Lead Data Curation Architect for AI-Ready Research Data

Johns Hopkins Applied Physics Lab; Laurel (MD); On-site; USD 105,000 - 290,000; Full time

Job summary

Johns Hopkins Applied Physics Laboratory (APL) seeks a Principal Data Curation Architect to lead data-management initiatives in a classified IT environment in Laurel, MD. You will design and launch end-to-end data pipelines and governance for decades of datasets.

Work with data owners, IT, and security leadership to ensure data is discoverable, well-documented, and AI/ML-ready, applying FAIR principles and modern metadata practices for sustainable research.

Qualifications

• Bachelor's degree in library/information science, data science, or related field.
• At least 2 years leading technology projects or data-management initiatives.
• Experience curating large structured datasets with quality assessment and documentation.
• Ability to obtain a Secret security clearance; U.S. citizenship required.

Responsibilities

• Lead projects to establish data-management and curation capabilities, including scope and budgets.
• Develop and manage end-to-end data pipelines and lifecycle workflows.
• Inventory and assess legacy and active research data with owners.
• Create and implement data governance processes and standard operating procedures.
• Train data owners and consumers on data curation for AI/ML readiness.
• Drive evaluation and implementation of data catalogs and metadata tools.

Skills

Data governance

Data curation

Project management

Stakeholder engagement

FAIR data principles

Requirements gathering

Communication

Education

Bachelor's degree in library/information science, data science, scientific research or a related field

Tools

SQL

Databases

ETL tools

Job description

Johns Hopkins Applied Physics Laboratory (APL) seeks a Principal Data Curation Architect to lead data-management initiatives in a classified IT environment in Laurel, MD. You will design and launch end-to-end data pipelines and governance for decades of datasets.

Work with data owners, IT, and security leadership to ensure data is discoverable, well-documented, and AI/ML-ready, applying FAIR principles and modern metadata practices for sustainable research.