Start by exporting the data from your Teradata database. You can use the `bteq` command-line utility to export data into a CSV file. Execute a SQL query in `bteq` to select the data you need and use the `.EXPORT` command to write the output to a CSV file.

```sql
.LOGON /,;
.EXPORT REPORT FILE=;
SELECT FROM .;
.EXPORT RESET;
.LOGOFF;
.EXIT;
```

Once you have your CSV file, ensure that it is properly formatted for BigQuery. Check for issues like correct delimiters, escape characters, and consistent data types. Remove any special characters or null values that might cause import errors.

Use the `gsutil` command-line tool to upload your CSV file to a Google Cloud Storage bucket. This step is essential as BigQuery can easily load data from GCS.

```bash
gsutil cp gs:////
```

Before loading data into BigQuery, create a dataset where your table will reside. You can do this through the Google Cloud Console or using the `bq` command-line tool.

```bash
bq mk --dataset :
```

Define the schema of the table in BigQuery to match the CSV data structure. You need to specify the field names and data types. This can be done using a JSON file or directly in the `bq` command.

```json
[
{"name": "field1", "type": "STRING"},
{"name": "field2", "type": "INTEGER"},
...
]
```

Use the `bq` command-line tool to load the data from GCS into BigQuery. Reference the schema you defined earlier. Ensure that the data types in BigQuery match those in the CSV file to avoid any errors.

```bash
bq load --source_format=CSV --autodetect :. gs:////
```

After loading the data, verify that it has been imported correctly. You can use the BigQuery console or `bq` command-line tool to query the data and check for consistency.

```bash
bq query --nouse_legacy_sql 'SELECT FROM `..` LIMIT 10'
```

By following these steps, you can efficiently transfer data from Teradata to BigQuery without relying on third-party connectors or integrations.