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Research Data Manager

Pay
USD 105,000–150,000/yearAnnual period assumed — pay source
Salary Range $105,000—$150,000 USD
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Work setting
Remote — work setting source
Listed location: Remote
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Employment
Not stated

What you’ll work on

Full posting
  • Build automated data ingestion pipelines into the LIMS with validation and error handling

  • Convert and harmonize instrument, imaging, and omics data into standard schemas

  • Deploy and support Invenio and manage records, metadata schemas, and access controls

  • Design and maintain experimental metadata databases

  • Add AI/ML features such as semantic search and metadata extraction to data tools

From the employer’s posting
Build and maintain automated ingestion pipelines with validation, logging, and error handling.
convert, normalize, and harmonize heterogeneous data formats
Deploy, configure, and support document and research data management systems such as Invenio.
Design, create, and maintain databases for experimental metadata and small-to-moderate datasets
Incorporate ML/AI into data workflows and user interfaces, such as semantic search, metadata extraction and auto-annotation

What you’ll bring

All qualifications

Core experience

  • Bachelor’s degree in computer science, bioinformatics, data science, or a related field, plus five or more years of relevant experience.
  • Proficiency in Python, including data libraries (pandas and similar) and API development, plus SQL and experience with relational databases (for example PostgreSQL).
  • Experience writing production-quality scripts and services for data conversion, ETL/ELT, and system integration.
  • Hands-on experience with Docker and Kubernetes, and with at least one major cloud platform (AWS, Azure, or GCP).
  • Experience with Git, CI/CD, and practices such as testing and code review.
  • Experience with LIMS, ELN, or comparable research data systems in a biological or biotechnology laboratory setting.

Preferred experience

  • Experience with NoSQL or search technologies (for example Elasticsearch/OpenSearch) is a plus.
  • 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.
Qualification wording
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.
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.
Education & alternatives
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.
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Posting record

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Status in our records
Live
First seen by us
Oct 24, 202614 days ago
Recorded sightings
20
Last seen by us
Nov 7, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 14

Similar jobs usually come down between day 6 and day 58.

At Axle, jobs usually come down by about day 52.

From 959 similar postings that came down, and 10 of Axle’s. Coming down is not always a hire.

Axle is hiring

10 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring is picking up
  • Takes jobs down at a usual pace
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • AWS
  • Azure
  • Docker
  • Elasticsearch
  • GCP
  • Kubernetes
  • NoSQL
  • PostgreSQL
  • Terraform
  • Airflow
  • pandas
  • React
Where the posting names them
- 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.
- 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.
- 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.
- 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 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.

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Demand for Data Governance roles

New Data Governance postings at the employers we follow are up 175% over the last 5 weeks compared with the 5 before.

Weekly new Data Governance postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 64, the most 443, and the latest week had 443.
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About the role

We file this job under Data Governance, by the work the posting describes.

Data governance staff keep a company's data accurate, documented and used within the rules. They agree what key fields mean, set up quality checks, track who owns each dataset and look after master data.

Postings ask for SQL, a governance tool such as Collibra or Informatica, knowledge of privacy and data rules, and a lot of work with other teams.

A data engineer builds the systems that move data; governance staff set and check the standards that data has to meet.

Job description

View original posting ↗

(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, 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.

The diversity of Axle’s employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.

Accessibility: If you need an accommodation as part of the employment process please contact: careers@axleinfo.com

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
Source details

View original posting ↗

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Pay
Salary Range $105,000—$150,000 USD
Location & working pattern

Remote

Working pattern and location restrictions need checking in the full posting.

Work authorization
- 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

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