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data-engineer

Design and build ETL/ELT pipelines and lakehouse architectures.

Updated Mar 16, 2026
Search Tags:#ETL#dbt#data engineering#data pipelines#spark#lakehouse#ELT
One-click install
npx skills add https://github.com/Likas07/t3code-skills --skill data-engineer-likas07
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-engineer
Source: https://github.com/Likas07/t3code-skills/tree/main/skills/data-engineer
Command: npx skills add https://github.com/Likas07/t3code-skills --skill data-engineer-likas07

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the design, building, and operation of data infrastructure, transforming raw data into reliable, analytics-ready assets.

Core Features & Use Cases

  • Data Pipeline Engineering: Design and build idempotent, observable, and self-healing ETL/ELT pipelines.
  • Data Platform Architecture: Architect cloud-native data lakehouses and design open table format strategies.
  • Data Quality & Reliability: Define and enforce data contracts, implement SLA-based monitoring, and build data lineage tracking.
  • Streaming & Real-Time Data: Build event-driven pipelines with Kafka/Kinesis and implement stream processing.
  • Use Case: Automate the ingestion, cleansing, and aggregation of customer order data into a gold layer for business intelligence reporting, ensuring data freshness and quality.

Quick Start

Use the data-engineer skill to build a daily revenue aggregation from silver orders into a gold table.

Frequently Asked Questions about data-engineer

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

FAQPage Schema
How do I build reliable data pipelines for ETL and ELT workflows?▼

Medallion architecture organizes data into bronze, silver, and gold layers within a lakehouse. It progressively refines raw data into analytics-ready assets, allowing you to automate daily aggregations, such as moving silver orders into a gold table for business intelligence reporting.

How do I enforce data contracts and lineage tracking in a data lakehouse?▼

Event-driven pipelines process streaming data by integrating Kafka or Kinesis with stream processing frameworks. This architecture transforms real-time raw data into analytics-ready assets, ensuring data freshness and quality for immediate operational use.

What's the best way to architect a scalable data platform using Apache Spark and dbt?▼

Scalable data platforms are architected by combining Apache Spark for distributed processing with dbt for ELT transformations. This strategy supports open table formats and lakehouse designs to effectively process and transform large volumes of raw data.

Does this approach support building streaming systems with open table formats?▼

Data quality is maintained by building self-healing pipelines with built-in observability and data lineage tracking. This approach enforces data contracts and SLA-based monitoring to guarantee that raw data is reliably transformed into analytics-ready assets.

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