diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 0ca2b5b1..e3cc746d 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -80,7 +80,7 @@ jobs:
           WG: cumulus
           DB: cumulus_library_regression_db
         run: |
-          cumulus-library build -t core --profile $PROFILE --workgroup $WG --database $DB
+          cumulus-library build -t core --profile $PROFILE --workgroup $WG --database $DB --verbose
           cumulus-library export -t core ./tests/regression/data_export/ --profile $PROFILE --workgroup $WG --database $DB
       - name: Compare vs known data
         run: python ./tests/regression/run_regression.py
diff --git a/cumulus_library/databases.py b/cumulus_library/databases.py
index 789d415d..368de1a1 100644
--- a/cumulus_library/databases.py
+++ b/cumulus_library/databases.py
@@ -44,7 +44,9 @@ def fetchall(self) -> list[list] | None:
 class DatabaseParser(abc.ABC):
     """Parses information_schema results from a database"""
 
-    def _parse_found_schema(self, expected: dict[dict[list]], schema: list[list]):
+    def _parse_found_schema(
+        self, expected: dict[dict[list]], schema: dict[list]
+    ) -> dict:
         """Checks for presence of field for each column in a table
 
         :param expected: A nested dict describing the expected data format of
@@ -64,36 +66,33 @@ def _parse_found_schema(self, expected: dict[dict[list]], schema: list[list]):
         is a first pass, ignoring complexities of differing database variable
         types, just iterating through looking for column names.
 
-
-        TODO: on a per database instance, consider a more nuanced approach
-        if needed
+        TODO: on a per database instance, consider a more nuanced approach if needed
+              (compared to just checking if the schema contains the field name)
         """
         output = {}
-        for column, _ in expected.items():
-            output[column] = {}
-            if col_schema := schema[column.lower()]:
-                # is this an object column?
-                if len(expected[column]) > 0:
-                    for field in expected[column]:
-                        col_schema = col_schema.split(field, 1)
-                        if len(col_schema) != 2:
-                            output[column][field] = False
-                            col_schema = col_schema[0]
-                        else:
-                            output[column][field] = True
-                            col_schema = col_schema[1]
-                # otherwise this is a primitive col
-                else:
-                    output[column] = True
+
+        for column, fields in expected.items():
+            column_lower = column.lower()
+
+            # is this an object column? (like: "subject": ["reference"])
+            if fields:
+                col_schema = schema.get(column_lower, "").lower()
+                output[column] = {
+                    # TODO: make this check more robust
+                    field: field.lower() in col_schema
+                    for field in fields
+                }
+
+            # otherwise this is a primitive col (like: "recordedDate": None)
             else:
-                for field in expected[column]:
-                    output[column][field] = False
+                output[column] = column_lower in schema
+
         return output
 
     @abc.abstractmethod
     def validate_table_schema(
-        self, expected: dict[dict[list]], schema: list[tuple]
-    ) -> dict[bool]:
+        self, expected: dict[str, list[str]], schema: list[tuple]
+    ) -> dict:
         """Public interface for investigating if fields are in a table schema.
 
         This method should lightly format results and pass them to
@@ -182,7 +181,7 @@ def close(self) -> None:
 class AthenaParser(DatabaseParser):
     def validate_table_schema(
         self, expected: dict[dict[list]], schema: list[list]
-    ) -> bool:
+    ) -> dict:
         schema = dict(schema)
         return self._parse_found_schema(expected, schema)
 
@@ -291,6 +290,8 @@ def _compat_from_iso8601_timestamp(
             else:
                 return datetime.datetime(int(pieces[0]), int(pieces[1]), 1)
 
+        # TODO: return timezone-aware datetimes, like Athena does
+        #       (this currently generates naive datetimes, in UTC local time)
         return datetime.datetime.fromisoformat(value)
 
