Job Location (Full Address):

220 Hutchison Rd, Rochester, New York, United States of America, 14620

Opening:

Worker Subtype:

Regular

Time Type:

Full time

Scheduled Weekly Hours:

40

Department:

400010 Neuroscience

Work Shift:

UR - Day (United States of America)

Range:

UR URG 113

Compensation Range:

$77,216.00 - $115,824.00

Responsibilities:

Designs, develops, and maintains data engineering and analytics infrastructure to support MRI research, neuroimaging workflows, and multimodal scientific data analysis within the URMC CABIN research environment. Builds and supports scalable data pipelines and software systems that enable researchers to collect, process, manage, and analyze structured and unstructured research data generated from MRI scanners, imaging analysis tools, and related research platforms.

Develops data integration frameworks that aggregate information from multiple sources including imaging systems, research databases, clinical systems, and analysis environments. Supports the implementation and maintenance of research data infrastructure such as data repositories, data lakes, and workflow automation systems used in MRI and neuroscience research. Collaborates with researchers, engineers, and IT teams to deliver reliable, scalable, and reproducible data and software solutions that support scientific discovery and advanced imaging analysis workflows.

ESSENTIAL FUNCTIONS

Data Pipeline Development:

• Designs, builds, and maintains scalable Extract, Transform, and Load (ETL) pipelines that ingest and process large volumes of MRI and research data from diverse sources including imaging systems, research databases, and scientific computing platforms. Develops and maintains data architecture capable of supporting growing imaging datasets and complex research workflows.

Research Software Development:

• Collaborates with research teams to translate scientific, technical, and operational requirements into robust software and data workflow solutions. Develops tools and applications to support MRI data collection, processing, analysis, and reporting. Manages multiple project timelines while ensuring solutions meet the needs of research teams and stakeholders.

Imaging Data Workflow Solutions:

• Designs and implements project-specific data workflows supporting MRI research studies, including automated data ingestion, preprocessing pipelines, metadata management, and analytics infrastructure. Supports reproducible research practices and contributes technical expertise to the scientific research process.

Data Infrastructure Support:

• Supports the development and maintenance of research data infrastructure, including data repositories, research data lakes, high-performance computing (HPC) environments, and data access APIs. Ensures reliable, secure, and scalable access to research datasets used in imaging analysis and scientific computing workflows.

Software Engineering Practices:

• Follows established software development lifecycle practices including requirements gathering, architecture design, test planning, version control, code review, and deployment. Implements automated testing, validation, and quality assurance processes to ensure reliability of research data workflows and software tools.

Testing and Validation:

• Participates in the design and execution of testing procedures to validate data pipelines, research software, and system integrations. Ensures accuracy, reliability, and reproducibility of data processing workflows used in research studies.

Documentation:

• Develops and maintains comprehensive technical documentation for data pipelines, system architecture, APIs, and workflow automation tools. Ensures documentation supports maintainability, reproducibility, and long-term sustainability of research systems.

Technology Research and Development:

• Stays informed on emerging technologies in data engineering, neuroimaging analysis, scientific computing, and research software development. Evaluates new tools, frameworks, and platforms that may enhance MRI research data processing, analytics, and data management capabilities.

Additional Responsibilities:

• Performs other duties as assigned to support the technical and operational needs of the CABIN MRI research computing environment.

MINIMUM EDUCATION & EXPERIENCE

• Bachelor’s degree in Data Science, Computer Science, Biomedical Informatics, Bioinformatics, Statistics, Engineering, or a related field (required)
• 2+ years of experience in data engineering, research computing, or data-intensive scientific environments (required)
• An equivalent combination of education and experience may be considered (required)
• Strong programming experience in SQL and at least one additional language such as Python, R, or Java (required)
• Experience building and maintaining ETL pipelines and research data workflows (required)
• Experience working with large scientific datasets, particularly imaging or biomedical research data (preferred)
• Familiarity with MRI or neuroimaging data formats (e.g., DICOM, NIfTI) (preferred)
• Experience with Linux-based scientific computing environments (preferred)
• Experience with high-performance computing (HPC), container technologies (e.g., Docker/Singularity), or cloud infrastructure (IaaS/PaaS) (preferred)
• Experience with version control systems (e.g., Git) and collaborative software development workflows (preferred)
• Experience with data management systems used in research environments (e.g., REDCap, electronic lab notebooks, biospecimen management systems) (preferred)
• Familiarity with data standards, metadata management, and data exchange formats used in scientific research (preferred)

KNOWLEDGE, SKILLS AND ABILITIES (required)

• Strong analytical and problem-solving abilities
• Ability to design scalable and maintainable data architectures
• Strong organizational and project coordination skills
• Ability to work effectively in collaborative and matrix research environments
• Excellent written and verbal communication skills for interacting with researchers and technical teams
• Ability to present technical concepts clearly to both technical and non-technical stakeholders
• Attention to detail and commitment to high-quality data and software practices