Featured Work
3 activeCivil Knowledge Hub
Division-level SharePoint hub centralizing SOPs, onboarding materials, training resources, FAQs, forms, and daily reference content for DeKalb County Superior Court staff.
Podio ATS Transformation
End-to-end Product Owner for a custom Podio-based applicant tracking system reducing manual processing ~50% and delivering Power BI recruiting dashboards.
Judicial Operations Modernization
Multi-year digital workflow modernization across Tyler Technologies eFile and Judicial Suite — supporting eFiling, digital docket, hearings, and caseflow operations.
Growth Roadmap
3 in progressMS-IT Cybersecurity
Master of Science in Information Technology with a Cybersecurity concentration at Middle Georgia State University.
PSPO I
Professional Scrum Product Owner I — formalizing Agile product ownership practice with Scrum.org certification.
Microsoft SC-900
Microsoft Security, Compliance, and Identity Fundamentals — building cybersecurity foundation alongside MS-IT degree.
Product Roadmap
Civil Knowledge Hub · 2026SharePoint Knowledge Hub — DeKalb County Superior Court
Civil Division · Product Owner: RJ JacksonCase Studies
SharePoint Knowledge Hub — DeKalb County Superior Court
Product Owner · SharePoint Engineer · Civil Division · 2026
Staff relied on scattered email chains, paper binders, and tribal knowledge for SOPs, onboarding, and daily procedures — causing delays and inconsistency.
Clerks, judicial officers, IT staff, division leadership, and external attorneys filing through Tyler eFile.
Centralized SOPs, searchable forms library, onboarding hub, training resources, quick links, FAQ — governed by content owners.
Phased delivery: Discovery → Architecture → Build → Training → Adoption. MVP-first, expanding to Phase 2 post-adoption.
SharePoint Knowledge Hub on Microsoft 365 — structured navigation, document libraries, metadata taxonomy, permissions model, and training plan.
Centralized 3+ fragmented systems. Reduced onboarding friction. Training materials cut ramp time for new clerks.
Phasing delivery by MVP kept scope manageable and built trust with stakeholders before expanding. Stakeholder interviews upfront prevented major rework.
Content governance should be defined in Discovery, not after Build. Waiting created ownership ambiguity during content migration.
Chose speed-to-MVP over full metadata taxonomy at launch. Got the hub live faster, but required a second taxonomy pass post-adoption.
Leadership and end-users had different definitions of "done." Aligning success criteria explicitly before build would have reduced post-launch scope creep.
AI accelerated quick-start guide drafting, case study structure, and training outline by ~60%. In future sprints, AI could auto-generate metadata suggestions from existing document names.
Podio ATS Platform — Eastern Personnel Services
Product Owner · Project Manager · 2017–2019
Manual applicant intake created duplicate data, lost candidates, and gave recruiters zero pipeline visibility. No source of truth across recruiting, HR, and payroll.
Recruiters, HR team, payroll, and company leadership needing real-time hiring metrics and accurate candidate records.
Structured candidate intake, pipeline tracking, recruiter dashboards, payroll integration, and elimination of manual data entry.
Custom Podio-based ATS with structured data capture at intake, automated workflow rules, and Power BI dashboards for performance reporting.
~50% reduction in manual processing time. Power BI dashboards tracking recruiter performance, hiring velocity, and payroll accuracy.
Before AI tools existed at scale — but the structured data modeling approach used here maps directly to how AI-augmented workflows are built today.
Capturing structured data at the point of intake — rather than cleaning it downstream — eliminated the root cause of most manual work. Getting this right early created compounding value.
User adoption required more change management than anticipated. Would build recruiter training into the rollout plan from day one, not as an afterthought.
Built a custom Podio solution instead of buying an off-the-shelf ATS. Higher build cost upfront, but perfect fit for the workflow. The right call given the unique intake process.
Recruiters and payroll had conflicting data needs. Resolved by building separate views of the same data model — one lesson I now apply at requirements gathering, not after.
If rebuilt today, AI could auto-screen resumes against job criteria at intake, flag duplicates, and generate weekly pipeline summaries — turning a workflow tool into an intelligent recruiting assistant.
How I Think
Product Owner MindsetRequirements Discovery
I start by separating the stated request from the actual business problem. Before building, I clarify users, workflows, pain points, constraints, and success criteria.
Example: Civil Knowledge Hub — separated "we need a SharePoint site" from the real issue: scattered SOPs, inconsistent onboarding, and tribal knowledge.
- Who is the actual user — not who requested the feature?
- What workflow breaks down today and why?
- What does "done" look like to each stakeholder?
- What constraints exist — technical, political, or procedural?
- What does success look like in 30, 60, 90 days?
Prioritization
I use MVP thinking to move the highest-value work first, reduce noise, and avoid overbuilding. The goal is not to build everything — it is to ship what changes outcomes.
Example: Civil Knowledge Hub — shipped the core MVP first instead of waiting for every metadata and governance feature to be perfect.
- What is the minimum that delivers real value to users?
- What can wait without blocking adoption?
- What has the highest impact-to-effort ratio?
- What do users ask for vs. what do they actually need?
- What is the cost of delaying this item vs. the cost of doing it now?
Stakeholder Alignment
I translate between leadership goals, end-user needs, and technical execution. My role is to make sure the right people understand the right details at the right time.
Example: Court technology work — translated leadership goals, clerk workflows, and IT constraints into practical rollout steps.
- Who has decision authority vs. who has influence vs. who is impacted?
- What does each stakeholder need to feel confident?
- Where do goals conflict — and how do I surface that early?
- Am I communicating progress in the language each audience uses?
- Who is not in the room who should be?
Risk & Tradeoffs
I evaluate scope, adoption, permissions, data sensitivity, user behavior, and support burden before recommending a solution. Good delivery requires knowing what not to build yet.
Example: Civil Knowledge Hub — balanced speed-to-launch against permissions, content ownership, and long-term governance.
- What breaks if this goes wrong — and how bad is that?
- What is the support burden after launch?
- Are there permission or data sensitivity issues to resolve first?
- What behavior change is required from users, and is that realistic?
- What am I choosing not to build — and why is that the right call now?
Retrospectives
After delivery, I look at what worked, what created friction, what users actually adopted, and what I would change in the next iteration.
Example: Podio ATS — learned that adoption and training need to be planned as part of the product, not added after launch.
- What did users actually adopt vs. what did we build?
- What created the most friction post-launch?
- What assumption turned out to be wrong?
- What would I do differently in the next sprint?
- What did this project teach me about the next one?
PO Retrospective Framework
Applied to every deliveryAfter every project or sprint, I run through these five questions. They keep me honest, sharpen my thinking for the next initiative, and force me to examine both the process and the product outcome — not just whether we shipped on time.
Impact Metrics
Active Backlog
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Experience
Education & Certifications
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