Research Data Engineer II

Brown University Health is hiring a Research Data Engineer II to design, build, and maintain scalable data systems and operationalize complex data pipelines. The role supports reliable healthcare data products, data quality, cloud modernization, collaboration across departments, and mentoring junior engineers.

Responsibilities:

• 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. Produces training materials on a variety of topics for use by data specialists across the organization. Contributes to cloud migration initiatives, data infrastructure modernization, and system optimization efforts. Monitors analytics department capacity resources such as Microsoft cloud computing capacity. Proactively develops and implements plans to reduce analytics department capacity usage without reducing data functions through optimization of processing techniques. Works in tandem with data governance bodies to advocate for safer, more secure, and more effective data procedures

Requirements:

• Bachelor's Degree in STEM-related field or equivalent
• Proficient in Structured Query Language (SQL), Python, and other data tools
• Four years' experience in data engineering, with strong proficiency in SQL, Python, and other data tools
• Experience with architecting scalable data systems and working with large, complex datasets
• Experience designing and operationalizing complex solutions
• Experience with data modeling, ETL processes, and data pipeline optimization
• Firsthand experience with cloud platforms, data warehousing, and ETL design
• Demonstrated experience mentoring junior engineers and collaborating across teams
• Strong analytical and critical thinking skills with an ability to decompose complex issues into practical solutions
• Excellent written and verbal communication skills, with the ability to convey technical information to both technical and non-technical stakeholders
• Skilled in rigorous and automated data validation techniques
• Well versed in data documentation methods that prioritize a return on investment of documentation time
• Epic Clarity Cogito certification
• Experience in healthcare data management and familiarity with healthcare industry standards