Professional learning path

Analytics Engineer

Publish a model-health metric that analysts can reuse across dashboards and business reviews.

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

Make trusted metrics reusable.

Publish a model-health metric that analysts can reuse across dashboards and business reviews.

Confirm that source tables are available and identify the grain, tenant scope, and freshness of each input.

Build a reusable analytical model and document joins, filters, and metric definitions.

Validate totals and edge cases against source records. Check the attribution of model and inference costs.

Publish the model and its definitions so analysts and BI developers share the same calculation.

Expected handoff

A tested analytical model with metric definitions and source references.

Explore your platform capabilities

Measure your progress

Number of reports reusing an approved metric
Time spent reconciling conflicting calculations

Use AI to draft SQL or explain a transformation; validate joins, filters, and tenant boundaries before publishing.