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# Temporary - Data Engineer

- **Company:** American Cancer Society, Inc.
- **Location:** Atlanta, GA, United States
- **Experience:** Experienced
- **Salary:** $93,600 - $99,840
- **Contract:** Temporary contract
- **Skills:** Airflow, Automation of Tests, Unit Testing, Microsoft Azure, Cloud Computing, Cloud Database, Code Review, Computational Biology, Continuous Delivery, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Security, Data Systems, Data Visualization, Data Warehousing, Github, Python (Programming Language), Machine Learning, OpenFlow, Performance Tuning, Power BI, Software Deployment, SQL Databases, Tableau (Software), Unstructured Data, Privacy Controls, Azure Data Factory, Delivery Pipeline, Snowflake, Electronic Medical Records, Gitlab, Information Technology, Cosmos DB, Looker Analytics, Software Version Control, Data Pipelines, Databricks
- **Published:** November 3, 2026
- **Apply:** https://www.careerjet.com/job/usa30f5abfe4f2235bf83006efa4218b6e/eaa

## About the Role

* Minimum Degree Required: Bachelor's Degree in Computer Science, Engineering, Computational Biology, or equivalent experience
 * Years of experience: 3+ years of relevant work experience. 3+ years of building a modern data pipeline, including the ETL, cloud-storage, reporting, and deployment

KNOWLEDGE, SKILLS, AND ABILITY:

 * Advanced proficiency in cloud data engineering within Microsoft Azure, including services such as Azure Data Factory, Azure Databricks, and Cosmos DB; experience with Snowflake for data warehousing and administration.
 * Strong expertise in SQL and Python, including development of complex ETL/ELT workflows, automation scripts, and performance-optimized data transformations.
 * Hands-on experience with modern orchestration and ingestion platforms such as Prefect.io, Apache Airflow, FiveTran, Rivery.io, or OpenFlow, supporting automated and scalable data movement.
 * Proven experience implementing CI/CD practices for data engineering - including automated testing, code reviews, version control (e.g., GitHub/GitLab), and deployment pipelines.
 * Experience designing and managing large-scale, high-density data systems, including structured, semi-structured, and unstructured data such as genomics, imaging, and biospecimen datasets.
 * Familiarity with BI and visualization tools (e.g., Power BI, Tableau, Looker) and the ability to translate complex research or analytic requirements into optimized data models.
 * Strong understanding of healthcare and research data domains, including claims, electronic health records (EHR), survey, and clinical datasets; knowledge of data governance, compliance, and privacy standards (e.g., HIPAA).
 * Demonstrated collaboration skills working with data scientists, analytics engineers, and researchers to deliver high-quality, reliable, and well-documented data solutions.

TRAVEL REQUIREMENTS:

 * Occasional travel- 10-15% - may be required for department or program/project meetings, vendor management, or enterprise workshops., * Availability and ability to work after hours, weekends, holidays, etc. as needed, to be on call and/or to fulfill job responsibilities and requirements.

## Description

* Design, develop, and orchestrate modern data pipelines and integrations (30%) - Architect and manage scalable data workflows using Azure Data Factory, Snowflake, dbt, and cloud orchestration tools such as Prefect.io or Airflow, ensuring high performance and reliability.
 * Implement and manage cloud-based data infrastructure (20%) - Administer and optimize data environments in Microsoft Azure, including Cosmos DB, Snowflake, and related services, performing configuration, monitoring, and cost optimization.
 * Develop and deploy advanced data engineering solutions (20%) - Utilize Python for complex data transformation, automation, and performance optimization within robust CI/CD pipelines with comprehensive unit testing and peer code reviews.
 * Integrate data from diverse sources (10%) - Leverage ingestion platforms such as FiveTran, Rivery.io, or OpenFlow to streamline and automate data collection from internal and external systems.
 * Collaborate with cross-functional teams (10%) - Partner with analytics engineers, data scientists, and business stakeholders to design and refine data models that power research, visualization, and machine learning initiatives.
 * Support data governance, security, and documentation (10%) - Implement and enforce best practices for data security, access control, and compliance, including Snowflake user and instance management.