Remote Data Engineer II: Scalable Health Data Pipelines

Brown Medicine; United States; Remote; USD 100,000 - 165,000; Full time

Job summary

Brown University Health seeks a Data Engineer II to design, build, and maintain scalable data systems that enable informed decision making across the enterprise. You will operationalize complex data pipelines, ensure data quality, and collaborate with multiple departments to deliver high performance data products.

You will mentor junior engineers, contribute to cloud and on-premises data architectures, document data processes, and participate in data governance to safeguard sensitive information

Qualifications

• Bachelor’s degree in STEM-related field or equivalent.
• Proficient in SQL, Python, and data tools.
• Experience building scalable data systems and pipelines.
• Experience with cloud platforms and data warehousing.
• Healthcare data familiarity preferred.

Responsibilities

• Designs, builds, and maintains large-scale data processing systems to ensure efficiency, scalability, and security.
• Works with vendor-supplied and home-grown data warehouses/lakes to reduce duplication and inefficiencies.
• Develops automated data validation and mapping rules to validate regular data source updates.
• Mentors junior engineers and documents data processes for users across the organization.
• Contributes to cloud migration and data infrastructure modernization.

Skills

SQL

Python

Data modeling

ETL design

Data pipelines

Data validation

Analytical thinking

Communication

Education

Bachelor’s Degree in STEM

Tools

Cloud platforms

Data warehouses

ETL tools

Job description

Brown University Health seeks a Data Engineer II to design, build, and maintain scalable data systems that enable informed decision making across the enterprise. You will operationalize complex data pipelines, ensure data quality, and collaborate with multiple departments to deliver high performance data products.

You will mentor junior engineers, contribute to cloud and on-premises data architectures, document data processes, and participate in data governance to safeguard sensitive information