     def cursor(self) -> duckdb.DuckDBPyConnection:
diff --git a/cumulus_library/template_sql/column_datatype.sql.jinja b/cumulus_library/template_sql/column_datatype.sql.jinja
index 334def33..b3c568aa 100644
--- a/cumulus_library/template_sql/column_datatype.sql.jinja
+++ b/cumulus_library/template_sql/column_datatype.sql.jinja
@@ -4,5 +4,5 @@ SELECT
 FROM information_schema.columns
 WHERE
     table_schema = '{{ schema_name }}'
-    AND table_name = '{{ table_name }}'
-    AND LOWER(column_name) IN ('{{ column_names|join("', '") }}') --noqa: LT05
+    AND table_name = '{{ table_name|lower }}'
+    AND LOWER(column_name) IN ('{{ column_names|join("', '")|lower }}') --noqa: LT05
diff --git a/tests/test_data/core/core__encounter.txt b/tests/test_data/core/core__encounter.txt
index a2c34dec..a4871e67 100644
--- a/tests/test_data/core/core__encounter.txt
+++ b/tests/test_data/core/core__encounter.txt
@@ -21,7 +21,7 @@
 ('75312bd2-d5ac-c62e-c9df-0004783725c7', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, None, None, 11, datetime.date(2018, 7, 2), datetime.date(2018, 7, 2), datetime.date(2018, 7, 2), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/3ae095ec-8fe0-133b-36d4-8785a6ad8df3', 'Encounter/75312bd2-d5ac-c62e-c9df-0004783725c7', 'female', 'white', 'not hispanic or latino', '662')
 ('75b68644-535d-bdc1-4c31-aa9fe7e1822f', {'id': None, 'code': 'EMER', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'EMER', 'emergency', 'finished', None, None, None, None, None, None, '55680006', 'http://snomed.info/sct', 25, datetime.date(2018, 6, 30), datetime.date(2018, 6, 30), datetime.date(2018, 6, 25), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/82b8a670-4700-30e8-24a0-b83efa3c5e0a', 'Encounter/75b68644-535d-bdc1-4c31-aa9fe7e1822f', 'female', 'white', 'not hispanic or latino', '672')
 ('79d8f213-7847-646b-8a66-5da208cc1c27', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, '431857002', 'http://snomed.info/sct', 77, datetime.date(2018, 7, 14), datetime.date(2018, 7, 14), datetime.date(2018, 7, 9), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/26a3984f-b2a8-e67f-7abc-ff147a0e6e35', 'Encounter/79d8f213-7847-646b-8a66-5da208cc1c27', 'male', 'white', 'not hispanic or latino', '674')
-('83d0d564-3bbf-48eb-7445-bd2b81130671', {'id': None, 'code': 'EMER', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'EMER', 'emergency', 'finished', None, None, None, None, None, None, '55680006', 'http://snomed.info/sct', 69, datetime.date(2018, 6, 16), datetime.date(2018, 6, 16), datetime.date(2018, 6, 11), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/16be855b-ece2-8b96-1ef9-a4d93adf3289', 'Encounter/83d0d564-3bbf-48eb-7445-bd2b81130671', 'male', 'white', 'not hispanic or latino', '660')
+('83d0d564-3bbf-48eb-7445-bd2b81130671', {'id': None, 'code': 'EMER', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'EMER', 'emergency', 'finished', None, None, None, None, None, None, '55680006', 'http://snomed.info/sct', 69, datetime.date(2018, 6, 16), datetime.date(2018, 6, 17), datetime.date(2018, 6, 11), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/16be855b-ece2-8b96-1ef9-a4d93adf3289', 'Encounter/83d0d564-3bbf-48eb-7445-bd2b81130671', 'male', 'white', 'not hispanic or latino', '660')
 ('8ff1dc01-5a28-b2d8-3b42-4b7a7d539970', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, None, None, 55, datetime.date(2018, 7, 15), datetime.date(2018, 7, 15), datetime.date(2018, 7, 9), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/c1bfec36-dc2c-afc8-c767-3d35ed2bf6f0', 'Encounter/8ff1dc01-5a28-b2d8-3b42-4b7a7d539970', 'female', 'white', 'not hispanic or latino', '662')
 ('91f94a9d-69a7-e30a-cd1a-68c52dc01e70', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, None, None, 22, datetime.date(2018, 6, 29), datetime.date(2018, 6, 29), datetime.date(2018, 6, 25), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/a28be3e1-a6bd-7df4-fc81-1140848f8453', 'Encounter/91f94a9d-69a7-e30a-cd1a-68c52dc01e70', 'female', 'white', 'not hispanic or latino', '662')
