Research Data Manager

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Benefits We

Offer:

• Paid Time Off and Paid Holidays
• 401K match up to 5%
• Educational Benefits for Career Growth
• Employee Referral Bonus
• Flexible Spending Accounts:
  • Healthcare (FSA)
  • Parking Reimbursement Account (PRK)
  • Dependent Care Assistant Program (DCAP)
  • Transportation Reimbursement Account (TRN)

Position Overview

The 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…