senior-data-engineer

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.

Quality

80%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

—

The risk profile of this skill

Fix and improve this skill with Tessl

tessl review fix ./engineering-team/skills/senior-data-engineer/SKILL.md

Quality

Content

72%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured with strong progressive disclosure and actionable Quick Start commands, but inline workflow sequencing and validation checkpoints are thin because real workflows are offloaded to reference files. Tightening the redundant trigger-phrase list would improve token efficiency.

Suggestions

Collapse or remove the 'Trigger Phrases' section — it duplicates the description's 'Use when' clause and consumes tokens without adding new trigger surface.

Inline a single end-to-end pipeline workflow with an explicit validation checkpoint (e.g. run data_quality_validator.py and only proceed on pass) so workflow_clarity is not capped at 3 for a batch skill.

Replace bare '→ See references/X.md for details' pointers with a one-line summary of what each reference contains, so the SKILL.md body stays actionable without opening every file.

Dimension	Reasoning	Score
Conciseness	Generally lean with efficient tables and a decision tree, but the long 'Trigger Phrases' list largely duplicates the description's 'Use when' clause and could be trimmed; sits above the 3 anchor but not fully token-optimal.	4 / 5
Actionability	Quick Start provides concrete, copy-pasteable commands (e.g. 'python scripts/pipeline_orchestrator.py generate --type airflow --source postgres --destination snowflake --schedule "0 5 * * *"') and references resolve to real files; minor gaps because several sections are bare pointers with no inline executable detail.	4 / 5
Workflow Clarity	The body itself sequences only loosely (Quick Start commands are parallel utilities, not a pipeline) and delegates real workflows to references/workflows.md; for a batch/data-pipeline skill the inline guidance lacks explicit validation checkpoints, capping this dimension at 3 per the rubric.	3 / 5
Progressive Disclosure	Clear overview with a table of contents and well-signaled one-level-deep references ('See references/data_pipeline_architecture.md for'); all referenced files exist and content is appropriately split between SKILL.md and references/, matching the top anchor.	5 / 5
	Total	16 / 20 Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is well-crafted: third-person voice, explicit 'Use when' trigger guidance, and a concrete capability inventory covering the modern data stack. It is comprehensive and distinct with only minor specificity gaps.

Dimension	Reasoning	Score
Specificity	Lists several concrete capability areas — 'building scalable data pipelines, ETL/ELT systems, and data infrastructure' plus 'data modeling, pipeline orchestration, data quality, and DataOps' — with only minor gaps in discrete action coverage; not a 5 because it enumerates domains/categories more than distinct atomic actions.	4 / 5
Completeness	Clearly answers both 'what' (building scalable data pipelines, ETL/ELT systems, data infrastructure, modeling, orchestration, quality, DataOps) and 'when' with an explicit 'Use when...' clause listing concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Strong natural trigger coverage via 'Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues'; a few common synonyms/variations are missing, so it sits just below the comprehensive 5 anchor.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear data-engineering niche with distinct triggers (pipelines, ETL/ELT, dbt, Airflow, Kafka, DataOps) and minimal overlap risk with adjacent devops/backend skills.	5 / 5
	Total	18 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: alirezarezvani/claude-skills
Path: engineering-team/skills/senior-data-engineer/SKILL.md
Commit: 19392f7

Reviewed: ~7 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.