Senior Data Engineer
benchflow-ai/skillsbench
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure.
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure.
Install Senior Data Engineer
$ npx skills add alirezarezvani/claude-skills --skill senior-data-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/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/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering-team/skills/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/alirezarezvani/claude-skills/tree/main/engineering-team/skills/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/alirezarezvani/claude-skills/tree/main/engineering-team/skills/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 alirezarezvani/claude-skills --skill senior-data-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/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/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering-team/skills/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/alirezarezvani/claude-skills/tree/main/engineering-team/skills/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 alirezarezvani/claude-skills --skill senior-data-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/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/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering-team/skills/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/alirezarezvani/claude-skills/tree/main/engineering-team/skills/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/alirezarezvani/claude-skills.git --path engineering-team/skills/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 alirezarezvani/claude-skills --skill senior-data-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/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/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering-team/skills/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/alirezarezvani/claude-skills/tree/main/engineering-team/skills/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 alirezarezvani/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 alirezarezvani/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/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering-team/skills/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/alirezarezvani/claude-skills/tree/main/engineering-team/skills/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 alirezarezvani/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 alirezarezvani/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/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering-team/skills/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/alirezarezvani/claude-skills/tree/main/engineering-team/skills/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
- 28k
- Used in
- 3 other repos
- Token cost
- ~1.4k tokens
- SKILL.md length
- 457 words
- Files
- 9 (incl. scripts, references)
- Skills in repo
- 342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure.
- Works in 3 steps: Data Pipeline Architecture → Data Modeling Patterns → DataOps Best Practices
- Designing data architectures
- SKILL.md covers Table of Contents, Trigger Phrases, Quick Start and Workflows, plus 4 more sections
- Runs Python scripts from its folder; calls python
What it does
Senior Data Engineer is an agent skill from alirezarezvani/claude-skills. 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.
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, Python and SQL. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.
When your agent uses it
- Designing data architectures
- Building data pipelines
- Optimizing data workflows
- Implementing data governance
Example prompts
- “/senior-data-engineer”
Requirements
- Python 3
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 19392f7. 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 ~32k if it reads all its reference files. Until then it costs about 107 tokens; SKILL.md has 457 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 alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 457 words, ~1,433 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 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.
Senior Data Engineer
Production-grade data engineering skill for building scalable, reliable data systems.
Table of Contents
- Trigger Phrases
- Quick Start
- Workflows
- Architecture Decision Framework
- Tech Stack
- Reference Documentation
- Troubleshooting
Trigger Phrases
Activate this skill when you see:
Pipeline Design:
- "Design a data pipeline for..."
- "Build an ETL/ELT process..."
- "How should I ingest data from..."
- "Set up data extraction from..."
Architecture:
- "Should I use batch or streaming?"
- "Lambda vs Kappa architecture"
- "How to handle late-arriving data"
- "Design a data lakehouse"
Data Modeling:
- "Create a dimensional model..."
- "Star schema vs snowflake"
- "Implement slowly changing dimensions"
- "Design a data vault"
Data Quality:
- "Add data validation to..."
- "Set up data quality checks"
- "Monitor data freshness"
- "Implement data contracts"
Performance:
- "Optimize this Spark job"
- "Query is running slow"
- "Reduce pipeline execution time"
- "Tune Airflow DAG"
Quick Start
Core Tools
# Generate pipeline orchestration config
python scripts/pipeline_orchestrator.py generate \
--type airflow \
--source postgres \
--destination snowflake \
--schedule "0 5 * * *"
# Validate data quality
python scripts/data_quality_validator.py validate \
--input data/sales.parquet \
--schema schemas/sales.json \
--checks freshness,completeness,uniqueness
# Optimize ETL performance
python scripts/etl_performance_optimizer.py analyze \
--query queries/daily_aggregation.sql \
--engine spark \
--recommendWorkflows
→ See references/workflows.md for details
Architecture Decision Framework
Use this framework to choose the right approach for your data pipeline.
Batch vs Streaming
| Criteria | Batch | Streaming |
|---|---|---|
| Latency requirement | Hours to days | Seconds to minutes |
| Data volume | Large historical datasets | Continuous event streams |
| Processing complexity | Complex transformations, ML | Simple aggregations, filtering |
| Cost sensitivity | More cost-effective | Higher infrastructure cost |
| Error handling | Easier to reprocess | Requires careful design |
Decision Tree:
Is real-time insight required?
├── Yes → Use streaming
│ └── Is exactly-once semantics needed?
│ ├── Yes → Kafka + Flink/Spark Structured Streaming
│ └── No → Kafka + consumer groups
└── No → Use batch
└── Is data volume > 1TB daily?
