diff --git a/src/preset_cli/cli/superset/sync/dbt/command.py b/src/preset_cli/cli/superset/sync/dbt/command.py
index af227497..f0993503 100644
--- a/src/preset_cli/cli/superset/sync/dbt/command.py
+++ b/src/preset_cli/cli/superset/sync/dbt/command.py
@@ -6,7 +6,7 @@
 import sys
 import warnings
 from pathlib import Path
-from typing import List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import click
 import yaml
@@ -335,6 +335,43 @@ def get_job(
     raise ValueError(f"Job {job_id} not available")
 
 
+def process_sl_metrics(
+    dbt_client: DBTClient,
+    environment_id: int,
+    model_map: Dict[ModelKey, ModelSchema],
+) -> Optional[List[MFMetricWithSQLSchema]]:
+    """
+    Fetch metrics from the semantic layer and return the ones we can map to models.
+    """
+    dialect = dbt_client.get_sl_dialect(environment_id)
+    mf_metric_schema = MFMetricWithSQLSchema()
+    sl_metrics: List[MFMetricWithSQLSchema] = []
+    for metric in dbt_client.get_sl_metrics(environment_id):
+        sql = dbt_client.get_sl_metric_sql(metric["name"], environment_id)
+        if sql is None:
+            continue
+
+        try:
+            model = get_model_from_sql(sql, dialect, model_map)
+        except MultipleModelsError:
+            continue
+
+        sl_metrics.append(
+            mf_metric_schema.load(
+                {
+                    "name": metric["name"],
+                    "type": metric["type"],
+                    "description": metric["description"],
+                    "sql": sql,
+                    "dialect": dialect.value,
+                    "model": model["unique_id"],
+                },
+            ),
+        )
+
+    return sl_metrics
+
+
 @click.command()
 @click.argument("token")
 @click.argument("job_id", type=click.INT, required=False, default=None)
@@ -448,34 +485,8 @@ def dbt_cloud(  # pylint: disable=too-many-arguments, too-many-locals
     models = apply_select(models, select, exclude)
     model_map = {ModelKey(model["schema"], model["name"]): model for model in models}
 
-    # original dbt <= 1.6 metrics
     og_metrics = dbt_client.get_og_metrics(job["id"])
-
-    # MetricFlow metrics
-    dialect = dbt_client.get_sl_dialect(job["environment_id"])
-    mf_metric_schema = MFMetricWithSQLSchema()
-    sl_metrics: List[MFMetricWithSQLSchema] = []
-    for metric in dbt_client.get_sl_metrics(job["environment_id"]):
-        sql = dbt_client.get_sl_metric_sql(metric["name"], job["environment_id"])
-        if sql is not None:
-            try:
-                model = get_model_from_sql(sql, dialect, model_map)
-            except MultipleModelsError:
-                continue
-
-            sl_metrics.append(
-                mf_metric_schema.load(
-                    {
-                        "name": metric["name"],
-                        "type": metric["type"],
-                        "description": metric["description"],
-                        "sql": sql,
-                        "dialect": dialect.value,
-                        "model": model["unique_id"],
-                    },
-                ),
-            )
-
+    sl_metrics = process_sl_metrics(dbt_client, job["environment_id"], model_map)
     superset_metrics = get_superset_metrics_per_model(og_metrics, sl_metrics)
 
     if exposures_only: