data-engineer

Name: data-engineer
Availability: InStock
Author: buiphucminhtam

Design scalable data pipelines with dbt, Airflow, Dagster, and Spark.

47|11|Updated Mar 6, 2026

Search Tags:#data #orchestration #dbt #pipeline #etl #airflow #data-quality

Ready to run

npx skills add https://github.com/buiphucminhtam/forgewright --skill data-engineer-buiphucminhtam

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-engineer
Source: https://github.com/buiphucminhtam/forgewright/tree/main/skills/data-engineer
Command: npx skills add https://github.com/buiphucminhtam/forgewright --skill data-engineer-buiphucminhtam

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

In many organizations, building and maintaining scalable data infrastructure and pipelines is complex and error-prone, slowing analytics and decision-making.

Core Features & Use Cases

ETL/ELT pipelines across diverse sources, warehouses, and marts
Data quality frameworks with validation, monitoring, and lineage
Orchestration and scheduling using Airflow or Dagster
Streaming and analytics engineering with Spark, dbt, and modern tools
Real-world use: streamline data ingestion, transformation, and delivery to analytics teams

Quick Start

Configure a production-ready data pipeline that ingests sources, applies transformations with dbt, and loads analytics-ready data into the warehouse.

Frequently Asked Questions about data-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a scalable data pipeline for ETL workflows?▼

To build a scalable data pipeline, you design ETL/ELT workflows that ingest diverse sources, apply transformations with dbt, and load analytics-ready data into the warehouse. This supports production-grade components and emphasizes idempotency.

What is the best way to orchestrate data pipelines using Airflow or Dagster?▼

Orchestrating data pipelines with Airflow or Dagster involves scheduling and automating ETL workflows across cloud platforms. This approach ensures reliable execution, data lineage tracking, and robust monitoring for production-grade infrastructure.

How does a data quality framework work with dbt transformations?▼

A data quality framework works with dbt by applying validation, monitoring, and lineage checks to your transformations. This ensures that analytics-ready data loaded into the warehouse meets predefined contracts and reliability standards.

Can I use Spark for streaming pipelines and analytics engineering?▼

Yes, you can use Spark for streaming pipelines and analytics engineering. The framework supports building real-time data infrastructure that ingests sources, applies transformations, and delivers data continuously to analytics teams.

Do I need data contracts to maintain reliable data infrastructure?▼

Yes, data contracts are needed to maintain reliable data infrastructure. They enforce schema and quality standards between producers and consumers, ensuring that your ETL pipelines remain robust, idempotent, and monitorable across diverse sources.

Why does my ETL pipeline fail to deliver analytics-ready data consistently?▼

ETL pipelines fail to deliver analytics-ready data consistently when they lack idempotency, robust monitoring, and data quality frameworks. Implementing orchestration with Airflow or Dagster and validation with dbt resolves these infrastructure challenges.