diff --git a/dags/simple_snowflake.py b/dags/simple_snowflake.py
new file mode 100644
index 00000000..07c0d0e0
--- /dev/null
+++ b/dags/simple_snowflake.py
@@ -0,0 +1,106 @@
+"""
+### Simple EL Pipeline with Data Quality Checks Using Snowflake
+
+Runs a data quality check, in SQL, on the forest fires dataset
+
+Note that this DAG will clean up after itself and delete all data it uploads.
+
+Ensure a Snowflake Warehouse, Database, Schema, and Role exist for the Snowflake
+connection provided to the Operator. The names of these data should replace the
+dummy values at the top of the file.
+
+A Snowflake Connection is also needed, named `snowflake_default`.
+
+What makes this a simple data quality case is:
+1. Absolute ground truth: the local CSV file is considered perfect and immutable.
+2. No transformations or business logic.
+3. Exact values of data to quality check are known.
+"""
+
+from airflow import DAG
+from airflow.models.baseoperator import chain
+from airflow.operators.empty import EmptyOperator
+from airflow.providers.common.sql.operators.sql import SQLColumnCheckOperator, SQLTableCheckOperator
+from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator
+from airflow.utils.dates import datetime
+from airflow.utils.task_group import TaskGroup
+
+
+SNOWFLAKE_FORESTFIRE_TABLE = "forestfires"
+SNOWFLAKE_CONN_ID = "snowflake_default"
+
+
+with DAG(
+    "simple_snowflake",
+    description="Example DAG showcasing loading and data quality checking with Snowflake.",
+    doc_md=__doc__,
+    start_date=datetime(2021, 1, 1),
+    schedule_interval=None,
+    template_searchpath="/usr/local/airflow/include/sql/snowflake_examples/",
+    catchup=False
+) as dag:
+
+
+    """
+    #### Snowflake table creation
+    Create the table to store sample forest fire data.
+    """
+    create_table = SnowflakeOperator(
+        task_id="create_table",
+        sql="{% include 'create_forestfire_table.sql' %}",
+        params={"table_name": SNOWFLAKE_FORESTFIRE_TABLE}
+    )
+
+    """
+    #### Insert data
+    Insert data into the Snowflake table using an existing SQL query (stored in
+    the include/sql/snowflake_examples/ directory).
+    """
+    load_data = SnowflakeOperator(
+        task_id="insert_query",
+        sql="{% include 'load_snowflake_forestfire_data.sql' %}",
+        params={"table_name": SNOWFLAKE_FORESTFIRE_TABLE}
+    )
+
+    with TaskGroup(group_id="quality_checks", default_args={"conn_id": SNOWFLAKE_CONN_ID}) as quality_check_group:
+        """
+        #### Column-level data quality check
+        Run data quality checks on columns of the audit table
+        """
+        column_checks = SQLColumnCheckOperator(
+            task_id="column_checks",
+            table=SNOWFLAKE_FORESTFIRE_TABLE,
+            column_mapping={"ID": {"null_check": {"equal_to": 0}}}
+        )
+
+        """
+        #### Table-level data quality check
+        Run data quality checks on the audit table
+        """
+        table_checks = SQLTableCheckOperator(
+            task_id="table_checks",
+            table=SNOWFLAKE_FORESTFIRE_TABLE,
+            checks={"row_count_check": {"check_statement": "COUNT(*) = 9"}}
+        )
+
+    """
+    #### Delete table
+    Clean up the table created for the example.
+    """
+    delete_table = SnowflakeOperator(
+        task_id="delete_table",
+        sql="{% include 'delete_snowflake_table.sql' %}",
+        params={"table_name": SNOWFLAKE_FORESTFIRE_TABLE}
+    )
+
+    begin = EmptyOperator(task_id="begin")
+    end = EmptyOperator(task_id="end")
+
+    chain(
+        begin,
+        create_table,
+        load_data,
+        quality_check_group,
+        delete_table,
+        end
+    )