Engineering Data Pipelines
telagod/code-abyss
Data engineering knowledge reference covering Airflow, Dagster, Kafka Streams, Flink, dbt, and data quality patterns.
Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka.
Install Senior Data Engineer
$ npx skills add borghei/Claude-Skills --skill senior-data-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/Claude-Skills senior-data-engineer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/senior-data-engineer .claude/skills/senior-data-engineer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "senior-data-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-engineer into .claude/skills/senior-data-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-engineer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-engineerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add borghei/Claude-Skills --skill senior-data-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/Claude-Skills senior-data-engineer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/senior-data-engineer .agents/skills/senior-data-engineer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "senior-data-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-engineer into .agents/skills/senior-data-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-engineer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill senior-data-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/Claude-Skills senior-data-engineer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/senior-data-engineer .cursor/skills/senior-data-engineer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "senior-data-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-engineer into .cursor/skills/senior-data-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-engineer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/borghei/Claude-Skills.git --path engineering/senior-data-engineer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add borghei/Claude-Skills --skill senior-data-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/Claude-Skills senior-data-engineer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/senior-data-engineer .gemini/skills/senior-data-engineer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "senior-data-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-engineer into .gemini/skills/senior-data-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-engineer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install borghei/Claude-Skills senior-data-engineerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add borghei/Claude-Skills --skill senior-data-engineer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/senior-data-engineer .github/skills/senior-data-engineer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "senior-data-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-engineer into .github/skills/senior-data-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-engineer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill senior-data-engineer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install borghei/Claude-Skills senior-data-engineer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/senior-data-engineer .opencode/skills/senior-data-engineer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "senior-data-engineer" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-engineer into .opencode/skills/senior-data-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-engineer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
senior-data-engineer- GitHub stars
- 874
- Token cost
- ~1.4k tokens
- SKILL.md length
- 411 words
- Files
- 9 (incl. scripts, references)
- Skills in repo
- 364
- Repo updated
- First seen
- Licence
- MIT
At a glance
Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka.
- Designing data architectures
- SKILL.md covers Core Capabilities, When to Use, Clarify First and Quick Start, plus 3 more sections
- Runs Python scripts from its folder; calls python
- Building pipelines
What it does
Senior Data Engineer is an agent skill from borghei/Claude-Skills. Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `references/data_modeling_patterns.md`, `references/data_pipeline_architecture.md` and `references/dataops_best_practices.md`).
It sits in Data & Analytics, covering Data pipelines and ETL. It works with Apache Airflow, dbt, Apache Kafka and Dagster. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
When your agent uses it
- Designing data architectures
- Building pipelines
- Adding data-quality checks
- Optimizing ETL/ELT
Example prompts
- “/senior-data-engineer”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit c9a1487. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Senior Data Engineer loads about 1.4k tokens when it runs, and up to ~29k if it reads all its reference files. Until then it costs about 63 tokens; SKILL.md has 411 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from borghei/Claude-Skills at commit c9a1487, republished under its MIT licence (© borghei). 411 words, ~1,373 tokens.
Download SKILL.mdSave it as
.claude/skills/senior-data-engineer/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.- name
- senior-data-engineer
- description
- Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.
- license
- MIT + Commons Clause
- metadata.version
- 1.2.0
- metadata.author
- borghei
- metadata.category
- engineering
- metadata.domain
- data-engineering
- metadata.updated
- 2026-06-17
- metadata.tags
- airflow, spark, data-pipelines, warehousing, etl
- metadata.python-tools
- pipeline_orchestrator.py, data_quality_validator.py, etl_performance_optimizer.py
- metadata.tech-stack
- python, sql, spark, airflow, dbt, kafka
Senior Data Engineer
Generate pipeline configurations (Airflow, Prefect, Dagster), validate data quality with profiling and anomaly detection, and optimize SQL/Spark performance with actionable recommendations.
Core Capabilities
- Pipeline generation — Airflow/Prefect/Dagster DAG code for batch and incremental loads, with DAG validation.
- Data quality — schema validation, profiling, anomaly detection, data contracts, and Great Expectations suite generation.
- ETL/ELT optimization — SQL and Spark analysis, partition strategy, and query cost estimation per warehouse.
- Architecture decisions — batch vs streaming and warehouse vs lakehouse trade-off frameworks.
- Reliability patterns — incremental watermarks, dead letter queues, freshness checks, and schema-drift detection.
When to Use
- Designing a data architecture or choosing batch vs streaming / warehouse vs lakehouse.
- Building or generating Airflow/Spark/dbt pipelines.
- Adding data-quality checks or data contracts.
- Optimizing slow ETL/ELT queries or troubleshooting pipeline failures.
