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Senior Data Engineer

Listed
alirezarezvani·vlatest·data·MIT

Leverages Alirez Rezvani's expertise to provide senior data engineering guidance, best practices, and solution design for complex projects.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add alirezarezvani/claude-skills --skill senior-data-engineer -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

alirezarezvani

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: api.pagerduty.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    api.pagerduty.comundeclaredjson-schema.orgstandards

    What it found

    • engineering-team/skills/senior-data-engineer/references/dataops_best_practices.md:1215contacts api.pagerduty.com
      curl -X POST https://api.pagerduty.com/incidents/{incident_id}/acknowledge

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned alirezarezvani/claude-skills at 19392f7a08.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
19392f7a08
Manifest
engineering-team/skills/senior-data-engineer/SKILL.md
Scanned
9 files, 8,649 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides advanced data engineering capabilities, including designing and implementing scalable data pipelines, optimizing data storage solutions, and integrating with big data technologies like Hadoop and Spark. It also supports real-time data processing and ETL (Extract, Transform, Load) workflows for enterprise-level data systems.

When to use it

  • Building large-scale data infrastructure for analytics or machine learning platforms.
  • Automating complex ETL processes across multiple data sources.
  • Optimizing query performance on big data clusters like Hadoop or cloud-based solutions.

Key capabilities

  • Design and deployment of scalable data pipelines
  • Integration with Hadoop, Spark, and cloud storage systems
  • Real-time data processing and transformation
  • Optimization of data schema and query performance

Example prompts

  • "Design a data pipeline to process real-time user activity logs from multiple sources."
  • "Optimize the query performance on our Hadoop cluster for faster analytics."
  • "Automate ETL workflows for daily ingestion of customer transaction data into a data warehouse."

Tips & gotchas

  • Ensure your environment has access to required big data tools like Spark or Hadoop.
  • Performance gains depend on proper schema design and data partitioning strategies.