Ensure that you have a working environment with Apache Iceberg installed. You can use a local setup, such as Apache Spark or Apache Flink, that supports Iceberg. Check the Iceberg documentation to set up the proper environment and dependencies.

Make sure your CSV file is clean and properly formatted. This includes ensuring that the file has headers for each column and consistent data formatting throughout. Verify that there are no missing values or extra delimiters that could cause parsing errors.

Define the schema for your Iceberg table. You can do this by writing a schema definition that matches the structure of your CSV data. Ensure that the data types in your schema correspond to the data types in the CSV file.

Use Apache Spark or another supported execution engine to read your CSV file into a DataFrame. This can typically be done using the built-in CSV reader capabilities. For Spark, you might use:
```python
csv_df = spark.read.format("csv").option("header", "true").load("path/to/your/file.csv")
```

Adjust the DataFrame to ensure it matches the Iceberg table schema. This may involve casting columns to the correct data types or renaming columns to fit the schema definitions. Use DataFrame operations to perform these transformations.

Write the transformed DataFrame to your Iceberg table. This step involves specifying the Iceberg table as the sink for your DataFrame. Using Spark, this can typically be done with:
```python
csv_df.write.format("iceberg").mode("append").save("iceberg_catalog.db.table_name")
```

Finally, verify that the data has been moved correctly by querying the Iceberg table. Use your execution engine to run simple queries that check the data integrity and ensure that all records have been successfully imported from the CSV file.