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Joel Parkinson
Data Processing & Quality Assurance Specialist
Client trust isn’t a vibe — it’s a process.
At Rune Digital, I use AI to accelerate delivery, not replace accountability.
• Client data is isolated to each project
• Data is never reused or trained across clients
• All outputs are human-reviewed and signed off
This is documented, enforced, and non-negotiable — especially for data cleaning, QA, and validation work.
If accuracy matters, the process matters.
Full QA & trust policy available as part of delivery documentation.
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I’ve started offering a Data Cleaning & Quality Assurance service focused on getting datasets delivery-ready before analysis or reporting.
My process covers:
• Schema & structural integrity
• Formatting and normalization
• Cross-field consistency checks
• Outlier and null handling
• Final delivery verification
This is designed for teams who need confidence, not guesswork, before data moves downstream.
I’ve documented the full QA checklist and workflow in my profile for anyone curious.
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A documented quality and safety framework.
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Documented pre-delivery QA checklist for data processing work.
This checklist reflects the process I use to validate datasets before handoff, including:
- Structural integrity checks (keys, schema, required fields)
- Formatting and normalization
- Accuracy and consistency review
- Final delivery readiness and manual QA
All deliverables are reviewed against this checklist prior to delivery to ensure accuracy, consistency, and reliability.
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