 ('98d4bd14-d78e-debb-e7dc-2df7786aedf3', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, None, None, 89, datetime.date(2018, 7, 9), datetime.date(2018, 7, 9), datetime.date(2018, 7, 9), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/e455ca3f-fc16-6ffc-297a-adc27e2db183', 'Encounter/98d4bd14-d78e-debb-e7dc-2df7786aedf3', 'male', 'white', 'hispanic or latino', '667')
@@ -45,6 +45,6 @@
 ('e613f29d-7505-6f2e-a1f5-bfbec300752d', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, '88805009', 'http://snomed.info/sct', 84, datetime.date(2018, 6, 29), datetime.date(2018, 6, 29), datetime.date(2018, 6, 25), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/1c498b42-61fd-6341-69c3-053f6e4fe404', 'Encounter/e613f29d-7505-6f2e-a1f5-bfbec300752d', 'female', 'white', 'not hispanic or latino', '667')
 ('e922a884-7039-a171-a65e-78051fe7afe6', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, None, None, 1, datetime.date(2018, 6, 13), datetime.date(2018, 6, 13), datetime.date(2018, 6, 11), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/8022fbbe-aaa4-056c-d0f5-ec074bf0656c', 'Encounter/e922a884-7039-a171-a65e-78051fe7afe6', 'male', 'white', 'not hispanic or latino', '665')
 ('ed151e04-3dd6-8cb7-a3e5-777c8a8667f1', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, '195662009', 'http://snomed.info/sct', 57, datetime.date(2018, 6, 6), datetime.date(2018, 6, 6), datetime.date(2018, 6, 4), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/19158de4-66a2-f70f-e3bf-4396b312c8f1', 'Encounter/ed151e04-3dd6-8cb7-a3e5-777c8a8667f1', 'female', 'white', 'not hispanic or latino', '000')
-('f2752dd7-1bf1-739d-dd8c-40122d0b63bc', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, '161665007', 'http://snomed.info/sct', 77, datetime.date(2018, 6, 1), datetime.date(2018, 6, 1), datetime.date(2018, 5, 28), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/9c8d8539-0b1e-73e2-b64f-83f1ea601fa4', 'Encounter/f2752dd7-1bf1-739d-dd8c-40122d0b63bc', 'male', 'white', 'not hispanic or latino', '674')
+('f2752dd7-1bf1-739d-dd8c-40122d0b63bc', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, '161665007', 'http://snomed.info/sct', 77, datetime.date(2018, 6, 1), datetime.date(2018, 6, 2), datetime.date(2018, 5, 28), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/9c8d8539-0b1e-73e2-b64f-83f1ea601fa4', 'Encounter/f2752dd7-1bf1-739d-dd8c-40122d0b63bc', 'male', 'white', 'not hispanic or latino', '674')
 ('f964be66-3fcd-95c8-0021-71c7d24c91b7', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, '431857002', 'http://snomed.info/sct', 63, datetime.date(2018, 6, 5), datetime.date(2018, 6, 5), datetime.date(2018, 6, 4), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/a5bc08ea-9462-c4f5-1bd2-ff342598ac99', 'Encounter/f964be66-3fcd-95c8-0021-71c7d24c91b7', 'female', 'white', 'not hispanic or latino', '661')
 ('fd0754a4-e96d-cba7-b3c0-77697a09c86e', {'id': None, 'code': 'AMB', 'display': None, 'system': 'http://terminology.hl7.org/CodeSystem/v3-ActCode', 'userSelected': None, 'version': None}, 'AMB', 'ambulatory', 'finished', None, None, None, None, None, None, None, None, 100, datetime.date(2018, 7, 10), datetime.date(2018, 7, 10), datetime.date(2018, 7, 9), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/6385ddd7-2639-6505-3789-0521b8f66c8b', 'Encounter/fd0754a4-e96d-cba7-b3c0-77697a09c86e', 'female', 'white', 'not hispanic or latino', '672')
diff --git a/tests/test_data/core/core__encounter_type.txt b/tests/test_data/core/core__encounter_type.txt
index 308fda57..732a4af7 100644
--- a/tests/test_data/core/core__encounter_type.txt
+++ b/tests/test_data/core/core__encounter_type.txt
@@ -21,7 +21,7 @@
