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Senior Data Engineer

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borghei·vlatest·data

Analyzes complex data pipelines, identifies bottlenecks, and suggests optimized solutions for senior data engineering teams.

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 borghei/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

borghei

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.comundeclared

    What it found

    • engineering/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 borghei/claude-skills at 4a698e8d07.

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

Commit
4a698e8d07
Manifest
engineering/senior-data-engineer/SKILL.md
Scanned
9 files, 7,984 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 assists senior data engineering teams by analyzing and optimizing complex data pipelines. It generates configuration code for orchestration tools like Airflow, Prefect, and Dagster; validates data quality through profiling and anomaly detection; and provides actionable recommendations to improve the performance of SQL and Spark queries. The tool supports both generating pipeline configurations and validating existing ones.

When to use it

  • Generating initial pipeline configurations for moving data between systems (e.g., PostgreSQL to Snowflake).
  • Validating the quality of datasets against defined schemas, including detecting anomalies.
  • Profiling datasets to understand their characteristics and identify potential issues.
  • Optimizing slow-running SQL or Spark queries within a data warehouse environment.
  • Estimating query costs for resource planning and optimization purposes.

Key capabilities

  • Pipeline Generation: Generates code for Airflow, Prefect, and Dagster pipelines.
  • Data Quality Validation: Validates data against schemas and detects anomalies using techniques like Great Expectations.
  • Data Profiling: Provides insights into dataset characteristics.
  • SQL/Spark Optimization: Analyzes and suggests improvements for SQL and Spark query performance.
  • Cost Estimation: Estimates the cost of executing queries in environments like Snowflake and BigQuery.

Example prompts

  • "Generate an Airflow DAG to incrementally load data from PostgreSQL into Snowflake, using tables 'orders' and 'customers'."
  • "Validate the quality of data.csv against the schema defined in schema.json, and detect any anomalies."
  • "Analyze this SQL query (query.sql) for performance bottlenecks and suggest optimizations for a Snowflake warehouse."

Tips & gotchas

  • The skill utilizes command-line tools (e.g., pipeline_orchestrator.py, data_quality_validator.py). Ensure these scripts are accessible within the agent's environment.
  • Subcommands support --json for machine-readable output and --output to write results to files.
  • The skill is designed for senior data engineering tasks; familiarity with concepts like Airflow DAGs, SQL query optimization, and Spark is beneficial.