diff --git a/src/dune_to_local/__init__.py b/src/dune_to_local/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mappings.py b/src/dune_to_local/mappings.py
similarity index 100%
rename from src/mappings.py
rename to src/dune_to_local/mappings.py
diff --git a/src/dune_to_local/postgres.py b/src/dune_to_local/postgres.py
new file mode 100644
index 0000000..9f7fbd6
--- /dev/null
+++ b/src/dune_to_local/postgres.py
@@ -0,0 +1,76 @@
+from typing import Any
+
+import pandas as pd
+import sqlalchemy
+from dune_client.client import DuneClient
+from dune_client.models import ExecutionResult
+from dune_client.query import QueryBase
+from pandas import DataFrame
+from sqlalchemy import create_engine
+
+from src.config import DuneToLocalJob, Env
+from src.dune_to_local.mappings import DUNE_TO_PG
+from src.logger import log
+
+DataTypes = dict[str, Any]
+
+
+def reformat_varbinary_columns(
+    df: DataFrame, varbinary_columns: list[str]
+) -> DataFrame:
+    for col in varbinary_columns:
+        df[col] = df[col].apply(lambda x: bytes.fromhex(x[2:]) if pd.notnull(x) else x)
+    return df
+
+
+def dune_result_to_df(result: ExecutionResult) -> tuple[DataFrame, dict[str, type]]:
+    metadata = result.metadata
+    dtypes, varbinary_columns = {}, []
+    for name, d_type in zip(metadata.column_names, metadata.column_types):
+        dtypes[name] = DUNE_TO_PG[d_type]
+        if d_type == "varbinary":
+            varbinary_columns.append(name)
+
+    df = pd.DataFrame(result.rows)
+    # escape bytes
+    df = reformat_varbinary_columns(df, varbinary_columns)
+    return df, dtypes
+
+
+def fetch_dune_data(dune_key: str, job: DuneToLocalJob) -> tuple[DataFrame, DataTypes]:
+    dune = DuneClient(dune_key, performance=job.query_engine)
+    response = dune.run_query(
+        query=QueryBase(
+            query_id=job.query_id,
+            params=[
+                # TODO: https://github.com/bh2smith/dune-sync/issues/30
+            ],
+        ),
+        ping_frequency=job.poll_frequency,
+    )
+    if response.result is None:
+        raise ValueError("Query execution failed!")
+    return dune_result_to_df(response.result)
+
+
+def save_to_postgres(
+    engine: sqlalchemy.engine.Engine, table_name: str, df: DataFrame, dtypes: DataTypes
+) -> None:
+    if df.empty:
+        log.warning("DataFrame is empty. Skipping save to PostgreSQL.")
+        return
+    with engine.connect() as connection:
+        df.to_sql(
+            table_name, connection, if_exists="replace", index=False, dtype=dtypes
+        )
+    log.info("Data saved to %s successfully!", table_name)
+
+
+def dune_to_postgres(env: Env, job: DuneToLocalJob) -> None:
+    df, types = fetch_dune_data(env.dune_api_key, job)
+    if not df.empty:
+        # Skip engine creation if unnecessary.
+        engine = create_engine(env.db_url)
+        save_to_postgres(engine, job.table_name, df, types)
+    else:
+        log.warning("No Query results found! Skipping write")
diff --git a/src/main.py b/src/main.py
index 63c39eb..57156e6 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,56 +1,5 @@
-from typing import Any
-
-import pandas as pd
-import sqlalchemy
-from dune_client.client import DuneClient
-from dune_client.models import ExecutionResult
-from dune_client.query import QueryBase
-from pandas import DataFrame
-from sqlalchemy import create_engine
-
 from src.config import Env, RuntimeConfig
-from src.logger import log
-from src.mappings import DUNE_TO_PG
-
-DataTypes = dict[str, Any]
-
-
-def reformat_varbinary_columns(
-    df: DataFrame, varbinary_columns: list[str]
-) -> DataFrame:
-    for col in varbinary_columns:
-        df[col] = df[col].apply(lambda x: bytes.fromhex(x[2:]) if pd.notnull(x) else x)
-    return df
-
-
-def dune_result_to_df(result: ExecutionResult) -> tuple[DataFrame, dict[str, type]]:
-    metadata = result.metadata
-    dtypes, varbinary_columns = {}, []
-    for name, d_type in zip(metadata.column_names, metadata.column_types):
-        dtypes[name] = DUNE_TO_PG[d_type]
-        if d_type == "varbinary":
-            varbinary_columns.append(name)
-
-    df = pd.DataFrame(result.rows)
-    # escape bytes
-    df = reformat_varbinary_columns(df, varbinary_columns)
-    return df, dtypes
-
-
-def fetch_dune_data(
-    dune: DuneClient, query: QueryBase, ping_frequency: int
-) -> tuple[DataFrame, DataTypes]:
-    result = dune.run_query(query, ping_frequency).result
-    if result is None:
-        raise ValueError("Query execution failed!")
-    return dune_result_to_df(result)
-
-
-def save_to_postgres(
-    engine: sqlalchemy.engine.Engine, table_name: str, df: DataFrame, dtypes: DataTypes
-) -> None:
-    df.to_sql(table_name, engine, if_exists="replace", index=False, dtype=dtypes)
-    log.info("Data saved to %s successfully!", table_name)
+from src.dune_to_local.postgres import dune_to_postgres
 
 
 def main() -> None:
@@ -58,14 +7,7 @@ def main() -> None:
     config = RuntimeConfig.load_from_toml("config.toml")
     # TODO: Async job execution https://github.com/bh2smith/dune-sync/issues/20
     for job in config.jobs:
-        df, types = fetch_dune_data(
-            dune=DuneClient(env.dune_api_key, performance=job.query_engine),
-            query=QueryBase(job.query_id),
-            ping_frequency=job.poll_frequency,
-        )
-        if df is not None:
-            engine = create_engine(env.db_url)
-            save_to_postgres(engine, job.table_name, df, types)
+        dune_to_postgres(env, job)
 
 
 if __name__ == "__main__":
diff --git a/tests/e2e_test.py b/tests/e2e_test.py
index 2dffa59..a7b645b 100644
--- a/tests/e2e_test.py
+++ b/tests/e2e_test.py
@@ -9,7 +9,7 @@
 from sqlalchemy import create_engine
 from sqlalchemy.dialects.postgresql import BYTEA
 
-from src.main import save_to_postgres, dune_result_to_df
+from src.dune_to_local.postgres import save_to_postgres, dune_result_to_df
 from tests.db_util import query_pg
 
 DB_URL = getenv("DB_URL", "postgresql://postgres:postgres@localhost:5432/postgres")