senior-data-engineer

Data engineering workflows for designing reliable pipelines and datasets: ingestion, transforms, orchestration, schema evolution, data contracts, quality…

Skill by vadimcomanescu

IndexedMIT★ 25

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/vadimcomanescu-senior-data-engineer

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About

Data engineering workflows for designing reliable pipelines and datasets: ingestion, transforms, orchestration, schema evolution, data contracts, quality checks, and observability. Use when building ETL/ELT, reviewing pipelines, defining warehouse/lake schemas, or diagnosing data quality incidents.

Use cases: data

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools: No tools found
Secrets: None asked for
Install scripts: None found
Runs as: Python package

Network hosts it talks to 1

codeload.github.com · found in install command

Files it touches

~/.claude/skills/senior-data-engineer

What's inside

Skill name: senior-data-engineer
Description: Data engineering workflows for designing reliable pipelines and datasets: ingestion, transforms, orchestration, schema evolution, data contracts, quality checks, and observability. Use when building ETL/ELT, reviewing pipelines, defining warehouse/lake schemas, or diagnosing data quality incidents.
License: MIT
Instructions: 22 lines in SKILL.md · view on GitHub

Before installing

Ships executable scripts

Files (5)

LICENSE.txt
SKILL.md
data-contract.md · references/
pipeline-checklist.md · references/
data_quality_scan.py · scripts/

SKILL.md (first lines)

# Senior Data Engineer

Make data pipelines boring: predictable, observable, and recoverable.

## Quick Start
1) Define the data contract (schema + semantics + freshness + ownership).
2) Design the pipeline:
   - Inputs, transformations, outputs, backfills, and failure handling
3) Data quality: checks for nulls, ranges, uniqueness, and referential integrity.
4) Operational story: retries, checkpoints, alerting, and lineage.

## Optional tool: lightweight profiling for CSV/JSONL
```bash
python ~/.codex/skills/senior-data-engineer/scripts/data_quality_scan.py path/to/data.csv --out /tmp/data_profile.json
```

## References
- Data contract template: `references/data-contract.md`
- Pipeline checklist: `references/pipeline-checklist.md`

From vadimcomanescu/codex-skills, MIT.

Signals

Stars: 25
Forks: 8
Open issues: 2
Last commit (this folder): Jan 19, 2026 (9 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License: MIT
Repository: github.com/vadimcomanescu/codex-skills
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

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senior-data-engineer skill. Agent Mart. https://agentmart.dev/skills/vadimcomanescu-senior-data-engineer (last updated 2026-10-24)