Analyst - Defense Acquisition
New
O
ObviantDefense acquisition data
Workplace: RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years in defense acquisition, budgeting, or contracting
- Required Skills
- Data AnalysisMicrosoft Excel
Requirements
- Have 6+ years of experience in defense acquisition, budgeting, or contracting, such as in a program office, comptroller or financial management, contracting, or PPBE role.
- Bring complete working knowledge of the defense acquisition system, from requirements such as JCIDS through funding such as PPBE and management such as DAS.
- Understand appropriations and colors of money, program elements and budget line items, justification exhibits (R-1, P-1, J-books), and the movement of funds from request to obligation.
- Understand contracting vehicles, contract types, and how awards and modifications are made and reported.
- Have deep familiarity with DoW data systems of record behind acquisition, budget, contracts, or related domains, including their omissions, reporting lags, and conflicts.
- Be comfortable working hands-on with large, messy datasets in Excel or similar tools.
- Explain complex concepts plainly to engineers, customers, and executives.
- Work effectively with ambiguity and incomplete data, and judge what is and is not good enough.
- Document knowledge clearly so others can use it.
- DAWIA/DAU certification in Program Management, Contracting, or Business – Financial Management is a plus.
- Experience at DoD/DoW, a military service, DIU, a defense-focused consultancy, or an FFRDC is a plus.
- Familiarity with LLMs, retrieval, or evaluating AI-generated answers; SQL or Python experience; and an active security clearance are nice to have.
Responsibilities
- Document what each defense data system contains, omits, and when its data is reported.
- Explain reporting lags, coverage gaps, omitted or restricted data, inconsistent identifiers, and disagreements between official sources.
- Connect budget requests to funded programs, executing contracts, and the organizations that manage them.
- Flag data and results that do not match how acquisition, budgeting, or contracting work.
- Advise product and engineering teams on how sources should be interpreted and presented.
- Write test questions and review model-generated answers for AI features.
- Join customer conversations to explain data sources and caveats.
- Capture and encode subject-matter knowledge to improve systems.
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