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Senior Data Engineer

Checked
ovachiever·vlatest·data

Analyzes complex data engineering challenges, designs scalable solutions, and optimizes pipelines for senior-level performance.

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 ovachiever/droid-tings --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

ovachiever

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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

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

  • Pinned to a commit: passed

    Scanned ovachiever/droid-tings at 7acd12a754.

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

Commit
7acd12a754
Manifest
skills/senior-data-engineer/SKILL.md
Scanned
7 files, 769 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 AI agents with expertise equivalent to a senior data engineer, enabling them to design, implement, and optimize production-grade AI/ML/Data systems. It leverages Python scripts for pipeline orchestration, data quality validation, and ETL performance optimization. The skill also offers access to reference documentation covering advanced data pipeline architecture, data modeling patterns, and DataOps best practices.

When to use it

  • Designing scalable data architectures for new AI/ML projects.
  • Optimizing existing data pipelines for improved performance and efficiency.
  • Implementing MLOps and DataOps best practices within a development workflow.
  • Troubleshooting complex issues related to data quality, pipeline failures, or system bottlenecks.
  • Reviewing and improving the security and compliance of data infrastructure.

Key capabilities

  • Pipeline orchestration using pipeline_orchestrator.py
  • Data Quality Validation with data_quality_validator.py
  • ETL Performance Optimization via etl_performance_optimizer.py
  • Expertise in advanced production patterns and architectures
  • Scalable system design and implementation
  • Performance optimization at scale
  • MLOps and DataOps best practices
  • Access to reference documentation on data pipeline architecture, modeling patterns, and DataOps

Example prompts

  • "Can you orchestrate a data pipeline processing files from /data/ and outputting results to /results/?"
  • "Analyze the data quality of the project/ directory."
  • "Optimize the ETL process using the configuration in config.yaml and deploy the changes."

Tips & gotchas

  • This skill is intended for complex data engineering challenges, not basic scripting tasks.
  • Familiarity with Python and common data tools (Spark, Airflow, dbt) will enhance usability.
  • Refer to the reference documentation (references/data_pipeline_architecture.md, references/data_modeling_patterns.md, references/dataops_best_practices.md) for detailed guidance on specific topics.