Research Data Engineer II

Reports to Manager, Data Architecture. The Data Engineer II is responsible for designing, building, and maintaining scalable, performant, and resilient data systems that will enable effective decision making and efficient business practices. Leads the operationalization of complex data pipelines, ensures data solutions align with business objectives. Mentors junior team members, increases organizational confidence in the validity of enterprise data sources, collaborates across departments, and leverages on premise and cloud-native technologies to deliver high performance data products.

Streamlines data management processes, improving data quality, fostering collaboration, and providing a solid foundation for informed decision-making across the entire healthcare system. Brown University Health employees are expected to successfully role model the organization's values of Compassion, Accountability, Respect, and Excellence as these values guide our everyday actions with patients, customers and one another. In addition to our values, all employees are expected to demonstrate the core Success Factors which tell us how we work together and how we get things done.

The core Success Factors include:
Instill Trust and Value Differences;
Patient and Community Focus and Collaborate.

Designs, builds, and maintains large-scale data processing systems, to ensure data pipelines are efficient, scalable, and secure. Works with multiple vendor supplied and home-grown data warehouses and lakes to reduce data duplication and inefficiencies. Performs advanced analysis of data sources to determine their value, recommends inclusion in analytical environments. Profiles and re‑organizes existing data sources that may be complex and/or disjointed.

Produces solid, well documented data infrastructure that can be used for years with minimal upkeep. Develops completely automated data validation and data mapping rules that validate regular data source updates, map new data values, and ensure that data values have not unexpectedly changed significantly. Ensures that data validation errors are quickly identified. Performs root cause analysis for any discrepancies or anomalies and implements corrective action.

Integrates and ingests information from a diverse array of sources, including clinical data, inputs from suppliers, financial transactions, applications bolstering Human Resources (HR) and other services, third‑party information, and even unstructured data from tools as ubiquitous as Excel spreadsheets and online forms. Innovates and leads the design and development of Extract, Transform, Load (ETL) processes. Evaluates and ensures the integrity of data movement, transformation code, and large datasets.

Writes detailed documentation for data processes, tools, and pipelines with data users in mind. Contributes, maintains, and grows a unified data catalog and data model. Maintains a proactive engagement with organizational partners to ascertain and define new data requirements and terminologies as they emerge. Manages the mapping of common or related terms in a single, unified location. Monitors and adjusts mapping in this centralized space to improve coordination with operational partners.

Proactively informs the data community about new developments and capabilities provided by the data architecture team. Designs, builds, and maintains a centralized repository of high‑quality, reliable data, to streamline the workflow of data scientists enabling them to pivot from time‑consuming tasks of building and validating datasets to the more sophisticated and impactful work of developing AI models. Mentors and guides junior engineers, fosters a culture of collaboration and continuous learning.

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