Remote Data Architect - End-to-End Ownership

Energy Jobline ZR; United (WV); Remote; USD 124,000 - 248,000; Full time

Job summary

Rockwoods is seeking a hands-on Data Architect to lead the design, implementation, and optimization of modern data platforms. This role requires architectural vision, strong communication, and the ability to own solutions end-to-end in a fast-paced environment.

The ideal candidate will work with Snowflake, dbt, Python, orchestration tools, and cloud services (AWS/Azure/GCP) to build scalable pipelines and AI-ready metadata foundations, including LLM integration and RAG architectures.

Qualifications

• 10+ years in Data Architecture/Data Engineering.
• Hands-on with Snowflake, dbt, Python.
• Experience with orchestration tools like Airflow/ADF.
• Knowledge of data modeling, pipelines, and event-driven architectures.
• Cloud infrastructure experience (AWS/Azure/GCP).
• Experience with AI/ML data environments, LLM integration, and RAG.

Responsibilities

• Define and implement modern data platform patterns across Snowflake, dbt, Python, orchestration tools (Airflow, ADF, etc.), and cloud- services (AWS/Azure/GCP).
• Design API-based and event-driven ingestion frameworks for scalable, real-time data processing.
• Establish semantic and metadata foundations to enable AI-ready environments, including support for LLM and RAG-based architectures.
• Build scalable data pipelines, transformation layers, and advanced data models for analytics and AI/ML workloads.
• Guide engineers through design reviews, solution standards, and best practices.

Skills

Data Architecture

Data Engineering

Snowflake

dbt

Python

Airflow

ADF

Cloud Infrastructure

LLM integration

RAG architectures

AWS

Azure

GCP

Job description

Rockwoods is seeking a hands-on Data Architect to lead the design, implementation, and optimization of modern data platforms. This role requires architectural vision, strong communication, and the ability to own solutions end-to-end in a fast-paced environment.

The ideal candidate will work with Snowflake, dbt, Python, orchestration tools, and cloud services (AWS/Azure/GCP) to build scalable pipelines and AI-ready metadata foundations, including LLM integration and RAG architectures.