Skip to content
TrustedSkillsbeta
Back to all skills

Data Engineer

Listed
sickn33·vlatest·data·MIT

Automates data pipeline creation, transformation, and loading from diverse sources into usable formats.

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 sickn33/agentic-awesome-skills --skill 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

sickn33

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.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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

    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 sickn33/antigravity-awesome-skills at 5a4cdd9df8.

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

Commit
5a4cdd9df8
Manifest
skills/data-engineer/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
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 transforms an AI agent into a specialized data engineer, capable of designing and implementing scalable data pipelines and modern data architectures. It focuses on building robust solutions encompassing batch and streaming processing, data warehousing, lakehouse architectures, and cloud-native data services. The skill guides the agent through defining sources, choosing appropriate tools, implementing transformations, and monitoring pipeline performance to ensure reliability and cost-effectiveness.

When to use it

  • Designing new batch or streaming data pipelines.
  • Building a data warehouse or lakehouse architecture for your organization.
  • Implementing data quality checks, lineage tracking, or governance procedures within your data infrastructure.

Key capabilities

  • Modern Data Stack Expertise: Proficiency in technologies like Delta Lake, Apache Iceberg, Apache Hudi, Snowflake, BigQuery, Redshift, Databricks SQL, AWS S3, Azure Data Lake, Google Cloud Storage, Fivetran/Airbyte, dbt, and BI tools.
  • Data Mesh Architecture: Understanding of domain-driven data ownership principles.
  • Real-time Analytics: Experience with Apache Pinot, ClickHouse, and Apache Druid for real-time analytics solutions.
  • Batch Processing & ETL/ELT: Expertise in Apache Spark 4.0, dbt Core/Cloud, and Apache Airflow.

Example prompts

  • "Design a data pipeline to ingest website clickstream data into Snowflake using Airflow."
  • "Help me build a data lakehouse architecture on AWS S3 with Delta Lake for storing customer transaction data."
  • "Implement dbt transformations to standardize product category names in our BigQuery dataset."

Tips & gotchas

  • Data Contracts are Key: Clearly define data sources, service level agreements (SLAs), and data contracts before starting any pipeline design.
  • Security First: Always prioritize protecting Personally Identifiable Information (PII) and enforce least-privilege access controls.
  • Validation is Crucial: Validate data before writing it to production storage sinks to ensure data quality.