Senior Data Engineer - Healthcare Analytics (Remote)

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

Benefits offered by this job

Dental

Vision

401(k) retirement savings plan

Group life insurance

AD&D insurance

FSA/HSA

Commuter benefits

Short-term and long-term disability

Supplemental insurance plans

Job summary

Fuze Health in Texas seeks a Senior Data Engineer to design, build and maintain data pipelines and the platform that powers operational insights for care teams and leadership.

You will collaborate with data scientists, data analysts, and DevOps to implement ELT/ETL workflows, ensure data quality and observability, and scale our cloud-based data infrastructure using Python, SQL, AWS, and Terraform.

Qualifications

• Bachelor's degree in a quantitative field such as Statistics, Economics, Engineering, Mathematics, or Data Science
• At least 4 years of work experience in software engineering
• Experience with one or more of the major cloud vendors: AWS, GCP, Azure
• Experienced in SQL-based and python data manipulation with large datasets
• Master's Degree in a quantitative field (Statistics, Economics, Engineering, Mathematics, or Data Science)
• Experience with AWS and Terraform
• Experience in designing ELT/ETL workflows
• Programming experience in Python
• At this time, Fuze Health is unable to sponsor or take over sponsorship of an employment Visa.

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

Python

SQL

Data engineering

Education

Bachelor's degree in a quantitative field

Master's degree in a quantitative field

Tools

Terraform

Job description

Fuze Health in Texas seeks a Senior Data Engineer to design, build and maintain data pipelines and the platform that powers operational insights for care teams and leadership.

You will collaborate with data scientists, data analysts, and DevOps to implement ELT/ETL workflows, ensure data quality and observability, and scale our cloud-based data infrastructure using Python, SQL, AWS, and Terraform.