Providence is a comprehensive health care organization that serves a wide range of communities. They are seeking a Data Software Engineer II responsible for designing, building, and modernizing data platforms, developing scalable data pipelines, and ensuring data stewardship throughout the data lifecycle.
Responsibilities:
- Designing, building, and modernizing data platforms through the migration, consolidation, and refactoring of enterprise data warehouses
- Develop scalable data pipelines and streaming solutions
- Work with large‑scale datasets in cloud environments
- Apply best practices in data modeling, governance, and security
- Ensure accuracy, reliability, and compliance across the data lifecycle
- Implement robust validation, monitoring, and alerting frameworks
- Drive meaningful data insights for caregivers by enabling and supporting AI‑powered tools, including generative AI and LLM‑based solutions
Requirements:
- Bachelor's Degree in Computer Engineering, Computer Science, Mathematics, Engineering., Or equivalent education/experience
- 2 years related experience; software engineering experience preferred. 2-5 years preferred
- Experience with object-oriented programming in C#, Java, Python or equivalent
- Experience with source code control systems such as Git
- SQL integration development experience using SQL/NoSQL
- Experience with agile methodologies and tools such as Azure Devops, TFS, and Jira
- Proven track record of working both independently and collaboratively as part of a multi-disciplined team
- Experience designing and successfully implementing a mid-sized project
- Experience in a healthcare setting
- Strong foundation in data engineering, including real‑time streaming architectures and end‑to‑end pipeline development
- Experience working with generative AI solutions, large language models (LLMs), and data‑driven platforms
- Experience modeling, processing, and optimizing large‑scale datasets in distributed or cloud environments
- Familiarity with data governance, security, and compliance best practices across the data lifecycle
- Proficiency in implementing data validation, monitoring, and alerting frameworks to ensure data reliability