Data Quality Engineer
Define and review quality evidence for a dataset or model input.
Design a recoverable reconciliation run
Run the reconciliation kit, then repeat it with the same inputs. Define how the production workflow should handle retries, late settlements, monitoring, and a failed downstream handoff.
Start with the browser demo or download the local kit. For the workspace exercise, you need approved access to the relevant Genedata capabilities and a reviewer for your output.
Practice tasks
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Notes and progress are saved on this device.
Catch problems before the handoff.
Define and review quality evidence for a dataset or model input.
Agree the critical fields, expected distributions, and downstream acceptance criteria with the owner.
Create repeatable validation checks and record the baseline or evaluation set used.
Run the checks before publication or promotion and investigate material differences with engineering.
Share the result, exceptions, and accountable owner; refresh the baseline only after reviewing the reason for change.
Expected handoff
A quality assessment with checks, baseline references, exceptions, and ownership.
Explore your platform capabilitiesMeasure your progress
- Quality issues found before publication
- Time to resolve an issue with the responsible owner
Use AI to propose edge cases and explain failed checks; have domain owners approve what counts as acceptable data.