Strategic Open Source Data Fabric Advisor

Apogee Engineering, LLC; Bath Township (OH); On-site; USD 120,000 - 170,000; Full time

Job summary

Apogee Engineering, LLC is seeking an Open Source Data Fabric Advisor to support the Open Source Portal (OSP), unHIDM, and broader DoW/IC data-access missions. The role requires TS/SCI clearance and is contingent upon contract award, located on-site at Wright Patterson AFB, Dayton, OH.

You will work with Data Scientists, IT teams, and stakeholders to translate requirements into scalable data solutions, improve data capture, and design efficient data-access workflows using data fabrics and lakes.

Qualifications

• Meets Bachelor’s degree in science or engineering.
• 8 years experience with DoW or similar programs.
• Strong understanding of OSINT and multi-source data integration.
• Strong data background and knowledge of on-prem/off-prem cloud.
• Experience with data fabrics, data lakes, or object-based data architectures.

Responsibilities

• Provide advisory support to OSP and unHIDM.
• Work closely with Data Scientists, IT teams, and mission stakeholders to translate requirements into technical solutions.
• Develop plans to improve unHIDM captures and automation of human expertise to effectively exploit Big Data.
• Assist in design and optimization of data-access workflows and data fabrics.
• Coordinate with stakeholders to ensure capabilities meet operational and analytic requirements.

Skills

OSINT

Data integration

Cloud concepts

Education

Bachelor's degree in science or engineering

Tools

Data fabrics

Data lakes

Object-based data architectures

Job description

Apogee Engineering, LLC is seeking an Open Source Data Fabric Advisor to support the Open Source Portal (OSP), unHIDM, and broader DoW/IC data-access missions. The role requires TS/SCI clearance and is contingent upon contract award, located on-site at Wright Patterson AFB, Dayton, OH.

You will work with Data Scientists, IT teams, and stakeholders to translate requirements into scalable data solutions, improve data capture, and design efficient data-access workflows using data fabrics and lakes.