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-engineer

SKILL.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

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
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/monoes/monomind/data-engineer/badge)](https://verified-skill.com/skills/monoes/monomind/data-engineer)

Something wrong with this skill or its page? Tell us.