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Data Pipeline Engineer

Checked
erichowens·vlatest·data·MIT

Helps with data, pipeline automation as part of automating DevOps pipelines and CI/CD workflows workflows.

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 curiositech/some_claude_skills --skill data-pipeline-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

erichowens

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Install target

Live repository

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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.

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  • Pinned to a commit: passed

    Scanned erichowens/some_claude_skills at 6713fc7a64.

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Commit
6713fc7a64
Manifest
.claude/skills/data-pipeline-engineer/SKILL.md
Scanned
1 file, 151 lines
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About this skill

What it does

This skill enables AI agents to act as expert data engineers specializing in building and optimizing data pipelines. It assists with tasks ranging from identifying data sources and choosing appropriate architectures (like Medallion, Lambda, or Kappa) to designing layers for data transformation and implementing quality gates. The skill leverages technologies like Spark, Kafka, Airflow, dbt, Great Expectations, and others commonly used in modern data stacks.

When to use it

  • You need help designing a new ETL/ELT pipeline from raw data sources to business-ready datasets.
  • You're struggling to choose between Lambda and Kappa architectures for your streaming data processing needs.
  • You want assistance structuring a dbt project with staging, intermediate, and mart layers.
  • You need guidance on implementing data quality checks using Great Expectations or dbt tests within a pipeline.
  • You are building an Airflow DAG for orchestrating data transformations and require help with sensors and task groups.

Key capabilities

  • Batch Processing: Expertise in Spark, dbt, and Databricks for incremental processing, partitioning, and Delta/Iceberg table formats.
  • Stream Processing: Proficiency in Kafka, Flink, and Spark Streaming for watermarks, exactly-once semantics, and windowing operations.
  • Orchestration: Knowledge of Airflow, Dagster, and Prefect for DAG design, sensor implementation, and task grouping.
  • Data Modeling: Experience with Kimball, Data Vault, and Slowly Changing Dimensions (SCD) modeling techniques using dbt and SQL.
  • Data Quality: Ability to implement validation suites and freshness checks using Great Expectations and dbt tests.

Example prompts

  • "I need help designing a Medallion architecture for ingesting data from Salesforce into Snowflake."
  • "What are the pros and cons of Lambda versus Kappa architectures for real-time fraud detection?"
  • "Can you show me an example Airflow DAG with sensors to monitor file arrival and trigger downstream tasks?"
  • “I want to implement a dbt project. What is a good directory structure?”

Tips & gotchas

  • The skill excels at providing guidance on architectural decisions and best practices for data engineering, rather than generating complete code implementations directly (though reference examples are provided).
  • Familiarity with core data engineering concepts like ETL/ELT, streaming architectures, and data warehousing is helpful to effectively utilize the skill.
  • Explore the ./references/ directory within the skill's files for detailed implementation examples of dbt projects, Airflow DAGs, and Spark Streaming jobs.