprise data platform ADRs (data-architect), deep warehouse platform tuning only (data-warehouse-engineer), dashboard design (bi-analyst), or ML modeling (data-scientist).
1 other project uses a version of this.
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- rade-nugroho/Agentic-Enteprises-Skillword for word, first seen May 20, 2026
Other versions1 project uses a version of this.
Word for word · 1
- rade-nugroho/Agentic-Enteprises-Skill★ 9100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 21
Add tests (unique, not null, relationships, custom SQL) and freshness checks
- Distinctive: line 22
Document models and expose lineage for BI and stakeholders
- Distinctive: line 88
Every mart documents grain and primary key in YAML
- Distinctive: line 91
No breaking grain change without migration note to BI
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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/dbt_project_structure.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/docs_lineage_exposures.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 layout →
references/dbt_project_structure.md - Modeling →
references/analytics_modeling.md - Incremental →
references/incremental_cdc.md - Tests →
references/testing_quality.md - Docs/CI →
references/docs_lineage_exposures.md - Metrics →
references/metrics_alignment.md
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