Research Data Engineer II

Brown Medicine

1d•$100,027 - $165,048•Remote

About The Position

The Research Data Engineer II reports to the Manager, Data Architecture and designs, builds, and maintains scalable, performant, and resilient data systems that support effective decision-making and efficient business practices. The role operationalizes complex data pipelines, aligns data solutions with business objectives, and delivers high-performance data products using on-premise and cloud-native technologies. It also streamlines data management, improves data quality, and supports informed decision-making across the healthcare system. The engineer works with vendor-supplied and internally developed data warehouses and lakes, integrating data from clinical, supplier, financial, HR, application, third-party, and unstructured sources. The position supports data scientists through a centralized repository of reliable, high-quality data, and contributes to cloud migration, infrastructure modernization, and system optimization. It collaborates with organizational partners and data governance bodies, mentors junior engineers, and helps build organizational confidence in enterprise data. This is a remote position, with travel to sites in Rhode Island and Southeastern Massachusetts as needed. The employee must establish a secure workspace to protect confidential information, including PHI and PII, and is responsible for workspace setup and maintenance costs and appropriate internet connectivity. The role performs independently within broad departmental policies and practices, referring complex policy or procedure questions to a supervisor. There is no supervisory responsibility. The stated schedule is Monday through Friday, 8:30 a.m. to 5:00 p.m., with eight daily hours.

Requirements

Bachelor’s degree in a STEM-related field or equivalent.
Four years of experience in data engineering.
Strong proficiency in SQL, Python, and other data tools.
Experience 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, including the ability to break complex issues into practical solutions.
Excellent written and verbal communication skills, including the ability to convey technical information to technical and non-technical stakeholders.
Skill in rigorous and automated data validation techniques.
Knowledge of data documentation methods that prioritize the return on investment of documentation time.
Ability to operate a computer, requiring visual and manual dexterity.
Ability to establish and maintain a secure remote workspace and appropriate internet connectivity; the employee is responsible for associated workspace and connectivity costs.

Nice To Haves

Epic Clarity Cogito certification.
Experience in healthcare data management and familiarity with healthcare industry standards.

Responsibilities

Design, build, and maintain large-scale data processing systems and efficient, scalable, and secure data pipelines.
Work with vendor-supplied and internally developed data warehouses and lakes to reduce data duplication and inefficiencies.
Analyze data sources, assess their value, and recommend their inclusion in analytical environments.
Profile and reorganize complex or disjointed data sources.
Produce durable, well-documented data infrastructure requiring minimal upkeep.
Develop automated data validation and mapping rules to validate source updates, map new values, identify unexpected changes, and surface errors quickly.
Investigate data discrepancies and anomalies through root cause analysis and implement corrective action.
Integrate and ingest data from clinical, supplier, financial, HR, application, third-party, and unstructured sources.
Lead the design and development of ETL processes and evaluate the integrity of data movement, transformation code, and large datasets.
Document data processes, tools, and pipelines for data users.
Contribute to and maintain a unified data catalog and data model.
Engage organizational partners to identify emerging data requirements and terminology; manage and monitor common-term mappings in a centralized location.
Inform the data community about developments and capabilities from the data architecture team.
Build and maintain a centralized repository of reliable, high-quality data to support data scientists.
Mentor junior engineers and foster collaboration and continuous learning.
Develop training materials for data specialists across the organization.
Contribute to cloud migration, data infrastructure modernization, and system optimization initiatives.
Monitor analytics department capacity resources, including Microsoft cloud computing capacity, and implement usage-reduction plans through processing optimization without reducing data functions.
Work with data governance bodies to promote safer, more secure, and more effective data procedures.
Model Brown University Health’s values of Compassion, Accountability, Respect, and Excellence and demonstrate the core Success Factors: Instill Trust and Value Differences, Patient and Community Focus, and Collaborate.