Technical Product Owner, HRS SF424

SF424 Forms, Grants.gov & Research Administration Domain Support

Expectation: Build enough SF424, Grants.gov, and research administration knowledge to support accurate product analysis and informed decision-making.

• Develop strong working knowledge of SF-424, SF-424 R&R, mandatory, individual, short organization, budget, subaward, and agency-specific form families used in federal grant application packages.
• Understand how HRS Grants supports proposal development, institutional routing and approvals, sponsor opportunity packages, Grants.gov submissions, attachments, budgets, subawards, certifications, and related research administration workflows.
• Analyze federal form instructions, business rules, form versions, schema changes, agency-specific requirements, and customer workflow expectations to determine product and implementation impact.
• Support the Product Owner and subject matter experts by preparing structured analysis, surfacing decision points, documenting rationale, and reducing dependency on a single product lead for detailed operational product support.
• Pay particular attention to complex budget-related forms and subaward forms, where calculations, repeats, attachments, cross-form dependencies, and HRS implementation logic may require deeper manual analysis and validation.

Requirements, Mapping, Validation & Engineering Readiness

Expectation: Turn SF424 source materials, mappings, and technical inputs into clear, validated requirements that engineering and QA can act on.

• Create and validate SF424 requirements workbooks from Grants.gov instructions, schemas, HRS Grants logic, field-level rules, and product priorities.
• Analyze key form details, including field definitions, versions, display and required rules, validation rules, list values, help text, repeatable sections, attachments, and budget logic.
• Review generated workbooks, scorecards, mappings, and inferencing outputs to identify gaps, incorrect assumptions, rule issues, and missing requirements.
• Translate ambiguous input into clear requirements, assumptions, open questions, decisions, and follow-up actions.
• Validate mappings between Grants.gov fields, XML/XSD schemas, HRS Grants data, configuration, source-system behavior, and expected form output.
• Partner with engineering, architecture, and QA to interpret requirements in the context of forms, schemas, integrations, data models, rules, attachments, and submission workflows.
• Prepare clean engineering handoff materials with validated requirements, assumptions, risks, test considerations, implementation notes, and unresolved questions.
• Support QA and post-build validation for form generation, validation, attachments, submission scenarios, regression coverage, UAT, defect triage, and acceptance.
• Balance business needs, technical constraints, data availability, Grants.gov compliance, implementation complexity, and release risk when recommending next steps.

Prototyping & Future-State SF424 Experience

Expectation: Use prototyping, AI-assisted discovery, and technical analysis to help redefine how SF424 supports a lower-friction path from funding discovery through integration, submission, and award.

• Explore future-state SF424 workflows that reduce friction across funding opportunity search, proposal preparation, form population, validation, submission, status tracking, and award transition.
• Use AI-assisted analysis to identify new ways to integrate with Grants.gov, agency systems, sponsor data sources, funding opportunity feeds, research administration systems, and related pre-award or post-award workflows.
• Create lightweight prototypes, workflow concepts, data-flow sketches, prompts, test cases, proof-of-concept artifacts, or structured experiments that help the product team evaluate new SF424 integration and automation approaches.
• Assess how emerging capabilities could improve opportunity discovery, data reuse, institutional approvals, sponsor-specific requirements, attachment preparation, validation confidence, submission readiness, award setup, and downstream handoffs.
• Translate prototype findings into practical recommendations, risks, assumptions, product options, and candidate backlog items that Product Management, the Product Owner, Engineering, and Services can evaluate for roadmap consideration.

Collaboration with Product Team

Expectation: Work closely with Product Management, the Product Owner, Engineering, QA, and Services to align decisions, dependencies, and delivery execution.

• Partner closely with Grants Product Management, the HRS SF424 Product Owner, engineering, QA, and Services to clarify requirements, align delivery expectations, and support product execution across SF424 forms and form waves.
• Coordinate with the Product Owner and Product Management to document decisions, dependencies, blockers, open questions, validation status, ownership, due dates, and follow-up actions.
• Work with other Product Owners and Product Managers to keep the system of record in Azure DevOps well-groomed, prioritized, current, and aligned with Huron’s SDLC process.
• Work with engineering and QA to ensure requirements, mappings, validation notes, test considerations, and handoff materials are clear, complete, and actionable.
• Collaborate with Services to understand implementation considerations, customer-facing impacts, UAT needs, and practical delivery constraints that may affect SF424 readiness or rollout.
• Communicate complex functional and technical findings clearly within the product team, including concise summaries, options, recommendations, risks, and escalation points.

Documentation, Knowledge Transfer & Process Improvement

Expectation: Create durable documentation, repeatable processes, and knowledge-transfer materials that improve continuity and execution quality.

• Document SF424 repository structure, form analysis approach, workbook creation steps, validation expectations, handoff process, generated artifact usage, known issues, and repeatable review patterns.
• Create and maintain templates, checklists, review guides, traceability notes, and process documentation that improve consistency and reduce reliance on informal knowledge transfer.
• Identify opportunities to improve requirements quality, validation throughput, generated output accuracy