Position OverviewThe Research Data Manager will build and maintain the data infrastructure for the SOM Center. This includes the Laboratory Information Management System (LIMS), document and repository platforms such as Invenio, and the metadata databases and pipelines that connect them. The role brings together instrument, assay, and analysis data and makes it findable, traceable, and reusable, in line with NIH data management and sharing requirements. The position combines hands-on software development with data stewardship, using cloud-native tooling and AI/ML to make data easier to find and use.
Key Responsibilities- Data integration. Connect laboratory instruments, analysis pipelines, and external data sources to the LIMS. Build and maintain automated ingestion pipelines with validation, logging, and error handling.
- Data wrangling and format conversion. Write and maintain custom scripts and services that convert, normalize, and harmonize heterogeneous data formats (instrument exports, imaging metadata, omics outputs, spreadsheets) into standardized schemas.
- Repository and document management. Deploy, configure, and support document and research data management systems such as Invenio. Manage records, metadata schemas, persistent identifiers, and access controls.
- Database development. Design, create, and maintain databases for experimental metadata and small-to-moderate datasets, including schema design, migrations, indexing, and backup and recovery.
- AI/ML-assisted discovery. Incorporate ML/AI into data workflows and user interfaces, such as semantic search, metadata extraction and auto-annotation, and natural-language query tools, to help researchers find and use data.
- Platform and cloud engineering. Develop, containerize, and deploy software solutions on cloud or local Kubernetes resources. Maintain CI/CD pipelines, monitoring, and infrastructure-as-code.
- Tool evaluation. Evaluate and recommend packages, platforms, and standards for data management, and prepare short technical assessments for IDSS leadership.
- Training and documentation. Train laboratory and analytical staff, and write standard operating procedures and technical documentation.
Required Qualifications- Data integration. Connect laboratory instruments, analysis pipelines, and external data sources to the LIMS. Build and maintain automated ingestion pipelines with validation, logging, and error handling.
- Data wrangling and format conversion. Write and maintain custom scripts and services that convert, normalize, and harmonize heterogeneous data formats (instrument exports, imaging metadata, omics outputs, spreadsheets) into standardized schemas.
- Repository and document management. Deploy, configure, and support document and research data management systems such as Invenio. Manage records, metadata schemas, persistent identifiers, and access controls.
- Database development. Design, create, and maintain databases for experimental metadata and small-to-moderate datasets, including schema design, migrations, indexing, and backup and recovery.
- AI/ML-assisted discovery. Incorporate ML/AI into data workflows and user interfaces, such as semantic search, metadata extraction and auto-annotation, and natural-language query tools, to help researchers find and use data.
- Platform and cloud engineering. Develop, containerize, and deploy software solutions on cloud or local Kubernetes resources. Maintain CI/CD pipelines, monitoring, and infrastructure-as-code.
- Tool evaluation. Evaluate and recommend packages, platforms, and standards for data management, and prepare short technical assessments for IDSS leadership.
- Training and documentation. Train laboratory and analytical staff, and write standard operating procedures and technical documentation.
Preferred Qualifications- Experience deploying or customizing Invenio/InvenioRDM or similar repositories (Dataverse, DSpace, CKAN).
- Experience integrating ML/AI components into applications, such as embeddings and vector search, LLM-based retrieval, or document parsing.
- Familiarity with workflow tools (Airflow, Nextflow, Snakemake, Argo) and with Helm, Terraform, or similar infrastructure-as-code tools.
- Experience with stem cell or tissue engineering laboratories.
- Experience with imaging and omics data.
- Experience with high-performance computing environments (for example NIH Biowulf).
- Experience with front-end tooling (React or similar) for data-facing interfaces.
Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.
This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate's experience, qualifications, skills, and location.
Salary Range
$105,000-$150,000 USD