Clarify First
Before generating pipelines, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Orchestrator — Airflow / Prefect / Dagster (
--type; changes the generated DAG code) - Source, destination & load mode — systems involved and batch vs incremental (
--source/--destination/--mode; shapes the pipeline) - Data-quality expectations — the schema and contracts to enforce (drives the Great Expectations suite generation)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Quick Start
# Generate an Airflow DAG for incremental PostgreSQL -> Snowflake
python scripts/pipeline_orchestrator.py generate \
--type airflow --source postgres --destination snowflake \
--tables orders,customers --mode incremental --schedule "0 5 * * *"
# Validate data quality against a schema
python scripts/data_quality_validator.py validate data.csv \
--schema schema.json --detect-anomalies --json
# Profile a dataset
python scripts/data_quality_validator.py profile data.csv --json
# Optimize a slow SQL query
python scripts/etl_performance_optimizer.py analyze-sql query.sql \
--warehouse snowflake --json
# Estimate query cost
python scripts/etl_performance_optimizer.py estimate-cost query.sql \
--warehouse bigquery --stats data_stats.json --jsonTools
| Tool | Subcommands | Purpose |
|---|---|---|
pipeline_orchestrator.py | generate, validate, template | Generate Airflow/Prefect/Dagster pipeline code, validate DAGs |
data_quality_validator.py | validate, profile, generate-suite, contract, schema | Schema validation, profiling, anomaly detection, Great Expectations |
etl_performance_optimizer.py | analyze-sql, analyze-spark, optimize-partition, estimate-cost, template | SQL/Spark optimization, partition strategy, cost estimation |
All subcommands support --json for machine-readable output and --output for file writing.
Show full SKILL.md (149 more words)Show less
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/pipeline-workflows.md — the three end-to-end worked pipelines with code: batch ETL (PostgreSQL → dbt → Snowflake), real-time streaming (Kafka → Spark → Delta Lake), and the data-quality framework. Read when building a concrete pipeline.
- references/decisions-and-troubleshooting.md — the batch-vs-streaming and warehouse-vs-lakehouse decision frameworks, anti-patterns, and the troubleshooting table. Read when choosing an architecture or diagnosing a failure.
- references/data_pipeline_architecture.md — deep reference on pipeline architecture patterns. Read for architecture design depth.
- references/data_modeling_patterns.md — dimensional modeling and data-modeling patterns. Read when modeling marts and dimensions.
- references/dataops_best_practices.md — DataOps practices for CI/CD, testing, and operating pipelines. Read when operationalizing pipelines.
Integration Points
| Skill | Integration |
|---|---|
senior-data-scientist | Feature engineering consumes curated mart data |
senior-ml-engineer | ML pipelines depend on feature store tables |
senior-devops | CI/CD for dbt, Airflow deployment, container orchestration |
senior-architect | Architecture reviews for lakehouse vs warehouse decisions |
code-reviewer | Pipeline code reviews for DAGs, dbt models, Spark jobs |
© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 8 other files (scripts, references) in engineering/senior-data-engineer of borghei/Claude-Skills.
- SKILL.md
- references/data_modeling_patterns.md
- references/data_pipeline_architecture.md
- references/dataops_best_practices.md
- references/decisions-and-troubleshooting.md
- references/pipeline-workflows.md
- scripts/data_quality_validator.py
- scripts/etl_performance_optimizer.py
- scripts/pipeline_orchestrator.py
Open the folder on GitHubat commit c9a1487
Compare with similar skills
Senior Data Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Senior Data Engineer this skillborghei/Claude-Skills | 874 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Engineering Data Pipelinestelagod/code-abyss | 244 | — | ~236 | Automated safety check: Pass | MIT | |
| Migrating Dagster To Airflowastronomer/agents | 450 | — | ~3.8k | Automated safety check: Pass | Apache-2.0 | |
| Senior Data Engineerbenchflow-ai/skillsbench | 1.8k | — | ~5.9k | Automated safety check: Pass | MIT | |
| Senior Data Engineeralirezarezvani/claude-skills | 28k | 3 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Senior Data Engineerdavila7/claude-code-templates | 32k | 1 repos | ~1.4k | Automated safety check: Pass | MIT |
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Questions about Senior Data Engineer
What does Senior Data Engineer do?
Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Senior Data Engineer is an agent skill from borghei/Claude-Skills. Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka.
When should I use Senior Data Engineer?
Senior Data Engineer fits situations like: designing data architectures; building pipelines; adding data-quality checks; optimizing ETL/ELT.
How do I install Senior Data Engineer in Claude Code?
Run `npx skills add borghei/Claude-Skills --skill senior-data-engineer -a claude-code`. Or copy the skill folder (engineering/senior-data-engineer in borghei/Claude-Skills) into .claude/skills/senior-data-engineer in your project. Claude Code loads it when a task matches its description.
How do I install Senior Data Engineer in Codex?
Run `npx skills add borghei/Claude-Skills --skill senior-data-engineer -a codex`. Or copy the skill folder (engineering/senior-data-engineer in borghei/Claude-Skills) into .agents/skills/senior-data-engineer in your project. Codex loads it when a task matches its description.
Can I use Senior Data Engineer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill senior-data-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/senior-data-engineer, .gemini/skills/senior-data-engineer, .github/skills/senior-data-engineer and .opencode/skills/senior-data-engineer in your project.
What does Senior Data Engineer need to run?
Going by SKILL.md and its folder, Senior Data Engineer needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Senior Data Engineer access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Senior Data Engineer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Senior Data Engineer use?
Senior Data Engineer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Senior Data Engineer use?
About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 28k tokens, read only when the agent opens those files.
What are the alternatives to Senior Data Engineer?
Skills that share tags, products or a category with Senior Data Engineer: Engineering Data Pipelines (telagod/code-abyss, 244 stars), Migrating Dagster To Airflow (astronomer/agents, 450 stars), Senior Data Engineer (benchflow-ai/skillsbench, 1.8k stars) and Senior Data Engineer (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Senior Data Engineer?
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 874 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.