Professional learning path

Data Engineer

Publish a feature dataset that downstream analysts and model builders can trace and reuse.

Prepare

Prepare a trusted reconciliation dataset

Run the reconciliation kit. Inspect identifiers, currency, and unmatched rows. Define the grain of the result and explain why a currency mismatch must remain an exception even when amounts are equal.

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.

Open the interactive demo Download the practice kit

Practice tasks

0 / 4 completed

Record the source key and the grain of each table.Your working notes

Explain the missing settlement and currency mismatch in the output.Your working notes

Change the amount tolerance and document which result changes.Your working notes

Publish the definition of a matched record with its limitations.Your working notes

Notes and progress are saved on this device.

Apply in your workspace

Build once. Deliver trusted data.

Publish a feature dataset that downstream analysts and model builders can trace and reuse.

Choose a source and confirm its owner, access scope, schema, and expected freshness before creating the pipeline.

Build the transformation in the visual workflow or in code. Keep the dataset version and schema artifact with the run.

Check the output and record upstream dataset and feature-group references. Coordinate with consumers before changing a shared field.

Publish the output and give the next team the run reference, lineage, expected refresh schedule, and recovery instructions.

Expected handoff

A versioned dataset, its schema, and a traceable run reference.

Explore your platform capabilities

Measure your progress

Time from a source change to a validated publication
Rework caused by missing contracts or unclear ownership

Use AI to draft transformation logic or explain a failure, then review the code and validate the output before release.