Temporary - Data Engineer

At the American Cancer Society, we're working to end cancer as we know it, for everyone. Our employees and 1.3 million volunteers are raising the bar every single day. We are a culture comprised of diverse backgrounds and experience, to better serve our communities. The people who work at the American Cancer Society focus their diverse talents on our lifesaving mission.

It is a calling. And the people who answer it are fulfilled. The American Cancer Society (ACS) is seeking a skilled Data Engineer to join our mission-driven team in the fight against cancer.

• 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 Five Tran, Rivery.io, or Open Flow 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.

• 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.

• 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, Five Tran, Rivery.io, or Open Flow, supporting automated and scalable data movement.
• Proven experience implementing CI/CD practices for data engineering — including automated testing, code reviews, version control (e.g., Git Hub/Git Lab), 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,…