Senior Data Engineer – ETL & Cloud Pipelines (Remote)

Fuze Health Inc.; Idaho; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Dental

Vision

401(k) retirement plan

Group life insurance

AD&D insurance

FSA and HSA

Commuter benefits

STD/LTD insurance

Supplemental insurance plans

Flexible vacation time

Paid sick time

Paid holidays

Floating holidays

Paid parental leave

Caregiver leave

Lifestyle Spending Account

Job summary

Fuze Health Inc. is seeking a Senior Data Engineer to build and maintain a platform delivering timely data for operational teams and business stakeholders.

You will collaborate with Data Scientists, Data Analysts, and DevOps to transform data into actionable insights and codify infrastructure as code for scalable systems. The role emphasizes ELT/ETL design, large-scale data processing, and varies across healthcare data sources, with a focus on reliability and observability.

Qualifications

• Requires 4+ years in software engineering or data engineering.
• Experience with SQL-based data manipulation and Python on large datasets.
• Experience with one or more cloud vendors (AWS, GCP, Azure).
• Experience designing ELT/ETL workflows and building data pipelines.

Responsibilities

• Deploy new data pipelines.
• Design and build data observability platforms and metrics.
• Build metadata-driven pipeline solutions.
• Monitor and optimize data transformations.
• Build third party data integrations.

Skills

SQL

Python

ETL/ELT design

Data pipelines

Cloud platforms

Education

Bachelor's degree in a quantitative field

Master's degree in a quantitative field

Tools

AWS

GCP

Azure

Terraform

Job description

Fuze Health Inc. is seeking a Senior Data Engineer to build and maintain a platform delivering timely data for operational teams and business stakeholders.

You will collaborate with Data Scientists, Data Analysts, and DevOps to transform data into actionable insights and codify infrastructure as code for scalable systems. The role emphasizes ELT/ETL design, large-scale data processing, and varies across healthcare data sources, with a focus on reliability and observability.