Remote Data Platform Engineer – Snowflake & AI Pipelines

National Debt Relief; Idaho; Remote; USD 125,000 - 144,000; Full time

Benefits offered by this job

Medical, dental, and vision benefits

401(k) with company match

Paid holidays and vacation

Job summary

National Debt Relief is seeking a Data Engineer to join the Data Engineering team to build, operate, and improve the data platform supporting analytics and decision-making. You will work on a Snowflake-based platform, focusing on ingestion, orchestration, transformation, and analytics-ready data models.

The role requires strong SQL and data modeling fundamentals, with depth in at least one area of the modern data stack, and the ability to collaborate across the platform.

Qualifications

• 4+ years of data engineering, analytics engineering, or data warehouse experience.
• Bachelor’s degree in a technical field or equivalent practical experience.
• Experience using Python to retrieve data from APIs.
• Experience with CI/CD workflows, version control, and automated testing for data pipelines.
• Experience in financial services or related industries preferred.
• Experience with Terraform or similar tooling preferred.
• Experience with semantic layers, metrics, or BI tools preferred.

Responsibilities

• Own data engineering projects end-to-end across ingestion, orchestration, transformation, and analytics layer.
• Develop Python-based ingestion pipelines as needed, integrating data from APIs and third-party systems.
• Design and build data pipelines using dbt, Python, Dagster, and Snowflake; apply sound modeling decisions.

Skills

SQL

Data modeling

Python

dbt

Dagster

Snowflake

AI coding agents

Infrastructure as code

Data pipelines

Education

Bachelor's degree in a technical field

Tools

Terraform

CI/CD tooling

Job description

National Debt Relief is seeking a Data Engineer to join the Data Engineering team to build, operate, and improve the data platform supporting analytics and decision-making. You will work on a Snowflake-based platform, focusing on ingestion, orchestration, transformation, and analytics-ready data models.

The role requires strong SQL and data modeling fundamentals, with depth in at least one area of the modern data stack, and the ability to collaborate across the platform.