├── Yes → Spark/Databricks
└── No → dbt + warehouse computeLambda vs Kappa Architecture
| Aspect | Lambda | Kappa |
|---|---|---|
| Complexity | Two codebases (batch + stream) | Single codebase |
| Maintenance | Higher (sync batch/stream logic) | Lower |
| Reprocessing | Native batch layer | Replay from source |
| Use case | ML training + real-time serving | Pure event-driven |
When to choose Lambda:
- Need to train ML models on historical data
- Complex batch transformations not feasible in streaming
- Existing batch infrastructure
When to choose Kappa:
- Event-sourced architecture
- All processing can be expressed as stream operations
- Starting fresh without legacy systems
Show full SKILL.md (173 more words)Show less
Data Warehouse vs Data Lakehouse
| Feature | Warehouse (Snowflake/BigQuery) | Lakehouse (Delta/Iceberg) |
|---|---|---|
| Best for | BI, SQL analytics | ML, unstructured data |
| Storage cost | Higher (proprietary format) | Lower (open formats) |
| Flexibility | Schema-on-write | Schema-on-read |
| Performance | Excellent for SQL | Good, improving |
| Ecosystem | Mature BI tools | Growing ML tooling |
Tech Stack
| Category | Technologies |
|---|---|
| Languages | Python, SQL, Scala |
| Orchestration | Airflow, Prefect, Dagster |
| Transformation | dbt, Spark, Flink |
| Streaming | Kafka, Kinesis, Pub/Sub |
| Storage | S3, GCS, Delta Lake, Iceberg |
| Warehouses | Snowflake, BigQuery, Redshift, Databricks |
| Quality | Great Expectations, dbt tests, Monte Carlo |
| Monitoring | Prometheus, Grafana, Datadog |
Reference Documentation
1. Data Pipeline Architecture
See references/data_pipeline_architecture.md for:
- Lambda vs Kappa architecture patterns
- Batch processing with Spark and Airflow
- Stream processing with Kafka and Flink
- Exactly-once semantics implementation
- Error handling and dead letter queues
2. Data Modeling Patterns
See references/data_modeling_patterns.md for:
- Dimensional modeling (Star/Snowflake)
- Slowly Changing Dimensions (SCD Types 1-6)
- Data Vault modeling
- dbt best practices
- Partitioning and clustering
3. DataOps Best Practices
See references/dataops_best_practices.md for:
- Data testing frameworks
- Data contracts and schema validation
- CI/CD for data pipelines
- Observability and lineage
- Incident response
Troubleshooting
→ See references/troubleshooting.md for details
© alirezarezvani, 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-team/skills/senior-data-engineer of alirezarezvani/claude-skills.
- SKILL.md
- references/data_modeling_patterns.md
- references/data_pipeline_architecture.md
- references/dataops_best_practices.md
- references/troubleshooting.md
- references/workflows.md
- scripts/data_quality_validator.py
- scripts/etl_performance_optimizer.py
- scripts/pipeline_orchestrator.py
Open the folder on GitHubat commit 19392f7
Used in 3 other repositories
We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in alirezarezvani/claude-skills, which our catalogue first saw on October 7, 2026.
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 skillalirezarezvani/claude-skills | 28k | 3 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Senior Data Engineerbenchflow-ai/skillsbench | 1.8k | — | ~5.9k | Automated safety check: Pass | MIT | |
| Senior Data Engineerdavila7/claude-code-templates | 32k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Transforming Dataancoleman/ai-design-components | 526 | — | ~3k | Automated safety check: Pass | MIT | |
| Databricks JobsKilo-Org/kilo-marketplace | 190 | 1 repos | ~3.1k | Automated safety check: Pass | Custom licence | |
| Engineering Data Pipelinestelagod/code-abyss | 243 | — | ~236 | Automated safety check: Pass | MIT |
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Questions about Senior Data Engineer
What does Senior Data Engineer do?
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Senior Data Engineer is an agent skill from alirezarezvani/claude-skills. Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure.
When should I use Senior Data Engineer?
Senior Data Engineer fits situations like: designing data architectures; building data pipelines; optimizing data workflows; implementing data governance.
How do I install Senior Data Engineer in Claude Code?
Run `npx skills add alirezarezvani/claude-skills --skill senior-data-engineer -a claude-code`. Or copy the skill folder (engineering-team/skills/senior-data-engineer in alirezarezvani/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 alirezarezvani/claude-skills --skill senior-data-engineer -a codex`. Or copy the skill folder (engineering-team/skills/senior-data-engineer in alirezarezvani/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 alirezarezvani/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 (the repository's licence). 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 5.7k 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 31k 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: Senior Data Engineer (benchflow-ai/skillsbench, 1.8k stars), Senior Data Engineer (davila7/claude-code-templates, 32k stars), Transforming Data (ancoleman/ai-design-components, 526 stars) and Databricks Jobs (Kilo-Org/kilo-marketplace, 190 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?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,829 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.
Source: alirezarezvani/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.