diff --git a/metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/queries_extractor.py b/metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/queries_extractor.py
index 1e2a12c6adb87..a826f09b9a7c8 100644
--- a/metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/queries_extractor.py
+++ b/metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/queries_extractor.py
@@ -247,32 +247,21 @@ def get_workunits_internal(
                         self.report.num_queries_by_project[project.id] += 1
                         queries.append(entry)
         self.report.num_total_queries = len(queries)
+        logger.info(f"Found {self.report.num_total_queries} total queries")
 
         with self.report.audit_log_preprocessing_timer:
             # Preprocessing stage that deduplicates the queries using query hash per usage bucket
-            # Using regular dictionary with
-            #   key: usage bucket
-            #   value: File backed dictionary with query hash as key and observed query as value
-            # This structure is chosen in order to maintain order of execution of queries
-
-            queries_deduped: Dict[int, FileBackedDict[ObservedQuery]]
+            # Note: FileBackedDict is an ordered dictionary, so the order of execution of
+            # queries is inherently maintained
+            queries_deduped: FileBackedDict[Dict[int, ObservedQuery]]
             queries_deduped = self.deduplicate_queries(queries)
-            self.report.num_unique_queries = len(
-                set(
-                    query_hash
-                    for bucket in queries_deduped.values()
-                    for query_hash in bucket
-                )
-            )
+            self.report.num_unique_queries = len(queries_deduped)
+            logger.info(f"Found {self.report.num_unique_queries} unique queries")
 
         with self.report.audit_log_load_timer:
             i = 0
-            for queries_in_bucket in queries_deduped.values():
-                # Ordering is essential for column-level lineage via temporary table
-                for row in queries_in_bucket.sql_query_iterator(
-                    "select value from data order by last_query_timestamp asc",
-                ):
-                    query = queries_in_bucket.deserializer(row["value"])
+            for _, query_instances in queries_deduped.items():
+                for query in query_instances.values():
                     if i > 0 and i % 10000 == 0:
                         logger.info(f"Added {i} query log entries to SQL aggregator")
 
@@ -283,7 +272,7 @@ def get_workunits_internal(
 
     def deduplicate_queries(
         self, queries: FileBackedList[ObservedQuery]
-    ) -> Dict[int, FileBackedDict[ObservedQuery]]:
+    ) -> FileBackedDict[Dict[int, ObservedQuery]]:
 
         # This fingerprint based deduplication is done here to reduce performance hit due to
         # repetitive sql parsing while adding observed query to aggregator that would otherwise
@@ -291,7 +280,7 @@ def deduplicate_queries(
         # With current implementation, it is possible that "Operation"(e.g. INSERT) is reported
         # only once per day, although it may have happened multiple times throughout the day.
 
-        queries_deduped: Dict[int, FileBackedDict[ObservedQuery]] = dict()
+        queries_deduped: FileBackedDict[Dict[int, ObservedQuery]] = FileBackedDict()
 
         for i, query in enumerate(queries):
             if i > 0 and i % 10000 == 0:
@@ -310,20 +299,14 @@ def deduplicate_queries(
                 query.query, self.identifiers.platform, fast=True
             )
 
-            if time_bucket not in queries_deduped:
-                # TODO: Cleanup, etc as required for file backed dicts after use
-                queries_deduped[time_bucket] = FileBackedDict[ObservedQuery](
-                    extra_columns={"last_query_timestamp": lambda e: e.timestamp}
-                )
+            query_instances = queries_deduped.setdefault(query.query_hash, {})
 
-            observed_query = queries_deduped[time_bucket].get(query.query_hash)
+            observed_query = query_instances.setdefault(time_bucket, query)
 
             # If the query already exists for this time bucket, update its attributes
-            if observed_query is not None:
+            if observed_query is not query:
                 observed_query.usage_multiplier += 1
                 observed_query.timestamp = query.timestamp
-            else:
-                queries_deduped[time_bucket][query.query_hash] = query
 
         return queries_deduped