data-engineer

Create idempotent ELT pipelines with Airflow, dbt, Spark, and Kafka.

Updated Apr 10, 2026
One-click install
npx skills add https://github.com/Exia-thd/Digital-Nervous --skill data-engineer-exia-thd
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: data-engineer
Source: https://github.com/Exia-thd/Digital-Nervous/tree/main/skills/data-engineer
Command: npx skills add https://github.com/Exia-thd/Digital-Nervous --skill data-engineer-exia-thd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Organizations struggle to reliably move, transform, and make data accessible at scale, leading to broken pipelines, data quality issues, and delayed insights.

Core Features & Use Cases

  • Idempotent ELT pipelines using Airflow/Dagster that load raw data, apply medallion architecture, and ensure repeatable results.
  • Data quality framework with schema contracts, tests, and anomaly detection integrated via dbt.
  • Stream processing support for real‑time ingestion using Spark or Kafka.
  • Use case: Build a daily pipeline that extracts sales data from an API, stores it in a Snowflake warehouse, and materializes reporting tables with dbt.

Quick Start

Ask the data engineer skill to design an ELT pipeline that loads daily sales data from an API into a Snowflake warehouse and creates dbt models for reporting.

Frequently Asked Questions about data-engineer

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

FAQPage Schema
How do I build idempotent ELT pipelines with Airflow and dbt?

To build idempotent ELT pipelines, this skill designs workflows using Airflow or Dagster that load raw data and apply a medallion architecture with dbt models to ensure repeatable, reliable results across runs.

What is medallion architecture and how does it apply to data warehousing?

Medallion architecture is a data design pattern that organizes data into bronze, silver, and gold layers. This skill applies it to progressively refine raw data into structured, analytics-ready reporting tables within your warehouse.

Can I use this approach for real-time stream processing with Kafka and Spark?

Yes, this skill supports real-time stream processing by designing ingestion workflows that use Spark or Kafka to capture and process streaming data for immediate analytical use cases.

How do I integrate data quality testing and anomaly detection into my data pipelines?

You can integrate data quality testing by defining schema contracts and anomaly detection checks directly within dbt models, ensuring pipeline outputs meet defined quality standards before reaching gold layers.

What is the best way to load daily API data into Snowflake and materialize reporting tables?

The best way to load daily API data into Snowflake is through an automated ELT pipeline that extracts source data, stores it in the warehouse, and uses dbt to materialize structured reporting tables for analytics.

Why do my data pipelines keep breaking and how do I prevent data quality issues?

Data pipelines break due to lack of idempotency and schema enforcement. This skill prevents these issues by implementing schema contracts, automated tests, and repeatable processing logic to guarantee reliable data delivery.