 ('75312bd2-d5ac-c62e-c9df-0004783725c7', 'AMB', 'ambulatory', 'http://snomed.info/sct', '410620009', 'Well child visit (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'finished', 11, datetime.date(2018, 7, 2), datetime.date(2018, 7, 2), datetime.date(2018, 7, 2), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/3ae095ec-8fe0-133b-36d4-8785a6ad8df3', 'Encounter/75312bd2-d5ac-c62e-c9df-0004783725c7', 'female', 'white', 'not hispanic or latino', '662')
 ('75b68644-535d-bdc1-4c31-aa9fe7e1822f', 'EMER', 'emergency', 'http://snomed.info/sct', '50849002', 'Emergency room admission (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', '55680006', 'Drug overdose', 'finished', 25, datetime.date(2018, 6, 30), datetime.date(2018, 6, 30), datetime.date(2018, 6, 25), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/82b8a670-4700-30e8-24a0-b83efa3c5e0a', 'Encounter/75b68644-535d-bdc1-4c31-aa9fe7e1822f', 'female', 'white', 'not hispanic or latino', '672')
 ('79d8f213-7847-646b-8a66-5da208cc1c27', 'AMB', 'ambulatory', 'http://snomed.info/sct', '185347001', 'Encounter for problem (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', '431857002', 'Chronic kidney disease stage 4 (disorder)', 'finished', 77, datetime.date(2018, 7, 14), datetime.date(2018, 7, 14), datetime.date(2018, 7, 9), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/26a3984f-b2a8-e67f-7abc-ff147a0e6e35', 'Encounter/79d8f213-7847-646b-8a66-5da208cc1c27', 'male', 'white', 'not hispanic or latino', '674')
-('83d0d564-3bbf-48eb-7445-bd2b81130671', 'EMER', 'emergency', 'http://snomed.info/sct', '50849002', 'Emergency room admission (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', '55680006', 'Drug overdose', 'finished', 69, datetime.date(2018, 6, 16), datetime.date(2018, 6, 16), datetime.date(2018, 6, 11), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/16be855b-ece2-8b96-1ef9-a4d93adf3289', 'Encounter/83d0d564-3bbf-48eb-7445-bd2b81130671', 'male', 'white', 'not hispanic or latino', '660')
+('83d0d564-3bbf-48eb-7445-bd2b81130671', 'EMER', 'emergency', 'http://snomed.info/sct', '50849002', 'Emergency room admission (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', '55680006', 'Drug overdose', 'finished', 69, datetime.date(2018, 6, 16), datetime.date(2018, 6, 17), datetime.date(2018, 6, 11), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/16be855b-ece2-8b96-1ef9-a4d93adf3289', 'Encounter/83d0d564-3bbf-48eb-7445-bd2b81130671', 'male', 'white', 'not hispanic or latino', '660')
 ('8ff1dc01-5a28-b2d8-3b42-4b7a7d539970', 'AMB', 'ambulatory', 'http://snomed.info/sct', '185349003', 'Encounter for check up (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'finished', 55, datetime.date(2018, 7, 15), datetime.date(2018, 7, 15), datetime.date(2018, 7, 9), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/c1bfec36-dc2c-afc8-c767-3d35ed2bf6f0', 'Encounter/8ff1dc01-5a28-b2d8-3b42-4b7a7d539970', 'female', 'white', 'not hispanic or latino', '662')
 ('91f94a9d-69a7-e30a-cd1a-68c52dc01e70', 'AMB', 'ambulatory', 'http://snomed.info/sct', '308335008', 'Patient encounter procedure', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'finished', 22, datetime.date(2018, 6, 29), datetime.date(2018, 6, 29), datetime.date(2018, 6, 25), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/a28be3e1-a6bd-7df4-fc81-1140848f8453', 'Encounter/91f94a9d-69a7-e30a-cd1a-68c52dc01e70', 'female', 'white', 'not hispanic or latino', '662')
 ('98d4bd14-d78e-debb-e7dc-2df7786aedf3', 'AMB', 'ambulatory', 'http://snomed.info/sct', '185349003', 'Encounter for check up (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'finished', 89, datetime.date(2018, 7, 9), datetime.date(2018, 7, 9), datetime.date(2018, 7, 9), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/e455ca3f-fc16-6ffc-297a-adc27e2db183', 'Encounter/98d4bd14-d78e-debb-e7dc-2df7786aedf3', 'male', 'white', 'hispanic or latino', '667')
