monoes/monomind/data-engineer
Claude Code and agent skill by monoes/monomind
Trust tier 3
- community
Source check pending
Checking that the SKILL.md file is still in the repository.
Org role guidance for a data engineer: build idempotent, observable pipelines with schema contracts and bronze-silver-gold layers for analytics-ready data. For hands-on validation tooling use data-quality-frameworks.
Install
npx vskill@latest install monoes/monomind/data-engineerSKILL.md
Data Engineer — Best Practices
Focus
Builds reliable, observable data pipelines and platform infrastructure that turn raw, messy data into trusted, analytics-ready assets.
Best practices
- Make every pipeline idempotent — rerunning it must never duplicate or corrupt data.
- Enforce explicit schema contracts between producers and consumers; schema drift should alert loudly, never silently corrupt downstream data.
- Follow a layered model (raw/bronze → cleansed/silver → business-ready/gold): never let consumers read directly from raw layers.
- Handle nulls and malformed records deliberately (impute, flag, or reject) — never let them propagate implicitly into business-facing tables.
- Prefer incremental/CDC processing over full-table refreshes to control cost and latency.
- Attach audit columns (
created_at,updated_at,deleted_at,source_system) and prefer soft deletes for traceability. - Set and monitor freshness/completeness SLAs per pipeline, with alerting on breach — not just on hard failure.
- Document data lineage so any row's provenance can be traced back to its source system.
Common pitfalls
- Silent data quality failures that only surface once a downstream report or model looks wrong.
- Full-table scans/refreshes that work fine in dev and become a cost or latency disaster at scale.
- Transforming data in place at the raw layer, destroying the ability to reprocess from source.
- Treating schema changes as someone else's problem instead of validating and gating them explicitly.
- Under-documenting pipeline ownership, so failures have no clear owner or runbook.
Tools & techniques
- Data contract tooling (e.g., dbt contracts, Great Expectations) enforced in CI, not just checked manually.
- Window-function based deduplication keyed on primary key + event timestamp for the silver layer.
- Partitioning/clustering (date partitions, Z-ordering) tuned to actual downstream query patterns.
- Pipeline observability with freshness, row-count, and schema-drift alerts wired to an on-call channel.
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingpackages/@monomind/cli/org-skills/data-engineer/SKILL.md
Popularity
- Repository stars
- 21
- Repository forks
- 3
- 7-day trend score
- 20
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
Embed badge
[](https://verified-skill.com/skills/monoes/monomind/data-engineer)