Data Engineer — Azure Pipelines for Health & Research

American Cancer Society, Inc.; Atlanta (GA); On-site; USD 62,000 - 66,000; Full time

Benefits offered by this job

Paid time off

Medical benefits

Dental benefits

Retirement benefits

Wellness programs

Professional development

Job summary

The American Cancer Society is seeking a Data Engineer to design, build, and optimize cloud-based data pipelines and infrastructures. You will work with Azure, Snowflake, dbt, and orchestration tools to deliver scalable data solutions for research, visualization, and machine learning initiatives.

You will collaborate with analytics engineers, data scientists, and business stakeholders to refine data models, ensure data governance, and implement secure, well-documented data processes.

Qualifications

• Bachelor's degree in CS/Engineering or related field.
• 3+ years building modern data pipelines (ETL/ELT).
• Experience with cloud data pipelines and software development practices.

Responsibilities

• Design, develop, and orchestrate modern data pipelines using Azure, Snowflake, and dbt.
• Administer and optimize Azure data infrastructure including Cosmos DB and Snowflake.
• Develop data engineering solutions with Python in CI/CD pipelines with tests.
• Integrate data from platforms like FiveTran, Rivery.io, OpenFlow.
• Collaborate with analytics engineers, data scientists, and stakeholders.
• Support data governance and security, including Snowflake access management.

Skills

SQL

Python

CI/CD

Data modeling

Cloud data engineering

Education

Bachelor's degree

Tools

Azure Data Factory

Azure Databricks

Cosmos DB

Snowflake

Prefect

Airflow

Fivetran

Rivery

OpenFlow

Git/GitHub

Job description

The American Cancer Society is seeking a Data Engineer to design, build, and optimize cloud-based data pipelines and infrastructures. You will work with Azure, Snowflake, dbt, and orchestration tools to deliver scalable data solutions for research, visualization, and machine learning initiatives.

You will collaborate with analytics engineers, data scientists, and business stakeholders to refine data models, ensure data governance, and implement secure, well-documented data processes.