@@ -45,6 +45,6 @@
 ('e613f29d-7505-6f2e-a1f5-bfbec300752d', 'AMB', 'ambulatory', 'http://snomed.info/sct', '448337001', 'Telemedicine consultation with patient', 'None', 'None', 'None', 'None', 'None', 'None', '88805009', 'Chronic congestive heart failure (disorder)', 'finished', 84, datetime.date(2018, 6, 29), datetime.date(2018, 6, 29), datetime.date(2018, 6, 25), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/1c498b42-61fd-6341-69c3-053f6e4fe404', 'Encounter/e613f29d-7505-6f2e-a1f5-bfbec300752d', 'female', 'white', 'not hispanic or latino', '667')
 ('e922a884-7039-a171-a65e-78051fe7afe6', 'AMB', 'ambulatory', 'http://snomed.info/sct', '410620009', 'Well child visit (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'finished', 1, datetime.date(2018, 6, 13), datetime.date(2018, 6, 13), datetime.date(2018, 6, 11), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/8022fbbe-aaa4-056c-d0f5-ec074bf0656c', 'Encounter/e922a884-7039-a171-a65e-78051fe7afe6', 'male', 'white', 'not hispanic or latino', '665')
 ('ed151e04-3dd6-8cb7-a3e5-777c8a8667f1', 'AMB', 'ambulatory', 'http://snomed.info/sct', '185345009', 'Encounter for symptom', 'None', 'None', 'None', 'None', 'None', 'None', '195662009', 'Acute viral pharyngitis (disorder)', 'finished', 57, datetime.date(2018, 6, 6), datetime.date(2018, 6, 6), datetime.date(2018, 6, 4), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/19158de4-66a2-f70f-e3bf-4396b312c8f1', 'Encounter/ed151e04-3dd6-8cb7-a3e5-777c8a8667f1', 'female', 'white', 'not hispanic or latino', '000')
-('f2752dd7-1bf1-739d-dd8c-40122d0b63bc', 'AMB', 'ambulatory', 'http://snomed.info/sct', '439740005', 'Postoperative follow-up visit (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', '161665007', 'History of renal transplant (situation)', 'finished', 77, datetime.date(2018, 6, 1), datetime.date(2018, 6, 1), datetime.date(2018, 5, 28), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/9c8d8539-0b1e-73e2-b64f-83f1ea601fa4', 'Encounter/f2752dd7-1bf1-739d-dd8c-40122d0b63bc', 'male', 'white', 'not hispanic or latino', '674')
+('f2752dd7-1bf1-739d-dd8c-40122d0b63bc', 'AMB', 'ambulatory', 'http://snomed.info/sct', '439740005', 'Postoperative follow-up visit (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', '161665007', 'History of renal transplant (situation)', 'finished', 77, datetime.date(2018, 6, 1), datetime.date(2018, 6, 2), datetime.date(2018, 5, 28), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/9c8d8539-0b1e-73e2-b64f-83f1ea601fa4', 'Encounter/f2752dd7-1bf1-739d-dd8c-40122d0b63bc', 'male', 'white', 'not hispanic or latino', '674')
 ('f964be66-3fcd-95c8-0021-71c7d24c91b7', 'AMB', 'ambulatory', 'http://snomed.info/sct', '185347001', 'Encounter for problem (procedure)', 'None', 'None', 'None', 'None', 'None', 'None', '431857002', 'Chronic kidney disease stage 4 (disorder)', 'finished', 63, datetime.date(2018, 6, 5), datetime.date(2018, 6, 5), datetime.date(2018, 6, 4), datetime.date(2018, 6, 1), datetime.date(2018, 1, 1), 'Patient/a5bc08ea-9462-c4f5-1bd2-ff342598ac99', 'Encounter/f964be66-3fcd-95c8-0021-71c7d24c91b7', 'female', 'white', 'not hispanic or latino', '661')
 ('fd0754a4-e96d-cba7-b3c0-77697a09c86e', 'AMB', 'ambulatory', 'http://snomed.info/sct', '702927004', 'Urgent care clinic (environment)', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'finished', 100, datetime.date(2018, 7, 10), datetime.date(2018, 7, 10), datetime.date(2018, 7, 9), datetime.date(2018, 7, 1), datetime.date(2018, 1, 1), 'Patient/6385ddd7-2639-6505-3789-0521b8f66c8b', 'Encounter/fd0754a4-e96d-cba7-b3c0-77697a09c86e', 'female', 'white', 'not hispanic or latino', '672')
diff --git a/tests/test_duckdb.py b/tests/test_duckdb.py
index 517463ac..2db351c7 100644
--- a/tests/test_duckdb.py
+++ b/tests/test_duckdb.py
@@ -1,15 +1,17 @@
 """ tests for duckdb backend support """
 
