diff --git a/content/docs/2.13/operate/opentelemetry.md b/content/docs/2.13/operate/opentelemetry.md
index 12a050b20..9696e75ec 100644
--- a/content/docs/2.13/operate/opentelemetry.md
+++ b/content/docs/2.13/operate/opentelemetry.md
@@ -40,6 +40,7 @@ The following metrics are being gathered:
 | `keda.scaler.errors` | The number of errors that have occurred for each scaler. |
 | `keda.scaler.errors.total` | The total number of errors encountered for all scalers. |
 | `keda.scaled.object.errors` | The number of errors that have occurred for each ScaledObject. |
+| `keda.scaled.job.errors` | The number of errors that have occurred for each ScaledJob. |
 | `keda.resource.totals` | Total number of KEDA custom resources per namespace for each custom resource type (CRD). |
 | `keda.trigger.totals` | Total number of triggers per trigger type. |
 | `keda.internal.scale.loop.latency` | Total deviation (in milliseconds) between the expected execution time and the actual execution time for the scaling loop. This latency could be produced due to accumulated scalers latencies or high load. This is an internal metric. |
diff --git a/content/docs/2.13/operate/prometheus.md b/content/docs/2.13/operate/prometheus.md
index a591235d1..0e99e4c34 100644
--- a/content/docs/2.13/operate/prometheus.md
+++ b/content/docs/2.13/operate/prometheus.md
@@ -18,6 +18,7 @@ The KEDA Operator exposes Prometheus metrics which can be scraped on port `8080`
 - `keda_scaler_errors` - The number of errors that have occurred for each scaler.
 - `keda_scaler_errors_total` - The total number of errors encountered for all scalers.
 - `keda_scaled_object_errors` - The number of errors that have occurred for each ScaledObject.
+- `keda_scaled_job_errors` - The number of errors that have occurred for each ScaledJob.
 - `keda_resource_totals` - Total number of KEDA custom resources per namespace for each custom resource type (CRD).
 - `keda_trigger_totals` - Total number of triggers per trigger type.
 - `keda_internal_scale_loop_latency` - Total deviation (in milliseconds) between the expected execution time and the actual execution time for the scaling loop. This latency could be produced due to accumulated scalers latencies or high load. This is an internal metric.
@@ -52,5 +53,6 @@ On top, the dashboard supports the following variables:
 - datasource
 - namespace
 - scaledObject
+- scaledJob
 - scaler
 - metric