Research Data Manager

(ID: 2026-3882)

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:

• 100% Medical, Dental & Vision Coverage for Employees
• 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

• Education and experience. Bachelor’s degree in computer science, bioinformatics, data science, or a related field, plus five or more years of relevant experience. A master’s degree may substitute for two years of experience.
• Programming and databases. Proficiency in Python, including data libraries (pandas and similar) and API development, plus SQL and experience with relational databases (for example PostgreSQL). Experience with NoSQL or search technologies (for example Elasticsearch/OpenSearch) is a plus.
• Data engineering. Experience writing production-quality scripts and services for data conversion, ETL/ELT, and system integration.
• Containers and cloud. Hands-on experience with Docker and Kubernetes, and with at least one major cloud platform (AWS, Azure, or GCP).
• Software engineering practices. Experience with Git, CI/CD, and practices such as testing and code review.
• Research systems. Experience with LIMS, ELN, or comparable research data systems in a biological or biotechnology laboratory setting.
• Data standards. Working knowledge of metadata standards, data modeling, and FAIR data principles.
• Communication. Strong communication skills and the ability to train and support scientific staff.
• Clearance. Ability to obtain and maintain NIH/HHS security clearance and meet federal contractor suitability requirements.

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, …