 import glob
+import json
 import os
 import tempfile
-from datetime import datetime, timedelta, timezone
+from datetime import datetime
 from pathlib import Path
 from unittest import mock
 
 import pytest
 
 from cumulus_library import cli, databases
+from cumulus_library.template_sql import base_templates
 
 
 @mock.patch.dict(
@@ -55,12 +57,12 @@ def test_duckdb_core_build_and_export():
 @pytest.mark.parametrize(
     "timestamp,expected",
     [
-        ("2021", datetime(2021, 1, 1, tzinfo=timezone.utc)),
-        ("2019-10", datetime(2019, 10, 1, tzinfo=timezone.utc)),
-        ("1923-01-23", datetime(1923, 1, 23, tzinfo=timezone.utc)),
+        ("2021", datetime(2021, 1, 1)),
+        ("2019-10", datetime(2019, 10, 1)),
+        ("1923-01-23", datetime(1923, 1, 23)),
         (
             "2023-01-16T07:55:25-05:00",
-            datetime(2023, 1, 16, 7, 55, 25, tzinfo=timezone(timedelta(hours=-5))),
+            datetime(2023, 1, 16, 12, 55, 25),
         ),
     ],
 )
@@ -71,4 +73,57 @@ def test_duckdb_from_iso8601_timestamp(timestamp, expected):
         .execute(f"select from_iso8601_timestamp('{timestamp}')")
         .fetchone()[0]
     )
-    assert parsed, expected
+    assert parsed == expected
+
+
+def test_duckdb_table_schema():
+    """Verify we can detect schemas correctly, even for nested camel case fields"""
+    db = databases.DuckDatabaseBackend(":memory:")
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        os.mkdir(f"{tmpdir}/observation")
+        with open(f"{tmpdir}/observation/test.ndjson", "w", encoding="utf8") as ndjson:
+            json.dump(
+                {
+                    "id": "test",
+                    "component": [{"dataAbsentReason": {"text": "Dunno"}}],
+                    "valueBoolean": False,
+                },
+                ndjson,
+            )
+
+        db.insert_tables(databases.read_ndjson_dir(tmpdir))
+
+        # Look for a mix of camel-cased and lower-cased fields. Both should work.
+        target_schema = {
+            "bodySite": [],
+            "CoMpOnEnT": ["dataabsentreason", "valueQuantity"],
+            "not_a_real_field": [],
+            "valueboolean": [],
+        }
+
+        # Query database for what exists right now as a schema
+        query = base_templates.get_column_datatype_query(
+            # Use a mixed-case table name
+            db.schema_name,
+            "Observation",
+            list(target_schema.keys()),
+        )
+        actual_schema = db.cursor().execute(query).fetchall()
+
+        # Validate that schema against what we were looking for
+        validated_schema = db.parser().validate_table_schema(
+            target_schema, actual_schema
+        )
+        # Note the all mixed-case results.
+        # These are guaranteed to be the same case as the expected/target schema.
+        expected_schema = {
+            "bodySite": True,  # real toplevel fields are guaranteed to be in schema
+            "CoMpOnEnT": {
+                "dataabsentreason": True,
+                "valueQuantity": False,
+            },
+            "not_a_real_field": False,
+            "valueboolean": True,
+        }
+        assert validated_schema == expected_schema
diff --git a/tests/test_templates.py b/tests/test_templates.py
index ab89b3ec..8c5d85bb 100644
--- a/tests/test_templates.py
+++ b/tests/test_templates.py
@@ -168,8 +168,8 @@ def test_get_column_datatype_query():
 
     query = base_templates.get_column_datatype_query(
         schema_name="schema_name",
-        table_name="table_name",
-        column_names=["foo", "bar"],
+        table_name="TABLE_NAME",
+        column_names=["foo", "BAR"],
     )
     assert query == expected